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Zhang Z, Qu Y, Ma F, Lv Q, Zhu X, Guo G, Li M, Yang W, Que B, Zhang Y, He T, Qiu X, Deng H, Song J, Liu Q, Wang B, Ke Y, Bai S, Li J, Lv L, Li R, Wang K, Li H, Feng H, Huang J, Yang W, Zhou Y, Song CP. Integrating high-throughput phenotyping and genome-wide association studies for enhanced drought resistance and yield prediction in wheat. THE NEW PHYTOLOGIST 2024; 243:1758-1775. [PMID: 38992951 DOI: 10.1111/nph.19942] [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: 01/21/2024] [Accepted: 04/19/2024] [Indexed: 07/13/2024]
Abstract
Drought, especially terminal drought, severely limits wheat growth and yield. Understanding the complex mechanisms behind the drought response in wheat is essential for developing drought-resistant varieties. This study aimed to dissect the genetic architecture and high-yielding wheat ideotypes under terminal drought. An automated high-throughput phenotyping platform was used to examine 28 392 image-based digital traits (i-traits) under different drought conditions during the flowering stage of a natural wheat population. Of the i-traits examined, 17 073 were identified as drought-related. A genome-wide association study (GWAS) identified 5320 drought-related significant single-nucleotide polymorphisms (SNPs) and 27 SNP clusters. A notable hotspot region controlling wheat drought tolerance was discovered, in which TaPP2C6 was shown to be an important negative regulator of the drought response. The tapp2c6 knockout lines exhibited enhanced drought resistance without a yield penalty. A haplotype analysis revealed a favored allele of TaPP2C6 that was significantly correlated with drought resistance, affirming its potential value in wheat breeding programs. We developed an advanced prediction model for wheat yield and drought resistance using 24 i-traits analyzed by machine learning. In summary, this study provides comprehensive insights into the high-yielding ideotype and an approach for the rapid breeding of drought-resistant wheat.
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Affiliation(s)
- Zhen Zhang
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Yunfeng Qu
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Feifei Ma
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Qian Lv
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Xiaojing Zhu
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Guanghui Guo
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Mengmeng Li
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Wei Yang
- School of Computer and Information Engineering, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Beibei Que
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Yun Zhang
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Tiantian He
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Xiaolong Qiu
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Hui Deng
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Jingyan Song
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Qian Liu
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Baoqi Wang
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Youlong Ke
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Shenglong Bai
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Jingyao Li
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Linlin Lv
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Ranzhe Li
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Kai Wang
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Hao Li
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jinling Huang
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
- Department of Biology, East Carolina University, Greenville, NC, 27858, USA
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yun Zhou
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
- Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng, 475004, China
| | - Chun-Peng Song
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
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Mahlein AK, Arnal Barbedo JG, Chiang KS, Del Ponte EM, Bock CH. From Detection to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Modern Plant Disease Management. PHYTOPATHOLOGY 2024:PHYTO01240009PER. [PMID: 38810274 DOI: 10.1094/phyto-01-24-0009-per] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
In the past decade, there has been a recognized need for innovative methods to monitor and manage plant diseases, aiming to meet the precision demands of modern agriculture. Over the last 15 years, significant advances in the detection, monitoring, and management of plant diseases have been made, largely propelled by cutting-edge technologies. Recent advances in precision agriculture have been driven by sophisticated tools such as optical sensors, artificial intelligence, microsensor networks, and autonomous driving vehicles. These technologies have enabled the development of novel cropping systems, allowing for targeted management of crops, contrasting with the traditional, homogeneous treatment of large crop areas. The research in this field is usually a highly collaborative and interdisciplinary endeavor. It brings together experts from diverse fields such as plant pathology, computer science, statistics, engineering, and agronomy to forge comprehensive solutions. Despite the progress, translating the advancements in the precision of decision-making or automation into agricultural practice remains a challenge. The knowledge transfer to agricultural practice and extension has been particularly challenging. Enhancing the accuracy and timeliness of disease detection continues to be a priority, with data-driven artificial intelligence systems poised to play a pivotal role. This perspective article addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management. It underscores the urgency of integrating innovative technological advances with traditional integrated pest management. It highlights unresolved issues regarding the establishment of control thresholds for site-specific treatments and the necessary alignment of digital technology use with regulatory frameworks. Importantly, the paper calls for intensified research efforts, widespread knowledge dissemination, and education to optimize the application of digital tools for plant disease management, recognizing the intersection of technology's potential with its current practical limitations.
