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Moll L, Giralt N, Planas M, Feliu L, Montesinos E, Bonaterra A, Badosa E. Prunus dulcis response to novel defense elicitor peptides and control of Xylella fastidiosa infections. PLANT CELL REPORTS 2024; 43:190. [PMID: 38976088 PMCID: PMC11231009 DOI: 10.1007/s00299-024-03276-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Accepted: 06/27/2024] [Indexed: 07/09/2024]
Abstract
KEY MESSAGE New defense elicitor peptides have been identified which control Xylella fastidiosa infections in almond. Xylella fastidiosa is a plant pathogenic bacterium that has been introduced in the European Union (EU), threatening the agricultural economy of relevant Mediterranean crops such as almond (Prunus dulcis). Plant defense elicitor peptides would be promising to manage diseases such as almond leaf scorch, but their effect on the host has not been fully studied. In this work, the response of almond plants to the defense elicitor peptide flg22-NH2 was studied in depth using RNA-seq, confirming the activation of the salicylic acid and abscisic acid pathways. Marker genes related to the response triggered by flg22-NH2 were used to study the effect of the application strategy of the peptide on almond plants and to depict its time course. The application of flg22-NH2 by endotherapy triggered the highest number of upregulated genes, especially at 6 h after the treatment. A library of peptides that includes BP100-flg15, HpaG23, FV7, RIJK2, PIP-1, Pep13, BP16-Pep13, flg15-BP100 and BP16 triggered a stronger defense response in almond plants than flg22-NH2. The best candidate, FV7, when applied by endotherapy on almond plants inoculated with X. fastidiosa, significantly reduced levels of the pathogen and decreased disease symptoms. Therefore, these novel plant defense elicitors are suitable candidates to manage diseases caused by X. fastidiosa, in particular almond leaf scorch.
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Affiliation(s)
- Luis Moll
- Laboratory of Plant Pathology, Institute of Food and Agricultural Technology-CIDSAV, University of Girona, Campus Montilivi, 17003, Girona, Spain
| | - Núria Giralt
- Laboratory of Plant Pathology, Institute of Food and Agricultural Technology-CIDSAV, University of Girona, Campus Montilivi, 17003, Girona, Spain
| | - Marta Planas
- LIPPSO, Department of Chemistry, University of Girona, Campus Montilivi, 17003, Girona, Spain
| | - Lidia Feliu
- LIPPSO, Department of Chemistry, University of Girona, Campus Montilivi, 17003, Girona, Spain
| | - Emilio Montesinos
- Laboratory of Plant Pathology, Institute of Food and Agricultural Technology-CIDSAV, University of Girona, Campus Montilivi, 17003, Girona, Spain
| | - Anna Bonaterra
- Laboratory of Plant Pathology, Institute of Food and Agricultural Technology-CIDSAV, University of Girona, Campus Montilivi, 17003, Girona, Spain
| | - Esther Badosa
- Laboratory of Plant Pathology, Institute of Food and Agricultural Technology-CIDSAV, University of Girona, Campus Montilivi, 17003, Girona, Spain.
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Feng G, Gu Y, Wang C, Zhou Y, Huang S, Luo B. Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective. PLANTS (BASEL, SWITZERLAND) 2024; 13:1722. [PMID: 38999562 DOI: 10.3390/plants13131722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 07/14/2024]
Abstract
Fusarium head blight (FHB) is a major threat to global wheat production. Recent reviews of wheat FHB focused on pathology or comprehensive prevention and lacked a summary of advanced detection techniques. Unlike traditional detection and management methods, wheat FHB detection based on various imaging technologies has the obvious advantages of a high degree of automation and efficiency. With the rapid development of computer vision and deep learning technology, the number of related research has grown explosively in recent years. This review begins with an overview of wheat FHB epidemic mechanisms and changes in the characteristics of infected wheat. On this basis, the imaging scales are divided into microscopic, medium, submacroscopic, and macroscopic scales. Then, we outline the recent relevant articles, algorithms, and methodologies about wheat FHB from disease detection to qualitative analysis and summarize the potential difficulties in the practicalization of the corresponding technology. This paper could provide researchers with more targeted technical support and breakthrough directions. Additionally, this paper provides an overview of the ideal application mode of the FHB detection technologies based on multi-scale imaging and then examines the development trend of the all-scale detection system, which paved the way for the fusion of non-destructive detection technologies of wheat FHB based on multi-scale imaging.
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Affiliation(s)
- Guoqing Feng
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- College of Agricultural Engineering, Jiangsu University, Zhenjiang 212000, China
| | - Ying Gu
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Cheng Wang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- College of Agricultural Engineering, Jiangsu University, Zhenjiang 212000, China
| | - Yanan Zhou
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Shuo Huang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Bin Luo
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- College of Agricultural Engineering, Jiangsu University, Zhenjiang 212000, China
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3
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Bai Y, Jin X. Hyperspectral approaches for rapid and spatial plant disease monitoring. TRENDS IN PLANT SCIENCE 2024; 29:711-712. [PMID: 38584079 DOI: 10.1016/j.tplants.2024.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/14/2024] [Accepted: 03/14/2024] [Indexed: 04/09/2024]
Affiliation(s)
- Yali Bai
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China; State Key Laboratory of Crop Gene Resources and Breeding, Beijing 100081, China; Information Technology Group, Wageningen University and Research, Wageningen, 6706, KN, The Netherlands
| | - Xiuliang Jin
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, China; State Key Laboratory of Crop Gene Resources and Breeding, Beijing 100081, China.
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4
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Serio F, Imbriani G, Girelli CR, Miglietta PP, Scortichini M, Fanizzi FP. A Decade after the Outbreak of Xylella fastidiosa subsp. pauca in Apulia (Southern Italy): Methodical Literature Analysis of Research Strategies. PLANTS (BASEL, SWITZERLAND) 2024; 13:1433. [PMID: 38891241 PMCID: PMC11175074 DOI: 10.3390/plants13111433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/17/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024]
Abstract
In 2013, an outbreak of Xylella fastidiosa (Xf) was identified for the first time in Europe, in the extreme south of Italy (Apulia, Salento territory). The locally identified subspecies pauca turned out to be lethal for olive trees, starting an unprecedented phytosanitary emergency for one of the most iconic cultivations of the Mediterranean area. Xf pauca (Xfp) is responsible for a severe disease, the olive quick decline syndrome (OQDS), spreading epidemically and with dramatic impact on the agriculture, the landscape, the tourism and the cultural heritage of this region. The bacterium, transmitted by insects that feed on xylem sap, causes rapid wilting in olive trees due to biofilm formation, which obstructs the plant xylematic vessels. The aim of this review is to perform a thorough analysis that offers a general overview of the published work, from 2013 to December 2023, related to the Xfp outbreak in Apulia. This latter hereto has killed millions of olive trees and left a ghostly landscape with more than 8000 square kilometers of infected territory, that is 40% of the region. The majority of the research efforts made to date to combat Xfp in olive plants are listed in the present review, starting with the early attempts to identify the bacterium, the investigations to pinpoint and possibly control the vector, the assessment of specific diagnostic techniques and the pioneered therapeutic approaches. Interestingly, according to the general set criteria for the preliminary examination of the accessible scientific literature related to the Xfp outbreak on Apulian olive trees, fewer than 300 papers can be found over the last decade. Most of them essentially emphasize the importance of developing diagnostic tools that can identify the disease early, even when infected plants are still asymptomatic, in order to reduce the risk of infection for the surrounding plants. On the other hand, in the published work, the diagnostic focus (57%) overwhelmingly encompasses all other possible investigation goals such as vectors, impacts and possible treatments. Notably, between 2013 and 2023, only 6.3% of the literature reports addressing the topic of Xfp in Apulia were concerned with the application of specific treatments against the bacterium. Among them, those reporting field trials on infected plants, including simple pruning indications, were further limited (6%).
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Affiliation(s)
- Francesca Serio
- Department of Biological and Environmental Sciences and Technology, University of Salento, 73100 Lecce, Italy; (F.S.); (G.I.); (C.R.G.); (P.P.M.)
| | - Giovanni Imbriani
- Department of Biological and Environmental Sciences and Technology, University of Salento, 73100 Lecce, Italy; (F.S.); (G.I.); (C.R.G.); (P.P.M.)
| | - Chiara Roberta Girelli
- Department of Biological and Environmental Sciences and Technology, University of Salento, 73100 Lecce, Italy; (F.S.); (G.I.); (C.R.G.); (P.P.M.)
| | - Pier Paolo Miglietta
- Department of Biological and Environmental Sciences and Technology, University of Salento, 73100 Lecce, Italy; (F.S.); (G.I.); (C.R.G.); (P.P.M.)
| | - Marco Scortichini
- Council for Agricultural Research and Economics (CREA)-Research Centre for Olive, Fruit and Citrus Crops, Via di Fioranello, 52, 00134 Roma, Italy;
| | - Francesco Paolo Fanizzi
- Department of Biological and Environmental Sciences and Technology, University of Salento, 73100 Lecce, Italy; (F.S.); (G.I.); (C.R.G.); (P.P.M.)
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De La Fuente L, Navas-Cortés JA, Landa BB. Ten Challenges to Understanding and Managing the Insect-Transmitted, Xylem-Limited Bacterial Pathogen Xylella fastidiosa. PHYTOPATHOLOGY 2024; 114:869-884. [PMID: 38557216 DOI: 10.1094/phyto-12-23-0476-kc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
An unprecedented plant health emergency in olives has been registered over the last decade in Italy, arguably more severe than what occurred repeatedly in grapes in the United States in the last 140 years. These emergencies are epidemics caused by a stealthy pathogen, the xylem-limited, insect-transmitted bacterium Xylella fastidiosa. Although these epidemics spurred research that answered many questions about the biology and management of this pathogen, many gaps in knowledge remain. For this review, we set out to represent both the U.S. and European perspectives on the most pressing challenges that need to be addressed. These are presented in 10 sections that we hope will stimulate discussion and interdisciplinary research. We reviewed intrinsic problems that arise from the fastidious growth of X. fastidiosa, the lack of specificity for insect transmission, and the economic and social importance of perennial mature woody plant hosts. Epidemiological models and predictions of pathogen establishment and disease expansion, vital for preparedness, are based on very limited data. Most of the current knowledge has been gathered from a few pathosystems, whereas several hundred remain to be studied, probably including those that will become the center of the next epidemic. Unfortunately, aspects of a particular pathosystem are not always transferable to others. We recommend diversification of research topics of both fundamental and applied nature addressing multiple pathosystems. Increasing preparedness through knowledge acquisition is the best strategy to anticipate and manage diseases caused by this pathogen, described as "the most dangerous plant bacterium known worldwide."
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Affiliation(s)
- Leonardo De La Fuente
- Department of Entomology and Plant Pathology, Auburn University, Auburn, AL 36849, U.S.A
| | - Juan A Navas-Cortés
- Department of Crop Protection. Institute for Sustainable Agriculture (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain
| | - Blanca B Landa
- Department of Crop Protection. Institute for Sustainable Agriculture (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain
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Johnson KA, Brannen PM, Chen C, Bock CH. Visual Assessment of Phony Peach Disease: Evaluating Rater Accuracy and Reliability. PLANT DISEASE 2024; 108:930-940. [PMID: 37822103 DOI: 10.1094/pdis-11-22-2669-re] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
Phony peach disease (PPD), found predominantly in central and southern Georgia, is a re-emerging disease caused by Xylella fastidiosa (Xf) subsp. multiplex. Accurate detection and rapid removal of symptomatic trees are crucial to effective disease management. Currently, peach producers rely solely on visual identification of symptoms to confirm PPD, which can be ambiguous if early in development. We compared visual assessment to quantitative PCR (qPCR) for detecting Xf in 'Julyprince' in 2019 and 2020 (JP2019 and JP2020) and in 'Scarletprince' in 2020 (SP2020). With no prior knowledge of qPCR results, all trees in each orchard were assessed by a cohort of five experienced and five inexperienced raters in the morning and afternoon. Visual identification accuracy of PPD was variable, but experienced raters were more accurate when identifying PPD trees. In JP2019, the mean rater accuracy for experienced and inexperienced raters was 0.882 and 0.805, respectively. For JP2020, the mean rater accuracy for experienced and inexperienced raters was 0.914 and 0.816, respectively. For SP2020, the mean rater accuracy for experienced and inexperienced raters was 0.898 and 0.807, respectively. All raters had false positive (FP) and false negative (FN) observations, but experienced raters had significantly lower FN rates compared with the inexperienced group. Almost all raters overestimated the incidence of PPD in the orchards. Reliability of visual assessments was demonstrated as moderate to good, regardless of experience. Further research is needed to develop accurate and reliable methods of detection to aid management of PPD as both FPs and FNs are costly to peach production.
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Affiliation(s)
- Kendall A Johnson
- Department of Plant Pathology, University of Georgia, Athens, GA 30602
| | - Phillip M Brannen
- Department of Plant Pathology, University of Georgia, Athens, GA 30602
| | - Chunxian Chen
- Southeastern Fruit and Tree Nut Research Station, USDA-ARS, Byron, GA 31008
| | - Clive H Bock
- Southeastern Fruit and Tree Nut Research Station, USDA-ARS, Byron, GA 31008
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7
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Loladze A, Rodrigues FA, Petroli CD, Muñoz-Zavala C, Naranjo S, San Vicente F, Gerard B, Montesinos-Lopez OA, Crossa J, Martini JW. Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize. FIELD CROPS RESEARCH 2024; 308:109281. [PMID: 38495466 PMCID: PMC10933745 DOI: 10.1016/j.fcr.2024.109281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 11/24/2023] [Accepted: 01/28/2024] [Indexed: 03/19/2024]
Abstract
Breeding for disease resistance is a central component of strategies implemented to mitigate biotic stress impacts on crop yield. Conventionally, genotypes of a plant population are evaluated through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by specifically trained staff, which limits manageable volumes and repeatability of evaluation trials. Remote sensing (RS) tools have the potential to streamline phenotyping processes and to deliver more standardized results at higher through-put. Here, we use a two-year evaluation trial of three newly developed biparental populations of maize doubled haploid lines (DH) to compare the results of genomic analyses of resistance to common rust (CR) when phenotyping is either based on conventional VS or on RS-derived (vegetation) indices. As a general observation, for each population × year combination, the broad sense heritability of VS was greater than or very close to the maximum heritability across all RS indices. Moreover, results of linkage mapping as well as of genomic prediction (GP), suggest that VS data was of a higher quality, indicated by higher - log p values in the linkage studies and higher predictive abilities for genomic prediction. Nevertheless, despite the qualitative differences between the phenotyping methods, each successfully identified the same genomic region on chromosome 10 as being associated with disease resistance. This region is likely related to the known CR resistance locus Rp1. Our results indicate that RS technology can be used to streamline genetic evaluation processes for foliar disease resistance in maize. In particular, RS can potentially reduce costs of phenotypic evaluations and increase trialing capacities.
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Affiliation(s)
| | | | - Cesar D. Petroli
- International Maize and Wheat Improvement Center – CIMMYT, Mexico
| | | | - Sergio Naranjo
- International Maize and Wheat Improvement Center – CIMMYT, Mexico
| | | | - Bruno Gerard
- International Maize and Wheat Improvement Center – CIMMYT, Mexico
- College of Agriculture and Environmental Sciences (CAES), University Mohammed VI Polytechnic (UM6P), Ben Guerir, Morocco
| | | | - Jose Crossa
- International Maize and Wheat Improvement Center – CIMMYT, Mexico
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Sapes G, Schroeder L, Scott A, Clark I, Juzwik J, Montgomery RA, Guzmán Q JA, Cavender-Bares J. Mechanistic links between physiology and spectral reflectance enable previsual detection of oak wilt and drought stress. Proc Natl Acad Sci U S A 2024; 121:e2316164121. [PMID: 38315867 PMCID: PMC10873599 DOI: 10.1073/pnas.2316164121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 12/11/2023] [Indexed: 02/07/2024] Open
Abstract
Tree mortality due to global change-including range expansion of invasive pests and pathogens-is a paramount threat to forest ecosystems. Oak forests are among the most prevalent and valuable ecosystems both ecologically and economically in the United States. There is increasing interest in monitoring oak decline and death due to both drought and the oak wilt pathogen (Bretziella fagacearum). We combined anatomical and ecophysiological measurements with spectroscopy at leaf, canopy, and airborne levels to enable differentiation of oak wilt and drought, and detection prior to visible symptom appearance. We performed an outdoor potted experiment with Quercus rubra saplings subjected to drought stress and/or artificially inoculated with the pathogen. Models developed from spectral reflectance accurately predicted ecophysiological indicators of oak wilt and drought decline in both potted and field experiments with naturally grown saplings. Both oak wilt and drought resulted in blocked water transport through xylem conduits. However, oak wilt impaired conduits in localized regions of the xylem due to formation of tyloses instead of emboli. The localized tylose formation resulted in more variable canopy photosynthesis and water content in diseased trees than drought-stressed ones. Reflectance signatures of plant photosynthesis, water content, and cellular damage detected oak wilt and drought 12 d before visual symptoms appeared. Our results show that leaf spectral reflectance models predict ecophysiological processes relevant to detection and differentiation of disease and drought. Coupling spectral models that detect physiological change with spatial information enhances capacity to differentiate plant stress types such as oak wilt and drought.
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Affiliation(s)
- Gerard Sapes
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN55108
- Agronomy Department, University of Florida, Gainesville, FL32611
| | - Lucy Schroeder
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN55108
| | - Allison Scott
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN55108
| | - Isaiah Clark
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN55108
| | - Jennifer Juzwik
- Northern Research Station, United States Department of Agriculture Forest Service, St. Paul, MN55108
| | | | - J. Antonio Guzmán Q
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN55108
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Fahad S, Li S, Zhai Y, Zhao C, Pikramenou Z, Wang M. Luminescence-Based Infrared Thermal Sensors: Comprehensive Insights. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2304237. [PMID: 37679096 DOI: 10.1002/smll.202304237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 07/08/2023] [Indexed: 09/09/2023]
Abstract
Recent chronological breakthroughs in materials innovation, their fabrication, and structural designs for disparate applications have paved transformational ways to subversively digitalize infrared (IR) thermal imaging sensors from traditional to smart. The noninvasive IR thermal imaging sensors are at the cutting edge of developments, exploiting the abilities of nanomaterials to acquire arbitrary, targeted, and tunable responses suitable for integration with host materials and devices, intimately disintegrate variegated signals from the target onto depiction without any discomfort, eliminating motional artifacts and collects precise physiological and physiochemical information in natural contexts. Highlighting several typical examples from recent literature, this review article summarizes an accessible, critical, and authoritative summary of an emerging class of advancement in the modalities of nano and micro-scale materials and devices, their fabrication designs and applications in infrared thermal sensors. Introduction is begun covering the importance of IR sensors, followed by a survey on sensing capabilities of various nano and micro structural materials, their design architects, and then culminating an overview of their diverse application swaths. The review concludes with a stimulating frontier debate on the opportunities, difficulties, and future approaches in the vibrant sector of infrared thermal imaging sensors.
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Affiliation(s)
- Shah Fahad
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, P. R. China
- Engineering Research Center of Integrated Circuits for Next-Generation Communications, Ministry of Education, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Song Li
- Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, 518055, P. R. China
| | - Yufei Zhai
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, P. R. China
| | - Cong Zhao
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, P. R. China
- Engineering Research Center of Integrated Circuits for Next-Generation Communications, Ministry of Education, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Zoe Pikramenou
- School of Chemistry, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Min Wang
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, P. R. China
- Engineering Research Center of Integrated Circuits for Next-Generation Communications, Ministry of Education, Southern University of Science and Technology, Shenzhen, 518055, China
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Attaluri S, Dharavath R. Novel plant disease detection techniques-a brief review. Mol Biol Rep 2023; 50:9677-9690. [PMID: 37823933 DOI: 10.1007/s11033-023-08838-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/25/2023] [Indexed: 10/13/2023]
Abstract
Plant pathogens cause severe losses to agricultural yield worldwide. Tracking plant health and early disease detection is important to reduce the disease spread and thus economic loss. Though visual scouting has been practiced from former times, detection of asymptomatic disease conditions is difficult. So, DNA-based and serological methods gained importance in plant disease detection. The progress in advanced technologies challenges the development of rapid, non-invasive, and on-field detection techniques such as spectroscopy. This review highlights various direct and indirect ways of detecting plant diseases like Enzyme-linked immunosorbent assay, Lateral flow assays, Polymerase chain reaction, spectroscopic techniques and biosensors. Although these techniques are sensitive and pathogen-specific, they are more laborious and time-intensive. As a consequence, a lot of interest is gained in in-field adaptable point-of-care devices with artificial intelligence-assisted pathogen detection at an early stage. More recently computer-aided techniques like neural networks are gaining significance in plant disease detection by image processing. In addition, a concise report on the latest progress achieved in plant disease detection techniques is provided.
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Galvan FER, Pavlick R, Trolley G, Aggarwal S, Sousa D, Starr C, Forrestel E, Bolton S, Alsina MDM, Dokoozlian N, Gold KM. Scalable Early Detection of Grapevine Viral Infection with Airborne Imaging Spectroscopy. PHYTOPATHOLOGY 2023; 113:1439-1446. [PMID: 37097472 DOI: 10.1094/phyto-01-23-0030-r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The U.S. wine and grape industry loses $3B annually due to viral diseases including grapevine leafroll-associated virus complex 3 (GLRaV-3). Current detection methods are labor-intensive and expensive. GLRaV-3 has a latent period in which the vines are infected but do not display visible symptoms, making it an ideal model to evaluate the scalability of imaging spectroscopy-based disease detection. The NASA Airborne Visible and Infrared Imaging Spectrometer Next Generation was deployed to detect GLRaV-3 in Cabernet Sauvignon grapevines in Lodi, CA in September 2020. Foliage was removed from the vines as part of mechanical harvest soon after image acquisition. In September of both 2020 and 2021, industry collaborators scouted 317 hectares on a vine-by-vine basis for visible viral symptoms and collected a subset for molecular confirmation testing. Symptomatic grapevines identified in 2021 were assumed to have been latently infected at the time of image acquisition. Random forest models were trained on a spectroscopic signal of noninfected and GLRaV-3 infected grapevines balanced with synthetic minority oversampling of noninfected and GLRaV-3 infected grapevines. The models were able to differentiate between noninfected and GLRaV-3 infected vines both pre- and postsymptomatically at 1 to 5 m resolution. The best-performing models had 87% accuracy distinguishing between noninfected and asymptomatic vines, and 85% accuracy distinguishing between noninfected and asymptomatic + symptomatic vines. The importance of nonvisible wavelengths suggests that this capacity is driven by disease-induced changes to plant physiology. The results lay a foundation for using the forthcoming hyperspectral satellite Surface Biology and Geology for regional disease monitoring in grapevine and other crop species. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.
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Affiliation(s)
| | - Ryan Pavlick
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109
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12
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Chen W, Modi D, Picot A. Soil and Phytomicrobiome for Plant Disease Suppression and Management under Climate Change: A Review. PLANTS (BASEL, SWITZERLAND) 2023; 12:2736. [PMID: 37514350 PMCID: PMC10384710 DOI: 10.3390/plants12142736] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/17/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023]
Abstract
The phytomicrobiome plays a crucial role in soil and ecosystem health, encompassing both beneficial members providing critical ecosystem goods and services and pathogens threatening food safety and security. The potential benefits of harnessing the power of the phytomicrobiome for plant disease suppression and management are indisputable and of interest in agriculture but also in forestry and landscaping. Indeed, plant diseases can be mitigated by in situ manipulations of resident microorganisms through agronomic practices (such as minimum tillage, crop rotation, cover cropping, organic mulching, etc.) as well as by applying microbial inoculants. However, numerous challenges, such as the lack of standardized methods for microbiome analysis and the difficulty in translating research findings into practical applications are at stake. Moreover, climate change is affecting the distribution, abundance, and virulence of many plant pathogens, while also altering the phytomicrobiome functioning, further compounding disease management strategies. Here, we will first review literature demonstrating how agricultural practices have been found effective in promoting soil health and enhancing disease suppressiveness and mitigation through a shift of the phytomicrobiome. Challenges and barriers to the identification and use of the phytomicrobiome for plant disease management will then be discussed before focusing on the potential impacts of climate change on the phytomicrobiome functioning and disease outcome.
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Affiliation(s)
- Wen Chen
- Ottawa Research and Development Centre, Science and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
- Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Dixi Modi
- Ottawa Research and Development Centre, Science and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
| | - Adeline Picot
- Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France
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13
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Wong CYS. Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications. AOB PLANTS 2023; 15:plad039. [PMID: 37560760 PMCID: PMC10407989 DOI: 10.1093/aobpla/plad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 07/04/2023] [Indexed: 08/11/2023]
Abstract
Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.
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14
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Jeger MJ, Fielder H, Beale T, Szyniszewska AM, Parnell S, Cunniffe NJ. What Can Be Learned by a Synoptic Review of Plant Disease Epidemics and Outbreaks Published in 2021? PHYTOPATHOLOGY 2023; 113:1141-1158. [PMID: 36935375 DOI: 10.1094/phyto-02-23-0069-ia] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
A synoptic review of plant disease epidemics and outbreaks was made using two complementary approaches. The first approach involved reviewing scientific literature published in 2021, in which quantitative data related to new plant disease epidemics or outbreaks were obtained via surveys or similar methodologies. The second approach involved retrieving new records added in 2021 to the CABI Distribution Database, which contains over a million global geographic records of organisms from over 50,000 species. The literature review retrieved 186 articles, describing studies in 62 categories (pathogen species/species complexes) across more than 40 host species on six continents. Pathogen species with more than five articles were Bursaphelenchus xylophilus, 'Candidatus Liberibacter asiaticus', cassava mosaic viruses, citrus tristeza virus, Erwinia amylovora, Fusarium spp. complexes, F. oxysporum f. sp. cubense, Magnaporthe oryzae, maize lethal necrosis co-infecting viruses, Meloidogyne spp. complexes, Pseudomonas syringae pvs., Puccinia striiformis f. sp. tritici, Xylella fastidiosa, and Zymoseptoria tritici. Automated searches of the CABI Distribution Database identified 617 distribution records new in 2021 of 283 plant pathogens. A further manual review of these records confirmed 15 pathogens reported in new locations: apple hammerhead viroid, apple rubbery wood viruses, Aphelenchoides besseyi, Biscogniauxia mediterranea, 'Ca. Liberibacter asiaticus', citrus tristeza virus, Colletotrichum siamense, cucurbit chlorotic yellows virus, Erwinia rhapontici, Erysiphe corylacearum, F. oxysporum f. sp. cubense Tropical race 4, Globodera rostochiensis, Nothophoma quercina, potato spindle tuber viroid, and tomato brown rugose fruit virus. Of these, four pathogens had at least 25% of all records reported in 2021. We assessed two of these pathogens-tomato brown rugose fruit virus and cucurbit chlorotic yellows virus-to be actively emerging in/spreading to new locations. Although three important pathogens-'Ca. Liberibacter asiaticus', citrus tristeza virus, and F. oxysporum f. sp. cubense-were represented in the results of both our literature review and our interrogation of the CABI Distribution Database, in general, our dual approaches revealed distinct sets of plant disease outbreaks and new records, with little overlap. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.