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Affiliation(s)
- Anne-Katrin Mahlein
- Institute of Sugar Beet Research (IfZ), Holtenser Landstrasse 77 37079 Göttingen, Germany
| | | | - Kuo-Szu Chiang
- Division of Biometrics, Department of Agronomy, National Chung Hsing University, Taichung, Taiwan
| | - Emerson M Del Ponte
- Departamento de Fitopatologia, Universidade Federal de Viçosa, Viçosa, MG 36570-000, Brazil
| | - Clive H Bock
- U.S. Department of Agriculture-Agricultural Research Service Southeastern Fruit and Tree Nut Research Station, Byron, GA 31008, U.S.A
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Krishnamoorthi S, Tan GZH, Dong Y, Leong R, Wu TY, Urano D. Hyperspectral imaging of liverwort Marchantia polymorpha identifies MpWRKY10 as a key regulator defining Foliar pigmentation patterns. Cell Rep 2024; 43:114463. [PMID: 38985675 DOI: 10.1016/j.celrep.2024.114463] [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: 10/16/2023] [Revised: 05/10/2024] [Accepted: 06/24/2024] [Indexed: 07/12/2024] Open
Abstract
Foliar pigmentation patterns vary among plant species and growth conditions. In this study, we utilize hyperspectral imaging to assess foliar pigmentation in the bryophyte Marchantia polymorpha under nutrient stress and identify associated genetic factors. Using singular value decomposition (SVD) for feature selection, we quantitate color variations induced by deficiencies in phosphate, nitrate, magnesium, calcium, and iron. Pseudo-colored thallus images show that disrupting MpWRKY10 causes irregular pigmentation with auronidin accumulation. Transcriptomic profiling shows that MpWRKY10 regulates phenylpropanoid pathway enzymes and R2R3-MYB transcription factors during phosphate deficiency, with MpMYB14 upregulation preceding pigment accumulation. MpWRKY10 is downregulated in older, pigmented thalli under phosphate deficiency but maintained in young thalli, where it suppresses pigmentation genes. This downregulation is absent in pigmented thalli due to aging. Comparative transcriptome analysis suggests similar WRKY and MYB roles in nutrient response and pigmentation in red-leaf lettuce, alluding to conserved genetic factors controlling foliar pigmentation patterns under nutrient deficiency.
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Affiliation(s)
| | | | - Yating Dong
- Temasek Life Sciences Laboratory, Singapore 117604, Singapore
| | - Richalynn Leong
- Temasek Life Sciences Laboratory, Singapore 117604, Singapore; Department of Biological Sciences, National University of Singapore, Singapore 117558, Singapore
| | - Ting-Ying Wu
- Temasek Life Sciences Laboratory, Singapore 117604, Singapore
| | - Daisuke Urano
- Temasek Life Sciences Laboratory, Singapore 117604, Singapore; Department of Biological Sciences, National University of Singapore, Singapore 117558, Singapore.
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4
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Santana DC, Otone JDDQ, Baio FHR, Teodoro LPR, Alves MEM, Junior CADS, Teodoro PE. Machine learning in the classification of asian rust severity in soybean using hyperspectral sensor. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 313:124113. [PMID: 38447444 DOI: 10.1016/j.saa.2024.124113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 02/26/2024] [Accepted: 03/02/2024] [Indexed: 03/08/2024]
Abstract
Traditional monitoring of asian soybean rust severity is a time- and labor-intensive task, as it requires visual assessments by skilled professionals in the field. Thus, the use of remote sensing and machine learning (ML) techniques in data processing has emerged as an approach that can increase efficiency in disease monitoring, enabling faster, more accurate and time- and labor-saving evaluations. The aims of the study were: (i) to identify the spectral signature of different levels of Asian soybean rust severity; (ii) to identify the most accurate machine learning algorithm for classifying disease severity levels; (iii) which spectral input provides the highest classification accuracy for the algorithms; (iv) to determine a sample size of leaves that guarantees the best accuracy for the algorithms. A field experiment was carried out in the 2022/2023 harvest in a randomized block design with a 6x3 factorial scheme (ML algorithms x severity levels) and four replications. Disease severity levels assessed were: healthy leaves, 25 % severity, and 50 % severity. Leaf hyperspectral analysis was carried out over a wide range from 350 to 2500 nm. From this analysis, 28 spectral bands were extracted, seeking to distinguish the spectral signature for each severity level with the least input dataset. Data was subjected to machine learning analysis using Artificial Neural Network (ANN), REPTree (DT) and J48 decision trees, Random Forest (RF), and Support Vector Machine (SVM) algorithms, as well as a traditional classification method (Logistic Regression - LR). Two different input datasets were tested for each algorithm: the full spectrum (ALL) provided by the sensor and the 28 spectral bands (SB). Tests with different sample sizes were also conducted to investigate the algorithms' ability to detect severity levels with a reduced sample size. Our findings indicate differences between the spectral curves for the severity levels assessed, which makes it possible to differentiate between healthy plants with low and high severity using hyperspectral sensing. SVM was the most accurate algorithm for classifying severity levels by using all the spectral information as input. This algorithm also provided high classification accuracy when using smaller leaf samples. This study reveals that hyperspectral sensing and the use of ML algorithms provide an accurate classification of different levels of Asian rust severity, and can be powerful tools for a more efficient disease monitoring process.
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Affiliation(s)
| | | | | | | | | | | | - Paulo Eduardo Teodoro
- Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul, 79560-000, MS, Brazil.
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Matese A, Prince Czarnecki JM, Samiappan S, Moorhead R. Are unmanned aerial vehicle-based hyperspectral imaging and machine learning advancing crop science? TRENDS IN PLANT SCIENCE 2024; 29:196-209. [PMID: 37802693 DOI: 10.1016/j.tplants.2023.09.001] [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] [Received: 10/13/2022] [Revised: 08/07/2023] [Accepted: 09/05/2023] [Indexed: 10/08/2023]
Abstract
The past few years have seen increased interest in unmanned aerial vehicle (UAV)-based hyperspectral imaging (HSI) and machine learning (ML) in agricultural research, concomitant with an increase in published research on these topics. We provide an updated review, written for agriculturalists, highlighting the benefits in the retrieval of biophysical parameters of crops via UAVs relative to less sophisticated options. We reviewed >70 recent papers and found few consistent results between similar studies. Owing to their high complexity and cost, especially when applied to crops of low value, the benefits of most of the research reviewed are difficult to explain. Future effort will be necessary to distill research findings into lower-cost options for end-users.