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Affiliation(s)
- Michael J Jeger
- Department of Life Sciences, Imperial College London, Ascot, U.K
| | | | | | | | - Stephen Parnell
- Warwick Crop Centre, University of Warwick, Wellesbourne Campus, Warwick, U.K
| | - Nik J Cunniffe
- Department of Plant Sciences, University of Cambridge, Cambridge, U.K
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15
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Blonda P, Tarantino C, Scortichini M, Maggi S, Tarantino M, Adamo M. Satellite monitoring of bio-fertilizer restoration in olive groves affected by Xylella fastidiosa subsp. pauca. Sci Rep 2023; 13:5695. [PMID: 37029149 PMCID: PMC10082035 DOI: 10.1038/s41598-023-32170-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 03/23/2023] [Indexed: 04/09/2023] Open
Abstract
Xylella fastidiosa subsp. pauca (Xfp), has attacked the olive trees in Southern Italy with severe impacts on the olive agro-ecosystem. To reduce both the Xfp cell concentration and the disease symptom, a bio-fertilizer restoration technique has been used. Our study applied multi-resolution satellite data to evaluate the effectiveness of such technique at both field and tree scale. For field scale, a time series of High Resolution (HR) Sentinel-2 images, acquired in the months of July and August from 2015 to 2020, was employed. First, four spectral indices from treated and untreated fields were compared. Then, their trends were correlated to meteo-events. For tree-scale, Very High Resolution (VHR) Pléiades images were selected at the closest dates of the Sentinel-2 data to investigate the response to treatments of each different cultivar. All indices from HR and VHR images were higher in treated fields than in those untreated. The analysis of VHR indices revealed that Oliarola Salentina can respond better to treatments than Leccino and Cellina cultivars. All findings were in agreement with in-field PCR results. Hence, HR data could be used to evaluate plant conditions at field level after treatments, while VHR imagery could be used to optimize treatment doses per cultivar.
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Affiliation(s)
- Palma Blonda
- Institute of Atmospheric Pollution Research, National Research Council of Italy, c/o Interateneo Physics Department, Via Amendola 173, 70126, Bari, Italy.
| | - Cristina Tarantino
- Institute of Atmospheric Pollution Research, National Research Council of Italy, c/o Interateneo Physics Department, Via Amendola 173, 70126, Bari, Italy
| | - Marco Scortichini
- Research Centre for Olive, Fruit and Citrus Crops, Council for Agricultural Research and Economics, Via di Fioranello 52, 00134, Rome, Italy
| | - Sabino Maggi
- Institute of Atmospheric Pollution Research, National Research Council of Italy, c/o Interateneo Physics Department, Via Amendola 173, 70126, Bari, Italy
| | - Maria Tarantino
- Interateneo Physics Department, University of Bari, Via Amendola 173, 70126, Bari, Italy
| | - Maria Adamo
- Institute of Atmospheric Pollution Research, National Research Council of Italy, c/o Interateneo Physics Department, Via Amendola 173, 70126, Bari, Italy
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16
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Xu K, Ye H. Light scattering in stacked mesophyll cells results in similarity characteristic of solar spectral reflectance and transmittance of natural leaves. Sci Rep 2023; 13:4694. [PMID: 36949090 PMCID: PMC10033640 DOI: 10.1038/s41598-023-31718-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 03/16/2023] [Indexed: 03/24/2023] Open
Abstract
Solar spectral reflectance and transmittance of natural leaves exhibit dramatic similarity. To elucidate the formation mechanism and physiological significance, a radiative transfer model was constructed, and the effects of stacked mesophyll cells, chlorophyll content and leaf thickness on the visible light absorptance of the natural leaves were analyzed. Results indicated that light scattering caused by the stacked mesophyll cells is responsible for the similarity. The optical path of visible light in the natural leaves is increased with the scattering process, resulting in that the visible light transmittance is significantly reduced meanwhile the visible light reflectance is at a low level, thus the visible light absorptance tends to a maximum and the absorption of photosynthetically active radiation (PAR) by the natural leaves is significantly enhanced. Interestingly, as two key leaf functional traits affecting the absorption process of PAR, chlorophyll content and leaf thickness of the natural leaves in a certain environment show a convergent behavior, resulting in the high visible light absorptance of the natural leaves, which demonstrates the PAR utilizing strategies of the natural leaves. This work provides a new perspective for revealing the evolutionary processes and ecological strategies of natural leaves, and can be adopted to guide the improvement directions of crop photosynthesis.
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Affiliation(s)
- Kai Xu
- Department of Thermal Science and Energy Engineering, University of Science and Technology of China, Hefei, 230027, People's Republic of China
| | - Hong Ye
- Department of Thermal Science and Energy Engineering, University of Science and Technology of China, Hefei, 230027, People's Republic of China.
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17
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Mesías-Ruiz GA, Pérez-Ortiz M, Dorado J, de Castro AI, Peña JM. Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review. FRONTIERS IN PLANT SCIENCE 2023; 14:1143326. [PMID: 37056493 PMCID: PMC10088868 DOI: 10.3389/fpls.2023.1143326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 03/01/2023] [Indexed: 06/19/2023]
Abstract
Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks.
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Affiliation(s)
- Gustavo A. Mesías-Ruiz
- Institute of Agricultural Sciences (ICA), Spanish National Research Council (CSIC), Madrid, Spain
- Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas (ETSIAAB), Universidad Politécnica de Madrid, Madrid, Spain
| | - María Pérez-Ortiz
- Centre for Artificial Intelligence, University College London, London, United Kingdom
| | - José Dorado
- Institute of Agricultural Sciences (ICA), Spanish National Research Council (CSIC), Madrid, Spain
| | - Ana I. de Castro
- Environment and Agronomy Department, National Institute for Agricultural and Food Research and Technology (INIA), Spanish National Research Council (CSIC), Madrid, Spain
| | - José M. Peña
- Institute of Agricultural Sciences (ICA), Spanish National Research Council (CSIC), Madrid, Spain
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18
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Zhu F, Su Z, Sanaeifar A, Babu Perumal A, Gouda M, Zhou R, Li X, He Y. Fingerprint Spectral Signatures Revealing the Spatiotemporal Dynamics of Bipolaris Spot Blotch Progression for Presymptomatic Diagnosis. ENGINEERING 2023; 22:171-184. [DOI: 10.1016/j.eng.2022.10.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
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19
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Shin S, Ryu H, Jung JY, Yoon YJ, Kwon G, Lee N, Kim NH, Lee R, Oh J, Baek M, Choi YS, Lee J, Kim KH. Past and Future Epidemiological Perspectives and Integrated Management of Rice Bakanae in Korea. THE PLANT PATHOLOGY JOURNAL 2023; 39:1-20. [PMID: 36760045 PMCID: PMC9929170 DOI: 10.5423/ppj.rw.08.2022.0123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/22/2022] [Accepted: 11/23/2022] [Indexed: 06/18/2023]
Abstract
In the past, rice bakanae was considered an endemic disease that did not cause significant losses in Korea; however, the disease has recently become a serious threat due to climate change, changes in farming practices, and the emergence of fungicide-resistant strains. Since the bakanae outbreak in 2006, its incidence has gradually decreased due to the application of effective control measures such as hot water immersion methods and seed disinfectants. However, in 2013, a marked increase in bakanae incidence was observed, causing problems for rice farmers. Therefore, in this review, we present the potential risks from climate change based on an epidemiological understanding of the pathogen, host plant, and environment, which are the key elements influencing the incidence of bakanae. In addition, disease management options to reduce the disease pressure of bakanae below the economic threshold level are investigated, with a specific focus on resistant varieties, as well as chemical, biological, cultural, and physical control methods. Lastly, as more effective countermeasures to bakanae, we propose an integrated disease management option that combines different control methods, including advanced imaging technologies such as remote sensing. In this review, we revisit and examine bakanae, a traditional seed-borne fungal disease that has not gained considerable attention in the agricultural history of Korea. Based on the understanding of the present significance and anticipated risks of the disease, the findings of this study are expected to provide useful information for the establishment of an effective response strategy to bakanae in the era of climate change.
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Affiliation(s)
- Soobin Shin
- Department of Agricultural Biotechnology, Seoul National University, Seoul 08826,
Korea
| | - Hyunjoo Ryu
- Crop Protection Division, National Institute of Agricultural Sciences, Wanju 55365,
Korea
| | - Jin-Yong Jung
- Department of Agricultural Biotechnology, Seoul National University, Seoul 08826,
Korea
| | - Yoon-Ju Yoon
- Department of Agricultural Biotechnology, Seoul National University, Seoul 08826,
Korea
| | - Gudam Kwon
- Department of Agricultural Biotechnology, Seoul National University, Seoul 08826,
Korea
| | - Nahyun Lee
- Department of Agricultural Biotechnology, Seoul National University, Seoul 08826,
Korea
| | - Na Hee Kim
- Department of Agricultural Biotechnology, Seoul National University, Seoul 08826,
Korea
| | - Rowoon Lee
- Department of Agricultural Biotechnology, Seoul National University, Seoul 08826,
Korea
| | - Jiseon Oh
- Department of Agricultural Biotechnology, Seoul National University, Seoul 08826,
Korea
| | - Minju Baek
- Department of Agricultural Biotechnology, Seoul National University, Seoul 08826,
Korea
| | - Yoon Soo Choi
- Department of Agricultural Biotechnology, Seoul National University, Seoul 08826,
Korea
| | - Jungho Lee
- Interdisciplinary Program of Agriculture and Forest Meteorology, Seoul National University, Seoul 08826,
Korea
| | - Kwang-Hyung Kim
- Department of Agricultural Biotechnology, Seoul National University, Seoul 08826,
Korea
- Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826,
Korea
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20
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Luo Y, Huang H, Roques A. Early Monitoring of Forest Wood-Boring Pests with Remote Sensing. ANNUAL REVIEW OF ENTOMOLOGY 2023; 68:277-298. [PMID: 36198398 DOI: 10.1146/annurev-ento-120220-125410] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Wood-boring pests (WBPs) pose an enormous threat to global forest ecosystems because their early stage infestations show no visible symptoms and can result in rapid and widespread infestations at later stages, leading to large-scale tree death. Therefore, early-stage WBP detection is crucial for prompt management response. Early detection of WBPs requires advanced and effective methods like remote sensing. This review summarizes the applications of various remote sensing sensors, platforms, and detection methods for monitoring WBP infestations. The current capabilities, gaps in capabilities, and future potential for the accurate and rapid detection of WBPs are highlighted.
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Affiliation(s)
- Youqing Luo
- Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing, P.R. China;
- Sino-French Joint Laboratory for Invasive Forest Pests in Eurasia, Beijing Forestry University/French National Research Institute for Agriculture, Food and Environment (INRAE), Beijing, P.R. China/Paris, France
| | - Huaguo Huang
- Research Center of Forest Management Engineering of State Forestry and Grassland Administration, Beijing Forestry University, Beijing, P.R. China;
| | - Alain Roques
- Sino-French Joint Laboratory for Invasive Forest Pests in Eurasia, Beijing Forestry University/French National Research Institute for Agriculture, Food and Environment (INRAE), Beijing, P.R. China/Paris, France
- INRAE-Zoologie Forestière, Centre de recherche Val de Loire, Orléans, France;
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21
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Mustafa G, Zheng H, Li W, Yin Y, Wang Y, Zhou M, Liu P, Bilal M, Jia H, Li G, Cheng T, Tian Y, Cao W, Zhu Y, Yao X. Fusarium head blight monitoring in wheat ears using machine learning and multimodal data from asymptomatic to symptomatic periods. FRONTIERS IN PLANT SCIENCE 2023; 13:1102341. [PMID: 36726669 PMCID: PMC9885105 DOI: 10.3389/fpls.2022.1102341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 12/22/2022] [Indexed: 06/18/2023]
Abstract
The growth of the fusarium head blight (FHB) pathogen at the grain formation stage is a deadly threat to wheat production through disruption of the photosynthetic processes of wheat spikes. Real-time nondestructive and frequent proxy detection approaches are necessary to control pathogen propagation and targeted fungicide application. Therefore, this study examined the ch\lorophyll-related phenotypes or features from spectral and chlorophyll fluorescence for FHB monitoring. A methodology is developed using features extracted from hyperspectral reflectance (HR), chlorophyll fluorescence imaging (CFI), and high-throughput phenotyping (HTP) for asymptomatic to symptomatic disease detection from two consecutive years of experiments. The disease-sensitive features were selected using the Boruta feature-selection algorithm, and subjected to machine learning-sequential floating forward selection (ML-SFFS) for optimum feature combination. The results demonstrated that the biochemical parameters, HR, CFI, and HTP showed consistent alterations during the spike-pathogen interaction. Among the selected disease sensitive features, reciprocal reflectance (RR=1/700) demonstrated the highest coefficient of determination (R 2) of 0.81, with root mean square error (RMSE) of 11.1. The multivariate k-nearest neighbor model outperformed the competing multivariate and univariate models with an overall accuracy of R 2 = 0.92 and RMSE = 10.21. A combination of two to three kinds of features was found optimum for asymptomatic disease detection using ML-SFFS with an average classification accuracy of 87.04% that gradually improved to 95% for a disease severity level of 20%. The study demonstrated the fusion of chlorophyll-related phenotypes with the ML-SFFS might be a good choice for crop disease detection.
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Affiliation(s)
- Ghulam Mustafa
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Hengbiao Zheng
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Wei Li
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Yuming Yin
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Yongqing Wang
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Meng Zhou
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Peng Liu
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Muhammad Bilal
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Haiyan Jia
- Crop Genomics and Bioinformatics Center and National Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Guoqiang Li
- Crop Genomics and Bioinformatics Center and National Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Tao Cheng
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Yongchao Tian
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Weixing Cao
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Yan Zhu
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Xia Yao
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
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22
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Lin J, Huang X, Kou E, Cai W, Zhang H, Zhang X, Liu Y, Li W, Zheng Y, Lei B. Carbon dot based sensing platform for real-time imaging Cu 2+ distribution in plants and environment. Biosens Bioelectron 2023; 219:114848. [PMID: 36327556 DOI: 10.1016/j.bios.2022.114848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/21/2022] [Accepted: 10/20/2022] [Indexed: 11/19/2022]
Abstract
Divalent copper is a double-edged sword for plants, excess or shortage of copper ions will cause adverse reactions in plants. Currently, Cu2+ sensor for plants is still underdeveloped and new technology is urgently required for realizing one-step and real-time detection of Cu2+ in plants. Herein, a home-made and low-cost sensing platform is constructed by using carbon dots (CDs) as the optical probe, electronic devices for image acquisition, and a built-in algorithm program for image processing, which allows the dynamic monitoring of Cu2+ distribution in different plant species with high spatial and temporal resolution. We found that the detection limit of R-CDs for Cu2+ in water sample was 0.375 nM, and 11.7 mg/kg or even less Cu2+ in plants can be visually observed and accurately detected by the sensing platform. Moreover, this sensing platform has also been employed for reporting the spatial distribution of Cu2+ in the external environment of plants, demonstrating its applicability for monitoring Cu2+ both in living plants and the surrounding environment. This study provides a smart sensing platform for precise detection in plant internal and external environments, offering a promising strategy for precision agriculture in real-time and remote-control manners.
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Affiliation(s)
- Junjie Lin
- Key Laboratory for Biobased Materials and Energy of Ministry of Education, College of Materials and Energy, South China Agricultural University, Guangzhou, 510642, PR China
| | - Xiaoman Huang
- Key Laboratory for Biobased Materials and Energy of Ministry of Education, College of Materials and Energy, South China Agricultural University, Guangzhou, 510642, PR China
| | - Erfeng Kou
- Key Laboratory for Biobased Materials and Energy of Ministry of Education, College of Materials and Energy, South China Agricultural University, Guangzhou, 510642, PR China
| | - Wenxiao Cai
- Key Laboratory for Biobased Materials and Energy of Ministry of Education, College of Materials and Energy, South China Agricultural University, Guangzhou, 510642, PR China
| | - Haoran Zhang
- Key Laboratory for Biobased Materials and Energy of Ministry of Education, College of Materials and Energy, South China Agricultural University, Guangzhou, 510642, PR China
| | - Xuejie Zhang
- Key Laboratory for Biobased Materials and Energy of Ministry of Education, College of Materials and Energy, South China Agricultural University, Guangzhou, 510642, PR China
| | - Yingliang Liu
- Key Laboratory for Biobased Materials and Energy of Ministry of Education, College of Materials and Energy, South China Agricultural University, Guangzhou, 510642, PR China
| | - Wei Li
- Key Laboratory for Biobased Materials and Energy of Ministry of Education, College of Materials and Energy, South China Agricultural University, Guangzhou, 510642, PR China.
| | - Yinjian Zheng
- Institute of Urban Agriculture, Chinese Academy of Agricultural Science, Chengdu, 610218, PR China.
| | - Bingfu Lei
- Key Laboratory for Biobased Materials and Energy of Ministry of Education, College of Materials and Energy, South China Agricultural University, Guangzhou, 510642, PR China; Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong, Maoming, 525100, PR China.
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Berger K, Machwitz M, Kycko M, Kefauver SC, Van Wittenberghe S, Gerhards M, Verrelst J, Atzberger C, van der Tol C, Damm A, Rascher U, Herrmann I, Paz VS, Fahrner S, Pieruschka R, Prikaziuk E, Buchaillot ML, Halabuk A, Celesti M, Koren G, Gormus ET, Rossini M, Foerster M, Siegmann B, Abdelbaki A, Tagliabue G, Hank T, Darvishzadeh R, Aasen H, Garcia M, Pôças I, Bandopadhyay S, Sulis M, Tomelleri E, Rozenstein O, Filchev L, Stancile G, Schlerf M. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review. REMOTE SENSING OF ENVIRONMENT 2022; 280:113198. [PMID: 36090616 PMCID: PMC7613382 DOI: 10.1016/j.rse.2022.113198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under shortterm, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions.
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Affiliation(s)
- Katja Berger
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna 46980, Valencia, Spain
- Department of Geography, Ludwig-Maximilians-Universität München (LMU), Luisenstr. 37, 80333 Munich, Germany
| | - Miriam Machwitz
- Remote Sensing and Natural Resources Modelling Group, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, L-4422 Belvaux, Luxembourg
| | - Marlena Kycko
- Department of Geoinformatics Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warszawa, Poland
| | - Shawn C. Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Shari Van Wittenberghe
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna 46980, Valencia, Spain
| | - Max Gerhards
- Earth Observation and Climate Processes, Trier University, 54286 Trier, Germany
| | - Jochem Verrelst
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna 46980, Valencia, Spain
| | - Clement Atzberger
- Institute of Geomatics, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Str. 82, 1190 Vienna, Austria
| | - Christiaan van der Tol
- Faculty Geo-Information Science and Earth Observation, ITC, University of Twente, the Netherlands
| | - Alexander Damm
- Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Uwe Rascher
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Ittai Herrmann
- The Plant Sensing Laboratory, The Robert H. Smith Institute for Plant Sciences and Genetics in Agriculture, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 7610001, Israel
| | - Veronica Sobejano Paz
- Department of Environmental Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Sven Fahrner
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Roland Pieruschka
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Egor Prikaziuk
- Faculty Geo-Information Science and Earth Observation, ITC, University of Twente, the Netherlands
| | - Ma. Luisa Buchaillot
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Andrej Halabuk
- Institute of Landscape Ecology, Slovak Academy of Sciences, 814 99 Bratislava, Slovakia
| | - Marco Celesti
- HE Space for ESA - European Space Agency, European Space Research and Technology Centre (ESA-ESTEC), Keplerlaan 1, 2201, AZ Noordwijk, the Netherlands
| | - Gerbrand Koren
- Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, the Netherlands
| | - Esra Tunc Gormus
- Department of Geomatics Engineering, Karadeniz Technical University, 61080 Trabzon, Turkey
| | - Micol Rossini
- Remote Sensing of Environmental Dynamics Laboratory (LTDA), University of Milano - Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Michael Foerster
- Geoinformation in Environmental Planning Lab, Technische Universität Berlin, 10623 Berlin, Germany
| | - Bastian Siegmann
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Asmaa Abdelbaki
- Earth Observation and Climate Processes, Trier University, 54286 Trier, Germany
| | - Giulia Tagliabue
- Remote Sensing of Environmental Dynamics Laboratory (LTDA), University of Milano - Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Tobias Hank
- Department of Geography, Ludwig-Maximilians-Universität München (LMU), Luisenstr. 37, 80333 Munich, Germany
| | - Roshanak Darvishzadeh
- Faculty Geo-Information Science and Earth Observation, ITC, University of Twente, the Netherlands
| | - Helge Aasen
- Earth Observation and Analysis of Agroecosystems Team, Division Agroecology and Environment, Agroscope, Zurich, Switzerland
- Institute of Agricultural Science, ETH Zürich, Zurich, Switzerland
| | - Monica Garcia
- Research Centre for the Management of Agricultural and Environmental Risks (CEIGRAM), ETSIAAB, Universidad Politécnica de Madrid, 28040, Spain
| | - Isabel Pôças
- ForestWISE - Collaborative Laboratory for Integrated Forest & Fire Management, Quinta de Prados, Campus da UTAD, 5001-801 Vila Real, Portugal
| | | | - Mauro Sulis
- Remote Sensing and Natural Resources Modelling Group, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, L-4422 Belvaux, Luxembourg
| | - Enrico Tomelleri
- Faculty of Science and Technology, Free University of Bozen/Bolzano, Italy
| | - Offer Rozenstein
- Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization—Volcani Institute, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion 7528809, Israel
| | - Lachezar Filchev
- Space Research and Technology Institute, Bulgarian Academy of Sciences (SRTI-BAS), Bulgaria
| | - Gheorghe Stancile
- National Meteorological Administration, Building A, Soseaua Bucuresti-Ploiesti 97, 013686 Bucuresti, Romania
| | - Martin Schlerf
- Remote Sensing and Natural Resources Modelling Group, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, L-4422 Belvaux, Luxembourg
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Zhang J, Cong S, Zhang G, Ma Y, Zhang Y, Huang J. Detecting Pest-Infested Forest Damage through Multispectral Satellite Imagery and Improved UNet+. SENSORS (BASEL, SWITZERLAND) 2022; 22:7440. [PMID: 36236538 PMCID: PMC9570766 DOI: 10.3390/s22197440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 09/22/2022] [Accepted: 09/28/2022] [Indexed: 06/16/2023]
Abstract
Plant pests are the primary biological threats to agricultural and forestry production as well as forest ecosystem. Monitoring forest-pest damage via satellite images is crucial for the development of prevention and control strategies. Previous studies utilizing deep learning to monitor pest-infested damage in satellite imagery adopted RGB images, while multispectral imagery and vegetation indices were not used. Multispectral images and vegetation indices contain a wealth of useful information for detecting plant health, which can improve the precision of pest damage detection. The aim of the study is to further improve forest-pest infestation area segmentation by combining multispectral, vegetation indices and RGB information into deep learning. We also propose a new image segmentation method based on UNet++ with attention mechanism module for detecting forest damage induced by bark beetle and aspen leaf miner in Sentinel-2 images. The ResNeSt101 is used as the feature extraction backbone, and the attention mechanism scSE module is introduced in the decoding phase for improving the image segmentation results. We used Sentinel-2 imagery to produce a dataset based on forest health damage data gathered by the Ministry of Forests, Lands, Natural Resource Operations and Rural Development (FLNRORD) in British Columbia (BC), Canada, during aerial overview surveys (AOS) in 2020. The dataset contains the 11 original Sentinel-2 bands and 13 vegetation indices. The experimental results confirmed that the significance of vegetation indices and multispectral data in enhancing the segmentation effect. The results demonstrated that the proposed method exhibits better segmentation quality and more accurate quantitative indices with overall accuracy of 85.11%, in comparison with the state-of-the-art pest area segmentation methods.
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Crandall SG, Spychalla J, Crouch UT, Acevedo FE, Naegele RP, Miles TD. Rotting Grapes Don't Improve with Age: Cluster Rot Disease Complexes, Management, and Future Prospects. PLANT DISEASE 2022; 106:2013-2025. [PMID: 35108071 DOI: 10.1094/pdis-04-21-0695-fe] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cluster rots can be devastating to grape production around the world. There are several late-season rots that can affect grape berries, including Botrytis bunch rot, sour rot, black rot, Phomopsis fruit rot, bitter rot, and ripe rot. Tight-clustered varieties such as 'Pinot gris', 'Pinot noir', and 'Vignoles' are particularly susceptible to cluster rots. Symptoms or signs for these rots range from discolored berries or gray-brown sporulation in Botrytis bunch rot to sour rot, which smells distinctly of vinegar due to the presence of acetic acid bacteria. This review discusses the common symptoms and disease cycles of these different cluster rots. It also includes useful updates on disease diagnostics and management practices, including cultural practices in commercial vineyards and future prospects for disease management. By understanding what drives the development of different cluster rots, researchers will be able to identify new avenues for research to control these critical pathogens.
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Affiliation(s)
- Sharifa G Crandall
- Pennsylvania State University, Department of Plant Pathology & Environmental Microbiology, University Park, PA 16802
| | - Jamie Spychalla
- Pennsylvania State University, Department of Plant Pathology & Environmental Microbiology, University Park, PA 16802
| | - Uma T Crouch
- Pennsylvania State University, Department of Plant Pathology & Environmental Microbiology, University Park, PA 16802
| | - Flor E Acevedo
- Pennsylvania State University, Department of Entomology, University Park, PA 16802
| | - Rachel P Naegele
- United States Department of Agriculture-Agricultural Research Station, Parlier, CA 93648
| | - Timothy D Miles
- Michigan State University, Department of Plant, Soil and Microbial Sciences, East Lansing, MI 48824
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Cao Y, Yuan P, Xu H, Martínez-Ortega JF, Feng J, Zhai Z. Detecting Asymptomatic Infections of Rice Bacterial Leaf Blight Using Hyperspectral Imaging and 3-Dimensional Convolutional Neural Network With Spectral Dilated Convolution. FRONTIERS IN PLANT SCIENCE 2022; 13:963170. [PMID: 35909723 PMCID: PMC9328758 DOI: 10.3389/fpls.2022.963170] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Rice is one of the most important food crops for human beings. Its total production ranks third in the grain crop output. Bacterial Leaf Blight (BLB), as one of the three major diseases of rice, occurs every year, posing a huge threat to rice production and safety. There is an asymptomatic period between the infection and the onset periods, and BLB will spread rapidly and widely under suitable conditions. Therefore, accurate detection of early asymptomatic BLB is very necessary. The purpose of this study was to test the feasibility of detecting early asymptomatic infection of the rice BLB disease based on hyperspectral imaging and Spectral Dilated Convolution 3-Dimensional Convolutional Neural Network (SDC-3DCNN). First, hyperspectral images were obtained from rice leaves infected with the BLB disease at the tillering stage. The spectrum was smoothed by the Savitzky-Golay (SG) method, and the wavelength between 450 and 950 nm was intercepted for analysis. Then Principal Component Analysis (PCA) and Random Forest (RF) were used to extract the feature information from the original spectra as inputs. The overall performance of the SDC-3DCNN model with different numbers of input features and different spectral dilated ratios was evaluated. Lastly, the saliency map visualization was used to explain the sensitivity of individual wavelengths. The results showed that the performance of the SDC-3DCNN model reached an accuracy of 95.4427% when the number of inputs is 50 characteristic wavelengths (extracted by RF) and the dilated ratio is set at 5. The saliency-sensitive wavelengths were identified in the range from 530 to 570 nm, which overlaps with the important wavelengths extracted by RF. According to our findings, combining hyperspectral imaging and deep learning can be a reliable approach for identifying early asymptomatic infection of the rice BLB disease, providing sufficient support for early warning and rice disease prevention.
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Affiliation(s)
- Yifei Cao
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
- College of Engineering, Nanjing Agricultural University, Nanjing, China
| | - Peisen Yuan
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
| | - Huanliang Xu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
| | - José Fernán Martínez-Ortega
- Departamento de Ingeniería Telemática y Electrónica (DTE), Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Jiarui Feng
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
- College of Engineering, Nanjing Agricultural University, Nanjing, China
| | - Zhaoyu Zhai
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
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Epidemiologically-based strategies for the detection of emerging plant pathogens. Sci Rep 2022; 12:10972. [PMID: 35768558 PMCID: PMC9243127 DOI: 10.1038/s41598-022-13553-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
Emerging pests and pathogens of plants are a major threat to natural and managed ecosystems worldwide. Whilst it is well accepted that surveillance activities are key to both the early detection of new incursions and the ability to identify pest-free areas, the performance of these activities must be evaluated to ensure they are fit for purpose. This requires consideration of the number of potential hosts inspected or tested as well as the epidemiology of the pathogen and the detection method used. In the case of plant pathogens, one particular concern is whether the visual inspection of plant hosts for signs of disease is able to detect the presence of these pathogens at low prevalences, given that it takes time for these symptoms to develop. One such pathogen is the ST53 strain of the vector-borne bacterial pathogen Xylella fastidiosa in olive hosts, which was first identified in southern Italy in 2013. Additionally, X. fastidiosa ST53 in olive has a rapid rate of spread, which could also have important implications for surveillance. In the current study, we evaluate how well visual surveillance would be expected to perform for this pathogen and investigate whether molecular testing of either tree hosts or insect vectors offer feasible alternatives. Our results identify the main constraints to each of these strategies and can be used to inform and improve both current and future surveillance activities.