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Affiliation(s)
- Alessandro Matese
- Geosystems Research Institute, Mississippi State University, Box 9627, Starkville, MS, USA; Institute of BioEconomy, National Research Council (CNR-IBE), Via Caproni 8, 50145 Florence, Italy.
| | | | - Sathishkumar Samiappan
- Geosystems Research Institute, Mississippi State University, Box 9627, Starkville, MS, USA
| | - Robert Moorhead
- Geosystems Research Institute, Mississippi State University, Box 9627, Starkville, MS, USA
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Santos MG, Nunes da Silva M, Vasconcelos MW, Carvalho SMP. Scientific and technological advances in the development of sustainable disease management tools: a case study on kiwifruit bacterial canker. FRONTIERS IN PLANT SCIENCE 2024; 14:1306420. [PMID: 38273947 PMCID: PMC10808555 DOI: 10.3389/fpls.2023.1306420] [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: 10/03/2023] [Accepted: 12/21/2023] [Indexed: 01/27/2024]
Abstract
Plant disease outbreaks are increasing in a world facing climate change and globalized markets, representing a serious threat to food security. Kiwifruit Bacterial Canker (KBC), caused by the bacterium Pseudomonas syringae pv. actinidiae (Psa), was selected as a case study for being an example of a pandemic disease that severely impacted crop production, leading to huge economic losses, and for the effort that has been made to control this disease. This review provides an in-depth and critical analysis on the scientific progress made for developing alternative tools for sustainable KBC management. Their status in terms of technological maturity is discussed and a set of opportunities and threats are also presented. The gradual replacement of susceptible kiwifruit cultivars, with more tolerant ones, significantly reduced KBC incidence and was a major milestone for Psa containment - which highlights the importance of plant breeding. Nonetheless, this is a very laborious process. Moreover, the potential threat of Psa evolving to more virulent biovars, or resistant lineages to existing control methods, strengthens the need of keep on exploring effective and more environmentally friendly tools for KBC management. Currently, plant elicitors and beneficial fungi and bacteria are already being used in the field with some degree of success. Precision agriculture technologies, for improving early disease detection and preventing pathogen dispersal, are also being developed and optimized. These include hyperspectral technologies and forecast models for Psa risk assessment, with the latter being slightly more advanced in terms of technological maturity. Additionally, plant protection products based on innovative formulations with molecules with antibacterial activity against Psa (e.g., essential oils, phages and antimicrobial peptides) have been validated primarily in laboratory trials and with few compounds already reaching field application. The lessons learned with this pandemic disease, and the acquired scientific and technological knowledge, can be of importance for sustainably managing other plant diseases and handling future pandemic outbreaks.
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Affiliation(s)
- Miguel G. Santos
- GreenUPorto—Sustainable Agrifood Production Research Centre/Inov4Agro, DGAOT, Faculty of Sciences of the University of Porto, Vairão, Portugal
| | - Marta Nunes da Silva
- GreenUPorto—Sustainable Agrifood Production Research Centre/Inov4Agro, DGAOT, Faculty of Sciences of the University of Porto, Vairão, Portugal
- Universidade Católica Portuguesa, CBQF – Centro de Biotecnologia e Química Fina – Laboratório Associado, Escola Superior de Biotecnologia, Porto, Portugal
| | - Marta W. Vasconcelos
- Universidade Católica Portuguesa, CBQF – Centro de Biotecnologia e Química Fina – Laboratório Associado, Escola Superior de Biotecnologia, Porto, Portugal
| | - Susana M. P. Carvalho
- GreenUPorto—Sustainable Agrifood Production Research Centre/Inov4Agro, DGAOT, Faculty of Sciences of the University of Porto, Vairão, Portugal
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Li R, Chen S, Matsumoto H, Gouda M, Gafforov Y, Wang M, Liu Y. Predicting rice diseases using advanced technologies at different scales: present status and future perspectives. ABIOTECH 2023; 4:359-371. [PMID: 38106429 PMCID: PMC10721578 DOI: 10.1007/s42994-023-00126-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 10/30/2023] [Indexed: 12/19/2023]
Abstract
The past few years have witnessed significant progress in emerging disease detection techniques for accurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focus on image processing techniques using machine learning (ML) and deep learning (DL) models related to multi-scale rice diseases. Furthermore, we summarize applications of different detection techniques, including genomic, physiological, and biochemical approaches. In addition, we also present the state-of-the-art in contemporary optical sensing applications of pathogen-plant interaction phenotypes. This review serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice crops through ML and DL models.
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Affiliation(s)
- Ruyue Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058 China
| | - Sishi Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
| | - Haruna Matsumoto
- State Key Laboratory of Rice Biology, and Ministry of Agricultural and Rural Affairs Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Hangzhou, 310058 China
| | - Mostafa Gouda
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Department of Nutrition and Food Science, National Research Centre, Giza, 12622 Egypt
| | - Yusufjon Gafforov
- Central Asian Center for Development Studies, New Uzbekistan University, Tashkent, 100000 Uzbekistan
| | - Mengcen Wang
- State Key Laboratory of Rice Biology, and Ministry of Agricultural and Rural Affairs Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Hangzhou, 310058 China
- Global Education Program for AgriScience Frontiers, Graduate School of Agriculture, Hokkaido University, Sapporo, 060-8589 Japan
| | - Yufei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
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Liu F, Yang R, Chen R, Lamine Guindo M, He Y, Zhou J, Lu X, Chen M, Yang Y, Kong W. Digital techniques and trends for seed phenotyping using optical sensors. J Adv Res 2023:S2090-1232(23)00347-8. [PMID: 37956859 DOI: 10.1016/j.jare.2023.11.010] [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: 11/09/2022] [Revised: 10/19/2023] [Accepted: 11/10/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND The breeding of high-quality, high-yield, and disease-resistant varieties is closely related to food security. The investigation of breeding results relies on the evaluation of seed phenotype, which is a key step in the process of breeding. In the global digitalization trend, digital technology based on optical sensors can perform the digitization of seed phenotype in a non-contact, high throughput way, thus significantly improving breeding efficiency. AIM OF REVIEW This paper provides a comprehensive overview of the principles, characteristics, data processing methods, and bottlenecks associated with three digital technique types based on optical sensors: spectroscopy, digital imaging, and three-dimensional (3D) reconstruction techniques. In addition, the applicability and adaptability of digital techniques based on the optical sensors of maize seed phenotype traits, namely external visible phenotype (EVP) and internal invisible phenotype (IIP), are investigated. Furthermore, trends in future equipment, platform, phenotype data, and processing algorithms are discussed. This review offers conceptual and practical support for seed phenotype digitization based on optical sensors, which will provide reference and guidance for future research. KEY SCIENTIFIC CONCEPTS OF REVIEW The digital techniques based on optical sensors can perform non-contact and high-throughput seed phenotype evaluation. Due to the distinct characteristics of optical sensors, matching suitable digital techniques according to seed phenotype traits can greatly reduce resource loss, and promote the efficiency of seed evaluation as well as breeding decision-making. Future research in phenotype equipment and platform, phenotype data, and processing algorithms will make digital techniques better meet the demands of seed phenotype evaluation, and promote automatic, integrated, and intelligent evaluation of seed phenotype, further helping to lessen the gap between digital techniques and seed phenotyping.
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Affiliation(s)
- Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Rui Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Mahamed Lamine Guindo
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Jun Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
| | - Xiangyu Lu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Mengyuan Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Yinhui Yang
- College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
| | - Wenwen Kong
- College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China.
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9
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Ahmed E, Musio B, Todisco S, Mastrorilli P, Gallo V, Saponari M, Nigro F, Gualano S, Santoro F. Non-Targeted Spectranomics for the Early Detection of Xylella fastidiosa Infection in Asymptomatic Olive Trees, cv. Cellina di Nardò. Molecules 2023; 28:7512. [PMID: 38005234 PMCID: PMC10672767 DOI: 10.3390/molecules28227512] [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: 09/16/2023] [Revised: 10/31/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
Olive quick decline syndrome (OQDS) is a disease that has been seriously affecting olive trees in southern Italy since around 2009. During the disease, caused by Xylella fastidiosa subsp. pauca sequence type ST53 (Xf), the flow of water and nutrients within the trees is significantly compromised. Initially, infected trees may not show any symptoms, making early detection challenging. In this study, young artificially infected plants of the susceptible cultivar Cellina di Nardò were grown in a controlled environment and co-inoculated with additional xylem-inhabiting fungi. Asymptomatic leaves of olive plants at an early stage of infection were collected and analyzed using nuclear magnetic resonance (NMR), hyperspectral reflectance (HSR), and chemometrics. The application of a spectranomic approach contributed to shedding light on the relationship between the presence of specific hydrosoluble metabolites and the optical properties of both asymptomatic Xf-infected and non-infected olive leaves. Significant correlations between wavebands located in the range of 530-560 nm and 1380-1470 nm, and the following metabolites were found to be indicative of Xf infection: malic acid, fructose, sucrose, oleuropein derivatives, and formic acid. This information is the key to the development of HSR-based sensors capable of early detection of Xf infections in olive trees.
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Affiliation(s)
- Elhussein Ahmed
- Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Via Orabona, 4, I-70125 Bari, Italy; (E.A.); (S.T.); (P.M.); (V.G.)
- International Centre for Advanced Mediterranean Agronomic Studies of Bari (CIHEAM Bari), Via Ceglie 9, 70010 Valenzano, Italy;
| | - Biagia Musio
- Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Via Orabona, 4, I-70125 Bari, Italy; (E.A.); (S.T.); (P.M.); (V.G.)
| | - Stefano Todisco
- Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Via Orabona, 4, I-70125 Bari, Italy; (E.A.); (S.T.); (P.M.); (V.G.)
| | - Piero Mastrorilli
- Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Via Orabona, 4, I-70125 Bari, Italy; (E.A.); (S.T.); (P.M.); (V.G.)
- Innovative Solutions S.r.l.—Spin-Off Company of Polytechnic University of Bari, Zona H 150/B, 70015 Noci, Italy
| | - Vito Gallo
- Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Via Orabona, 4, I-70125 Bari, Italy; (E.A.); (S.T.); (P.M.); (V.G.)