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Pineda M, Pérez-Bueno ML, Barón M. Novel Vegetation Indices to Identify Broccoli Plants Infected With Xanthomonas campestris pv. campestris. FRONTIERS IN PLANT SCIENCE 2022; 13:790268. [PMID: 35812917 PMCID: PMC9265216 DOI: 10.3389/fpls.2022.790268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
A rapid diagnosis of black rot in brassicas, a devastating disease caused by Xanthomonas campestris pv. campestris (Xcc), would be desirable to avoid significant crop yield losses. The main aim of this work was to develop a method of detection of Xcc infection on broccoli leaves. Such method is based on the use of imaging sensors that capture information about the optical properties of leaves and provide data that can be implemented on machine learning algorithms capable of learning patterns. Based on this knowledge, the algorithms are able to classify plants into categories (healthy and infected). To ensure the robustness of the detection method upon future alterations in climate conditions, the response of broccoli plants to Xcc infection was analyzed under a range of growing environments, taking current climate conditions as reference. Two projections for years 2081-2100 were selected, according to the Assessment Report of Intergovernmental Panel on Climate Change. Thus, the response of broccoli plants to Xcc infection and climate conditions has been monitored using leaf temperature and five conventional vegetation indices (VIs) derived from hyperspectral reflectance. In addition, three novel VIs, named diseased broccoli indices (DBI1-DBI3), were defined based on the spectral reflectance signature of broccoli leaves upon Xcc infection. Finally, the nine parameters were implemented on several classifying algorithms. The detection method offering the best performance of classification was a multilayer perceptron-based artificial neural network. This model identified infected plants with accuracies of 88.1, 76.9, and 83.3%, depending on the growing conditions. In this model, the three Vis described in this work proved to be very informative parameters for the disease detection. To our best knowledge, this is the first time that future climate conditions have been taken into account to develop a robust detection model using classifying algorithms.
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Affiliation(s)
- Mónica Pineda
- Department of Biochemistry and Molecular and Cell Biology of Plants, Estación Experimental del Zaidín, Spanish National Research Council (CSIC), Granada, Spain
| | - María Luisa Pérez-Bueno
- Department of Biochemistry and Molecular and Cell Biology of Plants, Estación Experimental del Zaidín, Spanish National Research Council (CSIC), Granada, Spain
- Department of Plant Physiology, Facultad de Farmacia, University of Granada, Granada, Spain
| | - Matilde Barón
- Department of Biochemistry and Molecular and Cell Biology of Plants, Estación Experimental del Zaidín, Spanish National Research Council (CSIC), Granada, Spain
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Hyperspectral Reflectance Proxies to Diagnose In-Field Fusarium Head Blight in Wheat with Machine Learning. REMOTE SENSING 2022. [DOI: 10.3390/rs14122784] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Hyperspectral reflectance (HR) technology as proxy approach to diagnose fusarium head blight (FHB) in wheat crop could be a real-time and non-invasive approach for its in-field management to reduce grain damage. In-field canopy’s non-imaging HR (400–2400 nm using ground-based spectrometer system), photosynthesis rate (Pn) and disease severity (DS) data were simultaneously acquired from artificially inoculated wheat plots over a period of two years (2020 and 2021) in the field. Subsequently, continuous wavelet transform (CWT) was employed to select the consistent spectral bands (CSBs) and to develop the canopy-based difference indices with criterion of variable importance score using random forest—recursive feature elimination. Thereby, different machine learning algorithms were employed for FHB classification and multivariate estimation, and linear regression models to evaluate the newly developed indices against conventional vegetation indices. The results showed that inoculation reduced the Pn rate of spikes, elevated reflectance in visible and short-wave infrared regions and decreased in near infrared region at different days after inoculation (DAI). CWT analysis selected five CSBs (401, 460, 570, 786 and 840 nm) employing datasets from 2020 and 2021. These spectral bands were employed to develop wheat fusarium canopy indices (WFCI1 and WFCI2). Considering the average classification accuracy (ACA) in both years of experiments, WFCI1 manifested a maximum ACA of 75% at 5 DAI with DS of 9.73% which raised to 100% at 10 DAI with a DS of 18%. ACA mentions the averaged results of all machine learning classifiers (MLC). While in the perspective of MLC, random forest (RF) outperformed the rest of the MLC, individually, it revealed 100% classification accuracy through WFCI1 at DS 10.78% on the eight DAI. The univariate estimation of disease based on WFCI1 and WFCI2 with independent data produced R2 and root mean square error (RMSE) values of 0.80 and 14.7, and 0.81 and13.50, respectively. However, Knn regression analysis with both canopy indices (WFCI1 and WFCI2) manifested the maximum accuracy for disease estimation with RMSE of 11.61 and R2 = 0.83. Conclusively, the newly proposed HR indices show great potential as proxy approach for detecting FHB at early stage and understanding the physical state of crops in field conditions for the better management and control of plant diseases.
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Kuska MT, Heim RHJ, Geedicke I, Gold KM, Brugger A, Paulus S. Digital plant pathology: a foundation and guide to modern agriculture. JOURNAL OF PLANT DISEASES AND PROTECTION : SCIENTIFIC JOURNAL OF THE GERMAN PHYTOMEDICAL SOCIETY (DPG) 2022; 129:457-468. [PMID: 35502325 PMCID: PMC9046714 DOI: 10.1007/s41348-022-00600-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements. Selecting a sensor type (e.g., thermography or hyperspectral imaging), sensor platform (e.g., rovers, unmanned aerial vehicles, or satellites), and the problem-specific spatial and temporal scale adds to the challenge as all pathosystems are unique and differ in their interactions and symptoms, or lack thereof. Adding host-pathogen-environment interactions across time and space increases the complexity even further. Large data sets are necessary to enable a deeper understanding of these interactions. Therefore, modern machine learning methods are developed to realize the fast data analysis of such complex data sets. This reduces not only human effort but also enables an objective data perusal. Especially deep learning approaches show a high potential to identify probable cohesive parameters during plant-pathogen-environment interactions. Unfortunately, the performance and reliability of developed methods are often doubted by the potential user. Gaining their trust is thus needed for real field applications. Linking biological causes to machine learning features and a clear communication, even for non-experts of such results, is a crucial task that will bridge the gap between theory and praxis of a newly developed application. Therefore, we suggest a global connection of experts and data as the basis for defining a common and goal-oriented research roadmap. Such high interconnectivity will likely increase the chances of swift, successful progress in research and practice. A coordination within international excellence clusters will be useful to reduce redundancy of research while supporting the creation and progress of complementary research. With this review, we would like to discuss past research, achievements, as well as recurring and new challenges. Having such a retrospect available, we will attempt to reveal future challenges and provide a possible direction elevating the next decade of research in digital plant pathology.
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Affiliation(s)
- Matheus Thomas Kuska
- North Rhine-Westphalia Chamber of Agriculture, Gartenstraße 11, 50765 Cologne, Germany
| | - René H. J. Heim
- Institute of Sugar Beet Research, Holtenser Landstraße 77, 37079 Göttingen, Germany
| | - Ina Geedicke
- Institute of Sugar Beet Research, Holtenser Landstraße 77, 37079 Göttingen, Germany
| | - Kaitlin M. Gold
- Plant Pathology and Plant-Microbe Biology College of Agriculture and Life Science, Cornell University, Cornell AgriTech, 15 Castle Creek Drive, Geneva, 14456 USA
| | - Anna Brugger
- Bildungs- und Beratungszentrum Arenenberg, Arenenberg 8, 8268 Salenstein, Switzerland
| | - Stefan Paulus
- Institute of Sugar Beet Research, Holtenser Landstraße 77, 37079 Göttingen, Germany
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Surveying soil-borne disease development on wild rocket salad crop by proximal sensing based on high-resolution hyperspectral features. Sci Rep 2022; 12:5098. [PMID: 35332172 PMCID: PMC8948195 DOI: 10.1038/s41598-022-08969-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 03/14/2022] [Indexed: 11/08/2022] Open
Abstract
Wild rocket (Diplotaxis tenuifolia, Brassicaceae) is a baby-leaf vegetable crop of high economic interest, used in ready-to-eat minimally processed salads, with an appreciated taste and nutraceutical features. Disease management is key to achieving the sustainability of the entire production chain in intensive systems, where synthetic fungicides are limited or not permitted. In this context, soil-borne pathologies, much feared by growers, are becoming a real emergency. Digital screening of green beds can be implemented in order to optimize the use of sustainable means. The current study used a high-resolution hyperspectral array (spectroscopy at 350-2500 nm) to attempt to follow the progression of symptoms of Rhizoctonia, Sclerotinia, and Sclerotium disease across four different severity levels. A Random Forest machine learning model reduced dimensions of the training big dataset allowing to compute de novo vegetation indices specifically informative about canopy decay caused by all basal pathogenic attacks. Their transferability was also tested on the canopy dataset, which was useful for assessing the health status of wild rocket plants. Indeed, the progression of symptoms associated with soil-borne pathogens is closely related to the reduction of leaf absorbance of the canopy in certain ranges of visible and shortwave infrared spectral regions sensitive to reduction of chlorophyll and other pigments as well as to modifications of water content and turgor.
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Wearable Crop Sensor Based on Nano-Graphene Oxide for Noninvasive Real-Time Monitoring of Plant Water. MEMBRANES 2022; 12:membranes12040358. [PMID: 35448328 PMCID: PMC9026295 DOI: 10.3390/membranes12040358] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/07/2022] [Accepted: 03/21/2022] [Indexed: 02/01/2023]
Abstract
Real-time noninvasive monitoring of crop water information is an important basis for water-saving irrigation and precise management. Nano-electronic technology has the potential to enable smart plant sensors to communicate with electronic devices and promote the automatic and accurate distribution of water, fertilizer, and medicine to improve crop productivity. In this work, we present a new flexible graphene oxide (GO)-based noninvasive crop water sensor with high sensitivity, fast responsibility and good bio-interface compatibility. The humidity monitoring sensitivity of the sensor reached 7945 Ω/% RH, and the response time was 20.3 s. We first present the correlation monitoring of crop physiological characteristics by using flexible wearable sensors and photosynthesis systems, and have studied the response and synergistic effect of net photosynthetic rate and transpiration rate of maize plants under different light environments. Results show that in situ real-time sensing of plant transpiration was realized, and the internal water transportation within plants could be monitored dynamically. The synergistic effect of net photosynthetic rate and transpiration of maize plants can be jointly tested. This study provides a new technical method to carry out quantitative monitoring of crop water in the entire life cycle and build smart irrigation systems. Moreover, it holds great potential in studying individual plant biology and could provide basic support to carry out precise monitoring of crop physiological information.
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Feng ZH, Wang LY, Yang ZQ, Zhang YY, Li X, Song L, He L, Duan JZ, Feng W. Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning. FRONTIERS IN PLANT SCIENCE 2022; 13:828454. [PMID: 35386677 PMCID: PMC8977770 DOI: 10.3389/fpls.2022.828454] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 01/20/2022] [Indexed: 06/14/2023]
Abstract
Powdery mildew has a negative impact on wheat growth and restricts yield formation. Therefore, accurate monitoring of the disease is of great significance for the prevention and control of powdery mildew to protect world food security. The canopy spectral reflectance was obtained using a ground feature hyperspectrometer during the flowering and filling periods of wheat, and then the Savitzky-Golay method was used to smooth the measured spectral data, and as original reflectivity (OR). Firstly, the OR was spectrally transformed using the mean centralization (MC), multivariate scattering correction (MSC), and standard normal variate transform (SNV) methods. Secondly, the feature bands of above four transformed spectral data were extracted through a combination of the Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) algorithms. Finally, partial least square regression (PLSR), support vector regression (SVR), and random forest regression (RFR) were used to construct an optimal monitoring model for wheat powdery mildew disease index (mean disease index, mDI). The results showed that after Pearson correlation, two-band optimization combinations and machine learning method modeling comparisons, the comprehensive performance of the MC spectrum data was the best, and it was a better method for pretreating disease spectrum data. The transformed spectral data combined with the CARS-SPA algorithm was able to extract the characteristic bands more effectively. The number of bands screened was more than the number of bands extracted by the OR data, and the band positions were more evenly distributed. In comparison of different machine learning modeling methods, the RFR model performed the best (coefficient of determination, R 2 = 0.741-0.852), while the SVR and PLSR models performed similarly (R 2 = 0.733-0.836). Taken together, the estimation accuracy of spectral data transformation using the MC method combined with the RFR model (MC-RFR) was the highest, the model R 2 was 0.849-0.852, and the root mean square error (RMSE) and the mean absolute error (MAE) ranged from 2.084 to 2.177 and 1.684 to 1.777, respectively. Compared with the OR combined with the RFR model (OR-RFR), the R 2 increased by 14.39%, and the R 2 of RMSE and MAE decreased by 23.9 and 27.87%. Also, the monitoring accuracy of flowering stage is better than that of grain filling stage, which is due to the relative stability of canopy structure in flowering stage. It can be seen that without changing the shape of the spectral curve, and that the use of MC to preprocess spectral data, the use of CARS and SPA algorithms to extract characteristic bands, and the use of RFR modeling methods to enhance the synergy between multiple variables, and the established model (MC-CARS-SPA-RFR) can better extract the covariant relationship between the canopy spectrum and the disease, thereby improving the monitoring accuracy of wheat powdery mildew. The research results of this study provide ideas and methods for realizing high-precision remote sensing monitoring of crop disease status.