- Innovative Solutions S.r.l.—Spin-Off Company of Polytechnic University of Bari, Zona H 150/B, 70015 Noci, Italy
| | - Maria Saponari
- Istituto Per la Protezione Sostenibile Delle Piante, CNR, Via Amendola 122/D, I-70126 Bari, Italy;
| | - Franco Nigro
- Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro, Via Orabona, 4, I-70125 Bari, Italy;
| | - Stefania Gualano
- International Centre for Advanced Mediterranean Agronomic Studies of Bari (CIHEAM Bari), Via Ceglie 9, 70010 Valenzano, Italy;
| | - Franco Santoro
- International Centre for Advanced Mediterranean Agronomic Studies of Bari (CIHEAM Bari), Via Ceglie 9, 70010 Valenzano, Italy;
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10
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Ling Y, Zhao Q, Liu W, Wei K, Bao R, Song W, Nie X. Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley. PLANT METHODS 2023; 19:115. [PMID: 37891590 PMCID: PMC10604417 DOI: 10.1186/s13007-023-01096-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023]
Abstract
BACKGROUND Spike is the grain-bearing organ in cereal crops, which is a key proxy indicator determining the grain yield and quality. Machine learning methods for image analysis of spike-related phenotypic traits not only hold the promise for high-throughput estimating grain production and quality, but also lay the foundation for better dissection of the genetic basis for spike development. Barley (Hordeum vulgare L.) is one of the most important crops globally, ranking as the fourth largest cereal crop in terms of cultivated area and total yield. However, image analysis of spike-related traits in barley, especially based on CT-scanning, remains elusive at present. RESULTS In this study, we developed a non-invasive, high-throughput approach to quantitatively measuring the multitude of spike architectural traits in barley through combining X-ray computed tomography (CT) and a deep learning model (UNet). Firstly, the spikes of 11 barley accessions, including 2 wild barley, 3 landraces and 6 cultivars were used for X-ray CT scanning to obtain the tomographic images. And then, an optimized 3D image processing method was used to point cloud data to generate the 3D point cloud images of spike, namely 'virtual' spike, which is then used to investigate internal structures and morphological traits of barley spikes. Furthermore, the virtual spike-related traits, such as spike length, grain number per spike, grain volume, grain surface area, grain length and grain width as well as grain thickness were efficiently and non-destructively quantified. The virtual values of these traits were highly consistent with the actual value using manual measurement, demonstrating the accuracy and reliability of the developed model. The reconstruction process took 15 min approximately, 10 min for CT scanning and 5 min for imaging and features extraction, respectively. CONCLUSIONS This study provides an efficient, non-invasive and useful tool for dissecting barley spike architecture, which will contribute to high-throughput phenotyping and breeding for high yield in barley and other crops.
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Affiliation(s)
- Yimin Ling
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Qinlong Zhao
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Wenxin Liu
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Kexu Wei
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Runfei Bao
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Weining Song
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China
- ICARDA-NWSUAF Joint Research Centre, Northwest A&F University, Yangling, 712100, Shaanxi, China
| | - Xiaojun Nie
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China.
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11
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Rieker MEG, Lutz MA, El-Hasan A, Thomas S, Voegele RT. Hyperspectral Imaging and Selected Biological Control Agents for the Management of Fusarium Head Blight in Spring Wheat. PLANTS (BASEL, SWITZERLAND) 2023; 12:3534. [PMID: 37895997 PMCID: PMC10610361 DOI: 10.3390/plants12203534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/06/2023] [Accepted: 10/08/2023] [Indexed: 10/29/2023]
Abstract
Fusarium spp. are important pathogens on cereals, capable of causing considerable yield losses and significantly reducing the quality of harvested grains due to contamination with mycotoxins. The European Union intends to reduce the use of chemical-synthetic plant protection products (csPPP) by up to 50% by the year 2030. To realize this endeavor without significant economic losses for farmers, it is crucial to have both precise early detection of pathogens and effective alternatives for csPPP. To investigate both the early detection of Fusarium head blight (FHB) and the efficacy of selected biological control agents (BCAs), a pot experiment with spring wheat (cv. 'Servus') was conducted under semi-field conditions. Spikes were sprayed with different BCAs prior to inoculation with a mixture of F. graminearum and F. culmorum conidia. While early detection of FHB was investigated by hyperspectral imaging (HSI), the efficiency of the fungal (Trichoderma sp. T10, T. harzianum T16, T. asperellum T23 and Clonostachys rosea CRP1104) and bacterial (Bacillus subtilis HG77 and Pseudomonas fluorescens G308) BCAs was assessed by visual monitoring. Evaluation of the hyperspectral images using linear discriminant analysis (LDA) resulted in a pathogen detection nine days post inoculation (dpi) with the pathogen, and thus four days before the first symptoms could be visually detected. Furthermore, support vector machines (SVM) and a combination of LDA and distance classifier (DC) were also able to detect FHB symptoms earlier than manual rating. Scoring the spikes at 13 and 17 dpi with the pathogen showed no significant differences in the FHB incidence among the treatments. Nevertheless, there is a trend suggesting that all BCAs exhibit a diminishing effect against FHB, with fungal isolates demonstrating greater efficacy compared to bacterial ones.
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Affiliation(s)
- Martin E. G. Rieker
- Department of Phytopathology, Institute of Phytomedicine, Faculty of Agricultural Sciences, University of Hohenheim, 70599 Stuttgart, Germany; (M.A.L.); (A.E.-H.); (S.T.); (R.T.V.)
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12
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Maina AW, Oerke EC. Characterization of Rice- Magnaporthe oryzae Interactions by Hyperspectral Imaging. PLANT DISEASE 2023; 107:3139-3147. [PMID: 37871165 DOI: 10.1094/pdis-10-22-2294-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: 10/25/2023]
Abstract
Hyperspectral imaging has the potential to detect, characterize, and quantify plant diseases objectively and nondestructively to improve phenotyping in breeding for disease resistance. In this study, leaf spectral reflectance characteristics of five rice genotypes diseased with blast caused by three Magnaporthe oryzae isolates differing in virulence were compared with visual disease ratings under greenhouse conditions. Spectral information (140 wavebands, range 450 to 850 nm) of infected leaves was recorded with a hyperspectral imaging microscope at 3, 5, and 7 days postinoculation to examine differences in symptom phenotypes and to characterize the compatibility of host-pathogen interactions. Depending on the rice genotype × M. oryzae genotype interaction, blast symptoms varied from tiny necrosis to enlarged lesions with symptom subareas differing in tissue coloration and indicated gene-for-gene-specific interactions. The blast symptom types were differentiated based on their spectral characteristics in the visible/near-infrared range. Symptom-specific spectral signatures and differences in the composition of leaf blast symptom type(s) resulted in unique spectral and spatial patterns of the rice × M. oryzae interactions based on the size, shape, and color of the symptom subareas. Spectral angle mapper classification of spectra enabled (i) discrimination between healthy (green) and diseased tissue of rice genotypes, (ii) classification and quantification of different blast symptom subareas, and (iii) grading of the host-pathogen compatibility (low - intermediate - high). Hyperspectral imaging was more sensitive to small changes in disease resistance than visual disease assessments and enabled the characterization of various types of resistance/susceptibility reactions of tissue subjected to M. oryzae infection.
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Affiliation(s)
- Angeline W Maina
- Institute for Crop Science and Resource Conservation (INRES) - Plant Pathology, Rheinische Friedrich-Wilhelms University of Bonn, Bonn, Germany
| | - Erich-Christian Oerke
- Institute for Crop Science and Resource Conservation (INRES) - Plant Pathology, Rheinische Friedrich-Wilhelms University of Bonn, Bonn, Germany
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13
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Shopova E, Brankova L, Ivanov S, Urshev Z, Dimitrova L, Dimitrova M, Hristova P, Kizheva Y. Xanthomonas euvesicatoria-Specific Bacteriophage BsXeu269p/3 Reduces the Spread of Bacterial Spot Disease in Pepper Plants. PLANTS (BASEL, SWITZERLAND) 2023; 12:3348. [PMID: 37836088 PMCID: PMC10574073 DOI: 10.3390/plants12193348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 09/16/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023]
Abstract
The present study was focused on the pathosystem pepper plants (Capsicum annuum L.)-phytopathogenic bacterium X. euvesicatoria (wild strain 269p)-bacteriophage BsXeu269p/3 and the possibility of bacteriophage-mediated biocontrol of the disease. Two new model systems were designed for the monitoring of the effect of the phage treatment on the infectious process in vivo. The spread of the bacteriophage and the pathogen was monitored by qPCR. A new pair of primers for phage detection via qPCR was designed, as well as probes for TaqMan qPCR. The epiphytic bacterial population and the potential bacteriolytic effect of BsXeu269p/3 in vivo was observed by SEM. An aerosol-mediated transmission model system demonstrated that treatment with BsXeu269p/3 reduced the amount of X. euvesicatoria on the leaf surface five-fold. The needle-pricking model system showed a significant reduction of the amount of the pathogen in infectious lesions treated with BsXeu269p/3 (av. 59.7%), compared to the untreated control. We found that the phage titer is 10-fold higher in the infection lesions but it was still discoverable even in the absence of the specific host in the leaves. This is the first report of in vivo assessment of the biocontrol potential of locally isolated phages against BS pathogen X. euvesicatoria in Bulgaria.
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Affiliation(s)
- Elena Shopova
- Institute of Plant Physiology and Genetics, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., bl. 21., 1113 Sofia, Bulgaria; (E.S.); (L.D.)
| | - Liliana Brankova
- Institute of Plant Physiology and Genetics, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., bl. 21., 1113 Sofia, Bulgaria; (E.S.); (L.D.)
| | - Sergei Ivanov
- Centre of Food Biology, 1592 Sofia, Bulgaria;
- Faculty of Biology, Sofia University St. “Kliment Ohridski”, 8 Dragan Tzankov Blvd., 1164 Sofia, Bulgaria; (M.D.); (P.H.)
| | - Zoltan Urshev
- R&D Center, LB Bulgaricum PLC, 14 Malashevska Str., 1225 Sofia, Bulgaria;
| | - Lyudmila Dimitrova
- Institute of Plant Physiology and Genetics, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., bl. 21., 1113 Sofia, Bulgaria; (E.S.); (L.D.)
| | - Melani Dimitrova
- Faculty of Biology, Sofia University St. “Kliment Ohridski”, 8 Dragan Tzankov Blvd., 1164 Sofia, Bulgaria; (M.D.); (P.H.)
| | - Petya Hristova
- Faculty of Biology, Sofia University St. “Kliment Ohridski”, 8 Dragan Tzankov Blvd., 1164 Sofia, Bulgaria; (M.D.); (P.H.)
| | - Yoana Kizheva
- Faculty of Biology, Sofia University St. “Kliment Ohridski”, 8 Dragan Tzankov Blvd., 1164 Sofia, Bulgaria; (M.D.); (P.H.)
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14
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Sirakov I, Velichkova K, Dinev T, Slavcheva-Sirakova D, Valkova E, Yorgov D, Veleva P, Atanasov V, Atanassova S. Detection of Fungal Diseases in Lettuce by VIR-NIR Spectroscopy in Aquaponics. Microorganisms 2023; 11:2348. [PMID: 37764192 PMCID: PMC10537723 DOI: 10.3390/microorganisms11092348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/14/2023] [Accepted: 09/17/2023] [Indexed: 09/29/2023] Open
Abstract
One of the main challenges facing the development of aquaponics is disease control, due on one hand to the fact that plants cannot be treated with chemicals because they can lead to mortality in cultured fish. The aim of this study was to apply the visible-near-infrared spectroscopy and vegetation index approach to test aquaponically cultivated lettuce (Lactuca sativa L.) infected with different fungal pathogens (Aspergillus niger, Fusarium oxysporum, and Alternaria alternata). The lettuces on the third leaf formation were placed in tanks (with dimensions 1 m/0.50 m/0.35 m) filled up with water from the aquaponics system every second day. In this study, we included reference fungal strains Aspergillus niger NBIMCC 3252, Fusarium oxysporum NBIMCC 125, and Alternaria alternata NBIMCC 109. Diffuse reflectance spectra of the leaves of lettuce were measured directly on the plants using a USB4000 spectrometer in the 450-1100 nm wavelength range. In near-infrared spectral range, the reflectance values of infected leaves are lower than those of the control, which indicates that some changes in cell structures occurred as a result of the fungal infection. All three investigated pathogens had a statistically significant effect on leaf water content and water band index. Vegetative indices such as Chlorophyll Absorption in Reflectance Index (CARI), Modified chlorophyll absorption in reflectance index (MCARI), Plant Senescence Reflectance Index (PSRI), Red Edge Index (REI2), Red Edge Index (REI3), and Water band index (WBI) were found to be effective in distinguishing infected plants from healthy ones, with WBI demonstrating the greatest reliability.