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Affiliation(s)
- Zi-Heng Feng
- State Key Laboratory of Wheat and Maize Crop Science, CIMMYT-China Wheat and Maize Joint Research Center, Henan Agricultural University, Zhengzhou, China
- Information and Management Science College, Henan Agricultural University, Zhengzhou, China
| | - Lu-Yuan Wang
- State Key Laboratory of Wheat and Maize Crop Science, CIMMYT-China Wheat and Maize Joint Research Center, Henan Agricultural University, Zhengzhou, China
| | - Zhe-Qing Yang
- State Key Laboratory of Wheat and Maize Crop Science, CIMMYT-China Wheat and Maize Joint Research Center, Henan Agricultural University, Zhengzhou, China
| | - Yan-Yan Zhang
- State Key Laboratory of Wheat and Maize Crop Science, CIMMYT-China Wheat and Maize Joint Research Center, Henan Agricultural University, Zhengzhou, China
| | - Xiao Li
- College of Science, Henan Agricultural University, Zhengzhou, China
| | - Li Song
- State Key Laboratory of Wheat and Maize Crop Science, CIMMYT-China Wheat and Maize Joint Research Center, Henan Agricultural University, Zhengzhou, China
| | - Li He
- State Key Laboratory of Wheat and Maize Crop Science, CIMMYT-China Wheat and Maize Joint Research Center, Henan Agricultural University, Zhengzhou, China
| | - Jian-Zhao Duan
- State Key Laboratory of Wheat and Maize Crop Science, CIMMYT-China Wheat and Maize Joint Research Center, Henan Agricultural University, Zhengzhou, China
| | - Wei Feng
- State Key Laboratory of Wheat and Maize Crop Science, CIMMYT-China Wheat and Maize Joint Research Center, Henan Agricultural University, Zhengzhou, China
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Tanner F, Tonn S, de Wit J, Van den Ackerveken G, Berger B, Plett D. Sensor-based phenotyping of above-ground plant-pathogen interactions. PLANT METHODS 2022; 18:35. [PMID: 35313920 PMCID: PMC8935837 DOI: 10.1186/s13007-022-00853-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 02/08/2022] [Indexed: 05/20/2023]
Abstract
Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease.
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Affiliation(s)
- Florian Tanner
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
| | - Sebastian Tonn
- Department of Biology, Plant-Microbe Interactions, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Jos de Wit
- Department of Imaging Physics, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands
| | - Guido Van den Ackerveken
- Department of Biology, Plant-Microbe Interactions, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Bettina Berger
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
| | - Darren Plett
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
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Weingarten E, Martin RE, Hughes RF, Vaughn NR, Shafron E, Asner GP. Early detection of a tree pathogen using airborne remote sensing. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e2519. [PMID: 34918400 DOI: 10.1002/eap.2519] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 08/25/2021] [Indexed: 06/14/2023]
Abstract
Native forests of Hawai'i Island are experiencing an ecological crisis in the form of Rapid 'Ōhi'a Death (ROD), a recently characterized disease caused by two fungal pathogens in the genus Ceratocystis. Since approximately 2010, this disease has caused extensive mortality of Hawai'i's keystone endemic tree, known as 'ōhi'a (Metrosideros polymorpha). Visible symptoms of ROD include rapid browning of canopy leaves, followed by death of the tree within weeks. This quick progression leading to tree mortality makes early detection critical to understanding where the disease will move at a timescale feasible for controlling the disease. We used repeat laser-guided imaging spectroscopy (LGIS) of forests on Hawai'i Island collected by the Global Airborne Observatory (GAO) in 2018 and 2019 to derive maps of foliar trait indices previously found to be important in distinguishing between ROD-infected and healthy 'ōhi'a canopies. Data from these maps were used to develop a prognostic indicator of tree stress prior to the visible onset of browning. We identified canopies that were green in 2018, but became brown in 2019 (defined as "to become brown"; TBB), and a corresponding set of canopies that remained green. The data mapped in 2018 showed separability of foliar trait indices between TBB and green 'ōhi'a, indicating early detection of canopy stress prior to the onset of ROD. Overall, a combination of linear and non-linear analyses revealed canopy water content (CWC), foliar tannins, leaf mass per area (LMA), phenols, cellulose, and non-structural carbohydrates (NSC) are primary drivers of the prognostic spectral capability which collectively result in strong consistent changes in leaf spectral reflectance in the near-infrared (700-1300 nm) and shortwave-infrared regions (1300-2500 nm). Results provide insight into the underlying foliar traits that are indicative of physiological responses of M. polymorpha trees infected with Ceratocycstis and suggest that imaging spectroscopy is an effective tool for identifying trees likely to succumb to ROD prior to the onset of visible symptoms.
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Affiliation(s)
- Erin Weingarten
- Center for Global Discovery and Conservation Science, Arizona State University, Tempe, Arizona, USA
- School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, Arizona, USA
| | - Roberta E Martin
- Center for Global Discovery and Conservation Science, Arizona State University, Tempe, Arizona, USA
- School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, Arizona, USA
| | | | - Nicholas R Vaughn
- Center for Global Discovery and Conservation Science, Arizona State University, Tempe, Arizona, USA
| | - Ethan Shafron
- Center for Global Discovery and Conservation Science, Arizona State University, Tempe, Arizona, USA
| | - Gregory P Asner
- Center for Global Discovery and Conservation Science, Arizona State University, Tempe, Arizona, USA
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Encinas-Valero M, Esteban R, Hereş AM, Becerril JM, García-Plazaola JI, Artexe U, Vivas M, Solla A, Moreno G, Curiel Yuste J. Photoprotective compounds as early markers to predict holm oak crown defoliation in declining Mediterranean savannahs. TREE PHYSIOLOGY 2022; 42:208-224. [PMID: 33611551 DOI: 10.1093/treephys/tpab006] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 01/04/2021] [Indexed: 06/12/2023]
Abstract
Dehesas, human-shaped savannah-like ecosystems, where the overstorey is mainly dominated by the evergreen holm oak (Quercus ilex L. subsp. ballota (Desf.) Samp.), are classified as a global conservation priority. Despite being Q. ilex a species adapted to the harsh Mediterranean environmental conditions, recent decades have witnessed worrisome trends of climate-change-induced holm oak mortality. Holm oak decline is evidenced by tree vigour loss, gradual defoliation and ultimately, death. However, before losing leaves, trees undergo leaf-level physiological adjustments in response to stress that may represent a promising field to develop biochemical early markers of holm oak decline. This study explored holm oak photoprotective responses (pigments, tocopherols and photosynthetic performance) in 144 mature holm oak trees with different health statuses (i.e., crown defoliation percentages) from healthy to first-stage declining individuals. Our results indicate differential photochemical performance and photoprotective compounds concentration depending on the trees' health status. Declining trees showed higher energy dissipation yield, lower photochemical efficiency and enhanced photoprotective compounds. In the case of total violaxanthin cycle pigments (VAZ) and tocopherols, shifts in leaf contents were significant at very early stages of crown defoliation, even before visual symptoms of decline were evident, supporting the value of these biochemical compounds as early stress markers. Linear mixed-effects models results showed an acute response, both in the photosynthesis performance index and in the concentration of foliar tocopherols, during the onset of tree decline, whereas VAZ showed a more gradual response along the defoliation gradient of the crown. These results collectively demonstrate that once a certain threshold of leaf physiological damage is surpassed, that leaf cannot counteract oxidative stress and progressive loss of leaves occurs. Therefore, the use of both photosynthesis performance indexes and the leaf tocopherols concentration as early diagnostic tools might predict declining trends, facilitating the implementation of preventive measures to counteract crown defoliation.
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Affiliation(s)
- Manuel Encinas-Valero
- BC3-Basque Centre for Climate Change, Scientific Campus of the University of the Basque Country, 48940 Leioa, Bizkaia, Spain
| | - Raquel Esteban
- Department of Plant Biology and Ecology, University of Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain
| | - Ana-Maria Hereş
- BC3-Basque Centre for Climate Change, Scientific Campus of the University of the Basque Country, 48940 Leioa, Bizkaia, Spain
- Department of Forest Sciences, Transilvania University of Braşov, Sirul Beethoven-1, 500123 Braşov, Romania
| | - José María Becerril
- Department of Plant Biology and Ecology, University of Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain
| | - José Ignacio García-Plazaola
- Department of Plant Biology and Ecology, University of Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain
| | - Unai Artexe
- Department of Plant Biology and Ecology, University of Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain
| | - María Vivas
- Faculty of Forestry, Institute for Dehesa Research (INDEHESA), University of Extremadura, Avenida Virgen del Puerto 2, 10600 Plasencia, Spain
| | - Alejandro Solla
- Faculty of Forestry, Institute for Dehesa Research (INDEHESA), University of Extremadura, Avenida Virgen del Puerto 2, 10600 Plasencia, Spain
| | - Gerardo Moreno
- Faculty of Forestry, Institute for Dehesa Research (INDEHESA), University of Extremadura, Avenida Virgen del Puerto 2, 10600 Plasencia, Spain
| | - Jorge Curiel Yuste
- BC3-Basque Centre for Climate Change, Scientific Campus of the University of the Basque Country, 48940 Leioa, Bizkaia, Spain
- IKERBASQUE, Basque Foundation for SciencePlaza Euskadi 548009 Bilbao, Bizkaia, Spain
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37
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Terentev A, Dolzhenko V, Fedotov A, Eremenko D. Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review. SENSORS 2022; 22:s22030757. [PMID: 35161504 PMCID: PMC8839015 DOI: 10.3390/s22030757] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 01/13/2022] [Accepted: 01/16/2022] [Indexed: 01/10/2023]
Abstract
The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants' disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.
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Affiliation(s)
- Anton Terentev
- All-Russian Institute of Plant Protection, 3 Podbelsokogo Str., Pushkin, 196608 Saint Petersburg, Russia;
- Correspondence: (A.T.); (A.F.); Tel.: +7-921-937-1550 (A.T.); +7-921-741-6303 (A.F.)
| | - Viktor Dolzhenko
- All-Russian Institute of Plant Protection, 3 Podbelsokogo Str., Pushkin, 196608 Saint Petersburg, Russia;
| | - Alexander Fedotov
- World-Class Research Center «Advanced Digital Technologies», Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya Str., 195251 Saint Petersburg, Russia;
- Correspondence: (A.T.); (A.F.); Tel.: +7-921-937-1550 (A.T.); +7-921-741-6303 (A.F.)
| | - Danila Eremenko
- World-Class Research Center «Advanced Digital Technologies», Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya Str., 195251 Saint Petersburg, Russia;
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Nißler R, Müller AT, Dohrman F, Kurth L, Li H, Cosio EG, Flavel BS, Giraldo JP, Mithöfer A, Kruss S. Detektion und Visualisierung der Pflanzen‐Pathogen‐Response durch Nah‐Infrarot‐fluoreszente Polyphenolsensoren. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202108373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Robert Nißler
- Physikalische Chemie II Ruhr-Universität Bochum Universitätsstraße 150 44801 Bochum Deutschland
- Institut für Physikalische Chemie Georg-August Universität Göttingen Tammannstraße 6 37077 Göttingen Deutschland
| | - Andrea T. Müller
- Research Group Plant Defense Physiology Max-Planck-Institut für Chemische Ökologie Hans-Knöll-Straße 8 07745 Jena Deutschland
| | - Frederike Dohrman
- Institut für Physikalische Chemie Georg-August Universität Göttingen Tammannstraße 6 37077 Göttingen Deutschland
| | - Larissa Kurth
- Institut für Physikalische Chemie Georg-August Universität Göttingen Tammannstraße 6 37077 Göttingen Deutschland
| | - Han Li
- Institute of Nanotechnology Karlsruhe Institute of Technology (KIT) 76344 Eggenstein-Leopoldshafen Deutschland
| | - Eric G. Cosio
- Institute for Nature Earth and Energy (INTE-PUCP) Pontifical Catholic University of Peru Av. Universitaria 1801, San Miguel 15088 Lima Peru
| | - Benjamin S. Flavel
- Institute of Nanotechnology Karlsruhe Institute of Technology (KIT) 76344 Eggenstein-Leopoldshafen Deutschland
| | - Juan Pablo Giraldo
- Department of Botany and Plant Sciences University of California Riverside CA 92507 USA
| | - Axel Mithöfer
- Research Group Plant Defense Physiology Max-Planck-Institut für Chemische Ökologie Hans-Knöll-Straße 8 07745 Jena Deutschland
| | - Sebastian Kruss
- Physikalische Chemie II Ruhr-Universität Bochum Universitätsstraße 150 44801 Bochum Deutschland
- Institut für Physikalische Chemie Georg-August Universität Göttingen Tammannstraße 6 37077 Göttingen Deutschland
- Fraunhofer-Institut für Mikroelektronische Schaltungen Finkenstraße 61 47057 Duisburg Deutschland
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Feng Z, Song L, Duan J, He L, Zhang Y, Wei Y, Feng W. Monitoring Wheat Powdery Mildew Based on Hyperspectral, Thermal Infrared, and RGB Image Data Fusion. SENSORS 2021; 22:s22010031. [PMID: 35009575 PMCID: PMC8747141 DOI: 10.3390/s22010031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/18/2021] [Accepted: 12/19/2021] [Indexed: 11/23/2022]
Abstract
Powdery mildew severely affects wheat growth and yield; therefore, its effective monitoring is essential for the prevention and control of the disease and global food security. In the present study, a spectroradiometer and thermal infrared cameras were used to obtain hyperspectral signature and thermal infrared images data, and thermal infrared temperature parameters (TP) and texture features (TF) were extracted from the thermal infrared images and RGB images of wheat with powdery mildew, during the wheat flowering and filling periods. Based on the ten vegetation indices from the hyperspectral data (VI), TF and TP were integrated, and partial least square regression, random forest regression (RFR), and support vector machine regression (SVR) algorithms were used to construct a prediction model for a wheat powdery mildew disease index. According to the results, the prediction accuracy of RFR was higher than in other models, under both single data source modeling and multi-source data modeling; among the three data sources, VI was the most suitable for powdery mildew monitoring, followed by TP, and finally TF. The RFR model had stable performance in multi-source data fusion modeling (VI&TP&TF), and had the optimal estimation performance with 0.872 and 0.862 of R2 for calibration and validation, respectively. The application of multi-source data collaborative modeling could improve the accuracy of remote sensing monitoring of wheat powdery mildew, and facilitate the achievement of high-precision remote sensing monitoring of crop disease status.
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Affiliation(s)
- Ziheng Feng
- State Key Laboratory of Wheat and Maize Crop Science, Agronomy College, Henan Agriculture University, Zhengzhou 450046, China; (Z.F.); (L.S.); (J.D.); (L.H.); (Y.Z.); (Y.W.)