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Affiliation(s)
- Ivaylo Sirakov
- Department of Animal Husbandry-Non-Ruminants and Other Animals, Faculty of Agriculture, Students Campus, Trakia University, 6000 Stara Zagora, Bulgaria
| | - Katya Velichkova
- Department of Biological Sciences, Faculty of Agriculture, Students Campus, Trakia University, 6000 Stara Zagora, Bulgaria
| | - Toncho Dinev
- Department of Biological Sciences, Faculty of Agriculture, Students Campus, Trakia University, 6000 Stara Zagora, Bulgaria
| | - Desislava Slavcheva-Sirakova
- Department of Botany and Agrometeorology, Faculty of Agronomy, Agricultural University, 12 Mendeleev blvd, 4000 Plovdiv, Bulgaria
| | - Elica Valkova
- Department of Biological Sciences, Faculty of Agriculture, Students Campus, Trakia University, 6000 Stara Zagora, Bulgaria
| | - Dimitar Yorgov
- Department of Agricultural Engineering, Faculty of Agriculture, Students Campus, Trakia University, 6000 Stara Zagora, Bulgaria
| | - Petya Veleva
- Department of Agricultural Engineering, Faculty of Agriculture, Students Campus, Trakia University, 6000 Stara Zagora, Bulgaria
| | - Vasil Atanasov
- Department of Biological Sciences, Faculty of Agriculture, Students Campus, Trakia University, 6000 Stara Zagora, Bulgaria
| | - Stefka Atanassova
- Department of Agricultural Engineering, Faculty of Agriculture, Students Campus, Trakia University, 6000 Stara Zagora, Bulgaria
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15
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Sikora RA, Helder J, Molendijk LPG, Desaeger J, Eves-van den Akker S, Mahlein AK. Integrated Nematode Management in a World in Transition: Constraints, Policy, Processes, and Technologies for the Future. ANNUAL REVIEW OF PHYTOPATHOLOGY 2023; 61:209-230. [PMID: 37186900 DOI: 10.1146/annurev-phyto-021622-113058] [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: 05/17/2023]
Abstract
Plant-parasitic nematodes are one of the most insidious pests limiting agricultural production, parasitizing mostly belowground and occasionally aboveground plant parts. They are an important and underestimated component of the estimated 30% yield loss inflicted on crops globally by biotic constraints. Nematode damage is intensified by interactions with biotic and abiotic factors constraints: soilborne pathogens, soil fertility degradation, reduced soil biodiversity, climate variability, and policies influencing the development of improved management options. This review focuses on the following topics: (a) biotic and abiotic constraints, (b) modification of production systems, (c) agricultural policies, (d) the microbiome, (e) genetic solutions, and (f) remote sensing. Improving integrated nematode management (INM) across all scales of agricultural production and along the Global North-Global South divide, where inequalities influence access to technology, is discussed. The importance of the integration of technological development in INM is critical to improving food security and human well-being in the future.
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Affiliation(s)
| | - Johannes Helder
- Laboratory of Nematology, Department of Plant Sciences, Wageningen University, Wageningen, The Netherlands
| | | | - Johan Desaeger
- Institute of Food and Agricultural Sciences, Department of Entomology and Nematology, Gulf Coast Research and Education Center, University of Florida, Wimauma, Florida, USA
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16
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Reis Pereira M, dos Santos FN, Tavares F, Cunha M. Enhancing host-pathogen phenotyping dynamics: early detection of tomato bacterial diseases using hyperspectral point measurement and predictive modeling. FRONTIERS IN PLANT SCIENCE 2023; 14:1242201. [PMID: 37662158 PMCID: PMC10468592 DOI: 10.3389/fpls.2023.1242201] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 07/27/2023] [Indexed: 09/05/2023]
Abstract
Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant disease diagnosis. This study aimed to investigate the potential of HS point-of-measurement (POM) data for in-situ, non-destructive diagnosis of tomato bacterial speck caused by Pseudomonas syringae pv. tomato (Pst), and bacterial spot, caused by Xanthomonas euvesicatoria (Xeu), on leaves (cv. cherry). Bacterial artificial infection was performed on tomato plants at the same phenological stage. A sensing system composed by a hyperspectral spectrometer, a transmission optical fiber bundle with a slitted probe and a white light source were used for spectral data acquisition, allowing the assessment of 3478 spectral points. An applied predictive classification model was developed, consisting of a normalizing pre-processing strategy allied with a Linear Discriminant Analysis (LDA) for reducing data dimensionality and a supervised machine learning algorithm (Support Vector Machine - SVM) for the classification task. The predicted model achieved classification accuracies of 100% and 74% for Pst and Xeu test set assessments, respectively, before symptom appearance. Model predictions were coherent with host-pathogen interactions mentioned in the literature (e.g., changes in photosynthetic pigment levels, production of bacterial-specific molecules, and activation of plants' defense mechanisms). Furthermore, these results were coherent with visual phenotyping inspection and PCR results. The reported outcomes support the application of spectral point measurements acquired in-vivo for plant disease diagnosis, aiming for more precise and eco-friendly phytosanitary approaches.