- Information and Management Science College, Henan Agricultural University, Zhengzhou 450046, China
| | - Li Song
- State Key Laboratory of Wheat and Maize Crop Science, Agronomy College, Henan Agriculture University, Zhengzhou 450046, China; (Z.F.); (L.S.); (J.D.); (L.H.); (Y.Z.); (Y.W.)
| | - Jianzhao Duan
- State Key Laboratory of Wheat and Maize Crop Science, Agronomy College, Henan Agriculture University, Zhengzhou 450046, China; (Z.F.); (L.S.); (J.D.); (L.H.); (Y.Z.); (Y.W.)
| | - Li He
- State Key Laboratory of Wheat and Maize Crop Science, Agronomy College, Henan Agriculture University, Zhengzhou 450046, China; (Z.F.); (L.S.); (J.D.); (L.H.); (Y.Z.); (Y.W.)
| | - Yanyan Zhang
- State Key Laboratory of Wheat and Maize Crop Science, Agronomy College, Henan Agriculture University, Zhengzhou 450046, China; (Z.F.); (L.S.); (J.D.); (L.H.); (Y.Z.); (Y.W.)
| | - Yongkang Wei
- State Key Laboratory of Wheat and Maize Crop Science, Agronomy College, Henan Agriculture University, Zhengzhou 450046, China; (Z.F.); (L.S.); (J.D.); (L.H.); (Y.Z.); (Y.W.)
| | - Wei Feng
- State Key Laboratory of Wheat and Maize Crop Science, Agronomy College, Henan Agriculture University, Zhengzhou 450046, China; (Z.F.); (L.S.); (J.D.); (L.H.); (Y.Z.); (Y.W.)
- Correspondence:
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Abstract
Plant disease threatens the environmental and financial sustainability of crop production, causing $220 billion in annual losses. The dire threat disease poses to modern agriculture demands tools for better detection and monitoring to prevent crop loss and input waste. The nascent discipline of plant disease sensing, or the science of using proximal and/or remote sensing to detect and diagnose disease, offers great promise to extend monitoring to previously unachievable resolutions, a basis to construct multiscale surveillance networks for early warning, alert, and response at low latency, an opportunity to mitigate loss while optimizing protection, and a dynamic new dimension to agricultural systems biology. Despite its revolutionary potential, plant disease sensing remains an underdeveloped discipline, with challenges facing both fundamental study and field application. This article offers a perspective on the current state and future of plant disease sensing, highlights remaining gaps to be filled, and presents a bold vision for the future of global agriculture.
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Pane C, Galieni A, Riefolo C, Nicastro N, Castrignanò A. Hyperspectral Reflectance Response of Wild Rocket ( Diplotaxis tenuifolia) Baby-Leaf to Bio-Based Disease Resistance Inducers Using a Linear Mixed Effect Model. PLANTS (BASEL, SWITZERLAND) 2021; 10:plants10122575. [PMID: 34961046 PMCID: PMC8707134 DOI: 10.3390/plants10122575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 11/23/2021] [Accepted: 11/24/2021] [Indexed: 06/14/2023]
Abstract
Baby leaf wild rocket cropping systems feeding the high convenience salad chain are prone to a set of disease agents that require management measures compatible with the sustainability-own features of the ready-to-eat food segment. In this light, bio-based disease resistance inducers able to elicit the plant's defense mechanism(s) against a wide-spectrum of pathogens are proposed as safe and effective remedies as alternatives to synthetic fungicides, to be, however, implemented under practical field applications. Hyperspectral-based proximal sensing was applied here to detect plant reflectance response to treatment of wild rocket beds with Trichoderma atroviride strain TA35, laminarin-based Vacciplant®, and Saccharomyces cerevisiae strain LAS117 cell wall extract-based Romeo®, compared to a local standard approach including synthetic fungicides (i.e., cyprodinil, fludioxonil, mandipropamid, and metalaxyl-m) and a not-treated control. Variability of the spectral information acquired in VIS-NIR-SWIR regions per treatment was explained by three principal components associated with foliar absorption of water, structural characteristics of the vegetation, and the ecophysiological plant status. Therefore, the following model-based statistical approach returned the interpretation of the inducers' performances at field scale consistent with their putative biological effects. The study stated that compost and laminarin-based treatments were the highest crop impacting ones, resulting in enhanced water intake and in stress-related pigment adjustment, respectively. Whereas plants under the conventional chemical management proved to be in better vigor and health status than the untreated control.
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Affiliation(s)
- Catello Pane
- Council for Agricultural Research and Economics (CREA), Research Centre for Vegetable and Ornamental Crops, Via Cavalleggeri 25, 84098 Pontecagnano Faiano, Italy;
| | - Angelica Galieni
- Council for Agricultural Research and Economics (CREA), Research Centre for Vegetable and Ornamental Crops, Via Salaria 1, 63030 Monsampolo del Tronto, Italy;
| | - Carmela Riefolo
- Council for Agricultural Research and Economics (CREA), Research Centre for Agriculture and Environment, Via Celso Ulpiani 5, 70125 Bari, Italy;
| | - Nicola Nicastro
- Council for Agricultural Research and Economics (CREA), Research Centre for Vegetable and Ornamental Crops, Via Cavalleggeri 25, 84098 Pontecagnano Faiano, Italy;
| | - Annamaria Castrignanò
- Department of Engineering and Geology (InGeo), “Gabriele D’Annunzio” University of Chieti-Pescara, Via dei Vestini 31, 66013 Chieti, Italy;
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Zarco-Tejada PJ, Poblete T, Camino C, Gonzalez-Dugo V, Calderon R, Hornero A, Hernandez-Clemente R, Román-Écija M, Velasco-Amo MP, Landa BB, Beck PSA, Saponari M, Boscia D, Navas-Cortes JA. Divergent abiotic spectral pathways unravel pathogen stress signals across species. Nat Commun 2021; 12:6088. [PMID: 34667165 PMCID: PMC8526582 DOI: 10.1038/s41467-021-26335-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 10/01/2021] [Indexed: 11/30/2022] Open
Abstract
Plant pathogens pose increasing threats to global food security, causing yield losses that exceed 30% in food-deficit regions. Xylella fastidiosa (Xf) represents the major transboundary plant pest and one of the world's most damaging pathogens in terms of socioeconomic impact. Spectral screening methods are critical to detect non-visual symptoms of early infection and prevent spread. However, the subtle pathogen-induced physiological alterations that are spectrally detectable are entangled with the dynamics of abiotic stresses. Here, using airborne spectroscopy and thermal scanning of areas covering more than one million trees of different species, infections and water stress levels, we reveal the existence of divergent pathogen- and host-specific spectral pathways that can disentangle biotic-induced symptoms. We demonstrate that uncoupling this biotic-abiotic spectral dynamics diminishes the uncertainty in the Xf detection to below 6% across different hosts. Assessing these deviating pathways against another harmful vascular pathogen that produces analogous symptoms, Verticillium dahliae, the divergent routes remained pathogen- and host-specific, revealing detection accuracies exceeding 92% across pathosystems. These urgently needed hyperspectral methods advance early detection of devastating pathogens to reduce the billions in crop losses worldwide.
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Affiliation(s)
- P J Zarco-Tejada
- School of Agriculture and Food (SAF-FVAS) and Faculty of Engineering and Information Technology (IE-FEIT), University of Melbourne, Melbourne, VIC, Australia.
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Avda. Menéndez Pidal s/n, 14004, Córdoba, Spain.
| | - T Poblete
- School of Agriculture and Food (SAF-FVAS) and Faculty of Engineering and Information Technology (IE-FEIT), University of Melbourne, Melbourne, VIC, Australia
| | - C Camino
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - V Gonzalez-Dugo
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Avda. Menéndez Pidal s/n, 14004, Córdoba, Spain
| | - R Calderon
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell AgriTech, Cornell University, Geneva, NY, USA
| | - A Hornero
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Avda. Menéndez Pidal s/n, 14004, Córdoba, Spain
- Department of Geography, Swansea University, Swansea, SA2 8PP, UK
| | | | - M Román-Écija
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Avda. Menéndez Pidal s/n, 14004, Córdoba, Spain
| | - M P Velasco-Amo
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Avda. Menéndez Pidal s/n, 14004, Córdoba, Spain
| | - B B Landa
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Avda. Menéndez Pidal s/n, 14004, Córdoba, Spain
| | - P S A Beck
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - M Saponari
- CNR, Istituto per la Protezione Sostenibile delle Piante, Bari, Italy
| | - D Boscia
- CNR, Istituto per la Protezione Sostenibile delle Piante, Bari, Italy
| | - J A Navas-Cortes
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Avda. Menéndez Pidal s/n, 14004, Córdoba, Spain
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43
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Nißler R, Müller AT, Dohrman F, Kurth L, Li H, Cosio EG, Flavel BS, Giraldo JP, Mithöfer A, Kruss S. Detection and Imaging of the Plant Pathogen Response by Near-Infrared Fluorescent Polyphenol Sensors. Angew Chem Int Ed Engl 2021; 61:e202108373. [PMID: 34608727 PMCID: PMC9298901 DOI: 10.1002/anie.202108373] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/28/2021] [Indexed: 12/17/2022]
Abstract
Plants use secondary metabolites such as polyphenols for chemical defense against pathogens and herbivores. Despite their importance in plant pathogen interactions and tolerance to diseases, it remains challenging to detect polyphenols in complex plant tissues. Here, we create molecular sensors for plant polyphenol imaging that are based on near-infrared (NIR) fluorescent single-wall carbon nanotubes (SWCNTs). We identified polyethylene glycol-phospholipids that render (6,5)-SWCNTs sensitive (Kd =90 nM) to plant polyphenols (tannins, flavonoids, …), which red-shift (up to 20 nm) and quench their emission (ca. 1000 nm). These sensors report changes in total polyphenol level after herbivore or pathogen challenge in crop plant systems (Soybean Glycine max) and leaf tissue extracts (Tococa spp.). We furthermore demonstrate remote chemical imaging of pathogen-induced polyphenol release from roots of soybean seedlings over the time course of 24 h. This approach allows in situ visualization and understanding of the chemical plant defense in real time and paves the way for plant phenotyping for optimized polyphenol secretion.
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Affiliation(s)
- Robert Nißler
- Physical Chemistry II, Bochum University, Universitätsstrasse 150, 44801, Bochum, Germany.,Institute of Physical Chemistry, Georg-August Universität Göttingen, Tammannstrasse 6, 37077, Göttingen, Germany
| | - Andrea T Müller
- Research Group Plant Defense Physiology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Strasse 8, 07745, Jena, Germany
| | - Frederike Dohrman
- Institute of Physical Chemistry, Georg-August Universität Göttingen, Tammannstrasse 6, 37077, Göttingen, Germany
| | - Larissa Kurth
- Institute of Physical Chemistry, Georg-August Universität Göttingen, Tammannstrasse 6, 37077, Göttingen, Germany
| | - Han Li
- Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), 76344, Eggenstein-Leopoldshafen, Germany
| | - Eric G Cosio
- Institute for Nature Earth and Energy (INTE-PUCP), Pontifical Catholic University of Peru, Av. Universitaria 1801, San Miguel, 15088, Lima, Peru
| | - Benjamin S Flavel
- Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), 76344, Eggenstein-Leopoldshafen, Germany
| | - Juan Pablo Giraldo
- Department of Botany and Plant Sciences, University of California, Riverside, CA, 92507, USA
| | - Axel Mithöfer
- Research Group Plant Defense Physiology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Strasse 8, 07745, Jena, Germany
| | - Sebastian Kruss
- Physical Chemistry II, Bochum University, Universitätsstrasse 150, 44801, Bochum, Germany.,Institute of Physical Chemistry, Georg-August Universität Göttingen, Tammannstrasse 6, 37077, Göttingen, Germany.,Fraunhofer Institute for Microelectronic Circuits and Systems, Finkenstrasse 61, 47057, Duisburg, Germany
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44
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Lassalle G. Monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing: Recommendations and guidelines based on a meta-review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 788:147758. [PMID: 34020093 DOI: 10.1016/j.scitotenv.2021.147758] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/05/2021] [Accepted: 05/10/2021] [Indexed: 06/12/2023]
Abstract
This review outlines the advances achieved in monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing over the last 50 years. A broad diversity of methods based on field and imaging spectroscopy were developed in that field for precision farming and environmental monitoring purposes. From the 466 articles reviewed, we identified the main factors to consider to achieve accurate monitoring of plant stress, namely: The plant species and the stressor to monitor, the goal (detection or quantification), and scale (field or broad-scale) of monitoring, and the need for controlled experiments. Based on these factors, we then provide recommendations and guidelines for the development of reliable methods to monitor 11 major biotic and abiotic plant stressors. For each stressor, the effects on plant health and reflectance are described and the most suited spectral regions, scale, spatial resolution, and processing approaches to achieve accurate monitoring are presented. As a perspective, we discuss two major components that should be implemented in future methods to improve stress monitoring: The discrimination of plant stressors with similar effects on plants and the transferability of the methods across scales.
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Affiliation(s)
- Guillaume Lassalle
- University of Campinas, UNICAMP, PO Box 6152, 13083-855 Campinas, SP, Brazil.
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45
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Pavan S, Vergine M, Nicolì F, Sabella E, Aprile A, Negro C, Fanelli V, Savoia MA, Montilon V, Susca L, Delvento C, Lotti C, Nigro F, Montemurro C, Ricciardi L, De Bellis L, Luvisi A. Screening of Olive Biodiversity Defines Genotypes Potentially Resistant to Xylella fastidiosa. FRONTIERS IN PLANT SCIENCE 2021; 12:723879. [PMID: 34484283 PMCID: PMC8415753 DOI: 10.3389/fpls.2021.723879] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 07/23/2021] [Indexed: 06/12/2023]
Abstract
The recent outbreak of the Olive Quick Decline Syndrome (OQDS), caused by Xylella fastidiosa subsp. pauca (Xf), is dramatically altering ecosystem services in the peninsula of Salento (Apulia Region, southeastern Italy). Here we report the accomplishment of several exploratory missions in the Salento area, resulting in the identification of thirty paucisymptomatic or asymptomatic plants in olive orchards severely affected by the OQDS. The genetic profiles of such putatively resistant plants (PRPs), assessed by a selection of ten simple sequence repeat (SSR) markers, were compared with those of 141 Mediterranean cultivars. Most (23) PRPs formed a genetic cluster (K1) with 22 Italian cultivars, including 'Leccino' and 'FS17', previously reported as resistant to Xf. The remaining PRPs displayed relatedness with genetically differentiated germplasm, including a cluster of Tunisian cultivars. Markedly lower colonization levels were observed in PRPs of the cluster K1 with respect to control plants. Field evaluation of four cultivars related to PRPs allowed the definition of partial resistance in the genotypes 'Frantoio' and 'Nocellara Messinese'. Some of the PRPs identified in this study might be exploited in cultivation, or as parental clones of breeding programs. In addition, our results indicate the possibility to characterize resistance to Xf in cultivars genetically related to PRPs.
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Affiliation(s)
- Stefano Pavan
- Department of Soil, Plant and Food Science, University of Bari “Aldo Moro”, Bari, Italy
| | - Marzia Vergine
- Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy
| | - Francesca Nicolì
- Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy
| | - Erika Sabella
- Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy
| | - Alessio Aprile
- Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy
| | - Carmine Negro
- Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy
| | - Valentina Fanelli
- Department of Soil, Plant and Food Science, University of Bari “Aldo Moro”, Bari, Italy
| | | | - Vito Montilon
- Department of Soil, Plant and Food Science, University of Bari “Aldo Moro”, Bari, Italy
| | - Leonardo Susca
- Department of Soil, Plant and Food Science, University of Bari “Aldo Moro”, Bari, Italy
| | - Chiara Delvento
- Department of Soil, Plant and Food Science, University of Bari “Aldo Moro”, Bari, Italy
| | - Concetta Lotti
- Department of Agriculture, Food, Natural Resources and Engineering, University of Foggia, Foggia, Italy
| | - Franco Nigro
- Department of Soil, Plant and Food Science, University of Bari “Aldo Moro”, Bari, Italy
| | - Cinzia Montemurro
- Department of Soil, Plant and Food Science, University of Bari “Aldo Moro”, Bari, Italy
| | - Luigi Ricciardi
- Department of Soil, Plant and Food Science, University of Bari “Aldo Moro”, Bari, Italy
| | - Luigi De Bellis
- Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy
| | - Andrea Luvisi
- Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy
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46
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Porcar-Castell A, Malenovský Z, Magney T, Van Wittenberghe S, Fernández-Marín B, Maignan F, Zhang Y, Maseyk K, Atherton J, Albert LP, Robson TM, Zhao F, Garcia-Plazaola JI, Ensminger I, Rajewicz PA, Grebe S, Tikkanen M, Kellner JR, Ihalainen JA, Rascher U, Logan B. Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science. NATURE PLANTS 2021; 7:998-1009. [PMID: 34373605 DOI: 10.1038/s41477-021-00980-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 06/28/2021] [Indexed: 05/27/2023]
Abstract
For decades, the dynamic nature of chlorophyll a fluorescence (ChlaF) has provided insight into the biophysics and ecophysiology of the light reactions of photosynthesis from the subcellular to leaf scales. Recent advances in remote sensing methods enable detection of ChlaF induced by sunlight across a range of larger scales, from using instruments mounted on towers above plant canopies to Earth-orbiting satellites. This signal is referred to as solar-induced fluorescence (SIF) and its application promises to overcome spatial constraints on studies of photosynthesis, opening new research directions and opportunities in ecology, ecophysiology, biogeochemistry, agriculture and forestry. However, to unleash the full potential of SIF, intensive cross-disciplinary work is required to harmonize these new advances with the rich history of biophysical and ecophysiological studies of ChlaF, fostering the development of next-generation plant physiological and Earth-system models. Here, we introduce the scale-dependent link between SIF and photosynthesis, with an emphasis on seven remaining scientific challenges, and present a roadmap to facilitate future collaborative research towards new applications of SIF.
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Affiliation(s)
- Albert Porcar-Castell
- Optics of Photosynthesis Laboratory, Institute for Atmospheric and Earth System Research (INAR)/Forest Sciences, Viikki Plant Science Center (ViPS), University of Helsinki, Helsinki, Finland.
| | - Zbyněk Malenovský
- School of Geography, Planning, and Spatial Sciences, College of Sciences Engineering and Technology, University of Tasmania, Hobart, Tasmania, Australia
| | - Troy Magney
- Department of Plant Sciences, University of California, Davis, Davis, CA, USA
| | - Shari Van Wittenberghe
- Optics of Photosynthesis Laboratory, Institute for Atmospheric and Earth System Research (INAR)/Forest Sciences, Viikki Plant Science Center (ViPS), University of Helsinki, Helsinki, Finland
- Laboratory of Earth Observation, University of Valencia, Paterna, Spain
| | - Beatriz Fernández-Marín
- Department of Botany, Ecology and Plant Physiology, University of La Laguna (ULL), Tenerife, Spain
| | - Fabienne Maignan
- Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Yongguang Zhang
- International Institute for Earth System Sciences, Nanjing University, Nanjing, China
| | - Kadmiel Maseyk
- School of Environment, Earth and Ecosystem Sciences, The Open University, Milton Keynes, UK
| | - Jon Atherton
- Optics of Photosynthesis Laboratory, Institute for Atmospheric and Earth System Research (INAR)/Forest Sciences, Viikki Plant Science Center (ViPS), University of Helsinki, Helsinki, Finland
| | - Loren P Albert
- Institute at Brown for Environment and Society, Brown University, Providence, RI, USA
- Biology Department, West Virginia University, Morgantown, WV, USA
| | - Thomas Matthew Robson
- Organismal and Evolutionary Biology, Viikki Plant Science Centre (ViPS), Faculty of Biological and Environmental Science, University of Helsinki, Helsinki, Finland
| | - Feng Zhao
- School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing, China
| | | | - Ingo Ensminger
- Department of Biology, Graduate Programs in Cell & Systems Biology and Ecology & Evolutionary Biology, University of Toronto, Mississauga, Ontario, Canada
| | - Paulina A Rajewicz
- Optics of Photosynthesis Laboratory, Institute for Atmospheric and Earth System Research (INAR)/Forest Sciences, Viikki Plant Science Center (ViPS), University of Helsinki, Helsinki, Finland
| | - Steffen Grebe
- Molecular Plant Biology, University of Turku, Turku, Finland
| | - Mikko Tikkanen
- Molecular Plant Biology, University of Turku, Turku, Finland
| | - James R Kellner
- Institute at Brown for Environment and Society, Brown University, Providence, RI, USA
- Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, USA
| | - Janne A Ihalainen
- Nanoscience Center, Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland
| | - Uwe Rascher
- Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Barry Logan
- Biology Department, Bowdoin College, Brunswick, ME, USA
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47
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Identifying Optimal Wavelengths as Disease Signatures Using Hyperspectral Sensor and Machine Learning. REMOTE SENSING 2021. [DOI: 10.3390/rs13142833] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Hyperspectral sensors combined with machine learning are increasingly utilized in agricultural crop systems for diverse applications, including plant disease detection. This study was designed to identify the most important wavelengths to discriminate between healthy and diseased peanut (Arachis hypogaea L.) plants infected with Athelia rolfsii, the causal agent of peanut stem rot, using in-situ spectroscopy and machine learning. In greenhouse experiments, daily measurements were conducted to inspect disease symptoms visually and to collect spectral reflectance of peanut leaves on lateral stems of plants mock-inoculated and inoculated with A. rolfsii. Spectrum files were categorized into five classes based on foliar wilting symptoms. Five feature selection methods were compared to select the top 10 ranked wavelengths with and without a custom minimum distance of 20 nm. Recursive feature elimination methods outperformed the chi-square and SelectFromModel methods. Adding the minimum distance of 20 nm into the top selected wavelengths improved classification performance. Wavelengths of 501–505, 690–694, 763 and 884 nm were repeatedly selected by two or more feature selection methods. These selected wavelengths can be applied in designing optical sensors for automated stem rot detection in peanut fields. The machine-learning-based methodology can be adapted to identify spectral signatures of disease in other plant-pathogen systems.
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48
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Camino C, Calderón R, Parnell S, Dierkes H, Chemin Y, Román-Écija M, Montes-Borrego M, Landa B, Navas-Cortes J, Zarco-Tejada P, Beck P. Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits. REMOTE SENSING OF ENVIRONMENT 2021; 260:112420. [PMID: 34219817 PMCID: PMC8169955 DOI: 10.1016/j.rse.2021.112420] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 03/28/2021] [Accepted: 03/29/2021] [Indexed: 05/29/2023]
Abstract
The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf-infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf (n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400-850 nm) and short-wave infrared regions (SWIR, 950-1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (Tc), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR (n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64-65% and kappa = 0.26-31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution.
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Affiliation(s)
- C. Camino
- European Commission (EC), Joint Research Centre (JRC), Ispra, Italy
| | - R. Calderón
- School of Environment and Life Sciences, University of Salford, Manchester, United Kingdom
| | - S. Parnell
- School of Environment and Life Sciences, University of Salford, Manchester, United Kingdom
| | - H. Dierkes
- European Commission (EC), Joint Research Centre (JRC), Ispra, Italy
| | - Y. Chemin
- European Commission (EC), Joint Research Centre (JRC), Ispra, Italy
| | - M. Román-Écija
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Cordoba, Spain
| | - M. Montes-Borrego
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Cordoba, Spain
| | - B.B. Landa
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Cordoba, Spain
| | - J.A. Navas-Cortes
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Cordoba, Spain
| | - P.J. Zarco-Tejada
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Cordoba, Spain
- School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences (FVAS), and Department of Infrastructure Engineering, Faculty of Engineering and Information Technology (FEIT), University of Melbourne, Melbourne, Victoria, Australia
| | - P.S.A. Beck
- European Commission (EC), Joint Research Centre (JRC), Ispra, Italy
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49
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Silva G, Tomlinson J, Onkokesung N, Sommer S, Mrisho L, Legg J, Adams IP, Gutierrez-Vazquez Y, Howard TP, Laverick A, Hossain O, Wei Q, Gold KM, Boonham N. Plant pest surveillance: from satellites to molecules. Emerg Top Life Sci 2021; 5:275-287. [PMID: 33720345 PMCID: PMC8166340 DOI: 10.1042/etls20200300] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 02/19/2021] [Accepted: 02/22/2021] [Indexed: 11/18/2022]
Abstract
Plant pests and diseases impact both food security and natural ecosystems, and the impact has been accelerated in recent years due to several confounding factors. The globalisation of trade has moved pests out of natural ranges, creating damaging epidemics in new regions. Climate change has extended the range of pests and the pathogens they vector. Resistance to agrochemicals has made pathogens, pests, and weeds more difficult to control. Early detection is critical to achieve effective control, both from a biosecurity as well as an endemic pest perspective. Molecular diagnostics has revolutionised our ability to identify pests and diseases over the past two decades, but more recent technological innovations are enabling us to achieve better pest surveillance. In this review, we will explore the different technologies that are enabling this advancing capability and discuss the drivers that will shape its future deployment.
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Affiliation(s)
- Gonçalo Silva
- Natural Resources Institute, University of Greenwich, Central Avenue, Chatham Maritime, Kent ME4 4TB, U.K
| | - Jenny Tomlinson
- Fera Science Ltd., York Biotech Campus, Sand Hutton, York YO41 1LZ, U.K
| | - Nawaporn Onkokesung
- School of Natural and Environmental Sciences, Agriculture Building, Newcastle University, King's Road, Newcastle upon Tyne NE1 7RU, U.K
| | - Sarah Sommer
- School of Natural and Environmental Sciences, Agriculture Building, Newcastle University, King's Road, Newcastle upon Tyne NE1 7RU, U.K
| | - Latifa Mrisho
- International Institute of Tropical Agriculture, Dar el Salaam, Tanzania
| | - James Legg
- International Institute of Tropical Agriculture, Dar el Salaam, Tanzania
| | - Ian P Adams
- Fera Science Ltd., York Biotech Campus, Sand Hutton, York YO41 1LZ, U.K
| | | | - Thomas P Howard
- School of Natural and Environmental Sciences, Agriculture Building, Newcastle University, King's Road, Newcastle upon Tyne NE1 7RU, U.K
| | - Alex Laverick
- School of Natural and Environmental Sciences, Agriculture Building, Newcastle University, King's Road, Newcastle upon Tyne NE1 7RU, U.K
| | - Oindrila Hossain
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, U.S.A
| | - Qingshan Wei
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, U.S.A
| | - Kaitlin M Gold
- Plant Pathology and Plant Microbe Biology Section, Cornell University, 15 Castle Creek Drive, Geneva, NY 14456, U.S.A
| | - Neil Boonham
- School of Natural and Environmental Sciences, Agriculture Building, Newcastle University, King's Road, Newcastle upon Tyne NE1 7RU, U.K
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50
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Predicting Water Stress in Wild Blueberry Fields Using Airborne Visible and Near Infrared Imaging Spectroscopy. REMOTE SENSING 2021. [DOI: 10.3390/rs13081425] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Water management and irrigation practices are persistent challenges for many agricultural systems, exacerbated by changing seasonal and weather patterns. The wild blueberry industry is at heightened susceptibility due to its unique growing conditions and uncultivated nature. Stress detection in agricultural fields can prompt management responses to mitigate detrimental conditions, including drought and disease. We assessed airborne spectral data accompanied by ground sampled water potential over three developmental stages of wild blueberries collected throughout the 2019 summer on two adjacent fields, one irrigated and one non-irrigated. Ground sampled leaves were collected in tandem to the hyperspectral image collection with an unoccupied aerial vehicle (UAV) and then measured for leaf water potential. Using methods in machine learning and statistical analysis, we developed models to determine irrigation status and water potential. Seven models were assessed in this study, with four used to process six hyperspectral cube images for analysis. These images were classified as irrigated or non-irrigated and estimated for water potential levels, resulting in an R2 of 0.62 and verified with a validation dataset. Further investigation relating imaging spectroscopy and water potential will be beneficial in understanding the dynamics between the two for future studies.
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