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Affiliation(s)
- Mafalda Reis Pereira
- Faculdade de Ciências da Universidade do Porto (FCUP), Rua do Campo Alegre, Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, Porto, Portugal
| | - Filipe Neves dos Santos
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, Porto, Portugal
| | - Fernando Tavares
- Faculdade de Ciências da Universidade do Porto (FCUP), Rua do Campo Alegre, Porto, Portugal
- CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de Vairão, Universidade do Porto, Vairão, Portugal
- BIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO, Campus de Vairão, Vairão, Portugal
| | - Mário Cunha
- Faculdade de Ciências da Universidade do Porto (FCUP), Rua do Campo Alegre, Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, Porto, Portugal
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17
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Terentev A, Dolzhenko V. Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:5366. [PMID: 37420533 DOI: 10.3390/s23125366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/25/2023] [Accepted: 06/04/2023] [Indexed: 07/09/2023]
Abstract
The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection.
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Affiliation(s)
- Anton Terentev
- All-Russian Institute of Plant Protection, 196608 Saint Petersburg, Russia
| | - Viktor Dolzhenko
- All-Russian Institute of Plant Protection, 196608 Saint Petersburg, Russia
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18
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Zhang M, Chen T, Gu X, Chen D, Wang C, Wu W, Zhu Q, Zhao C. Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods. FRONTIERS IN PLANT SCIENCE 2023; 14:1073346. [PMID: 36968402 PMCID: PMC10030857 DOI: 10.3389/fpls.2023.1073346] [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: 10/18/2022] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
Tobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior quality, inherent quality, chemical compositions, and physical properties. All these aspects are formed during the growing season and are vulnerable to many environmental factors, such as climate, geography, irrigation, fertilization, diseases and pests, etc. Therefore, there is a great demand for tobacco growth monitoring and near real-time quality evaluation. Herein, hyperspectral remote sensing (HRS) is increasingly being considered as a cost-effective alternative to traditional destructive field sampling methods and laboratory trials to determine various agronomic parameters of tobacco with the assistance of diverse hyperspectral vegetation indices and machine learning algorithms. In light of this, we conduct a comprehensive review of the HRS applications in tobacco production management. In this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection. Finally, we discuss the major challenges and future opportunities for potential application prospects. We hope that this review could provide interested researchers, practitioners, or readers with a basic understanding of current HRS applications in tobacco production management, and give some guidelines for practical works.
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Affiliation(s)
- Mingzheng Zhang
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
| | - Tian’en Chen
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Xiaohe Gu
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Dong Chen
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Cong Wang
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Wenbiao Wu
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Qingzhen Zhu
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Chunjiang Zhao
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
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Fusarium Head Blight on Wheat: Biology, Modern Detection and Diagnosis and Integrated Disease Management. Toxins (Basel) 2023; 15:toxins15030192. [PMID: 36977083 PMCID: PMC10053988 DOI: 10.3390/toxins15030192] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 03/06/2023] Open
Abstract
Fusarium head blight (FHB) is a major threat for wheat production worldwide. Most reviews focus on Fusarium graminearum as a main causal agent of FHB. However, different Fusarium species are involved in this disease complex. These species differ in their geographic adaptation and mycotoxin profile. The incidence of FHB epidemics is highly correlated with weather conditions, especially rainy days with warm temperatures at anthesis and an abundance of primary inoculum. Yield losses due to the disease can reach up to 80% of the crop. This review summarizes the Fusarium species involved in the FHB disease complex with the corresponding mycotoxin profiles, disease cycle, diagnostic methods, the history of FHB epidemics, and the management strategy of the disease. In addition, it discusses the role of remote sensing technology in the integrated management of the disease. This technology can accelerate the phenotyping process in the breeding programs aiming at FHB-resistant varieties. Moreover, it can support the decision-making strategies to apply fungicides via monitoring and early detection of the diseases under field conditions. It can also be used for selective harvest to avoid mycotoxin-contaminated plots in the field.
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20
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Lacotte V, Dell'Aglio E, Peignier S, Benzaoui F, Heddi A, Rebollo R, Da Silva P. A comparative study revealed hyperspectral imaging as a potential standardized tool for the analysis of cuticle tanning over insect development. Heliyon 2023; 9:e13962. [PMID: 36895353 PMCID: PMC9988560 DOI: 10.1016/j.heliyon.2023.e13962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/10/2023] [Accepted: 02/16/2023] [Indexed: 02/24/2023] Open
Abstract
Cereal-feeding beetles are a major risk for cereal crop maintenance. Cereal weevils such as Sitophilus oryzae have symbiotic intracellular bacteria that provide essential aromatic amino acid to the host for the biosynthesis of their cuticle building blocks. Their cuticle is an important protective barrier against biotic and abiotic stresses, providing high resistance from insecticides. Quantitative optical methods specialized in insect cuticle analysis exist, but their scope of use and the repeatability of the results remain limited. Here, we investigated the potential of Hyperspectral Imaging (HSI) as a standardized cuticle analysis tool. Based on HSI, we acquired time series of average reflectance profiles from 400 to 1000 nm from symbiotic (with bacteria) and aposymbiotic (without bacteria) cereal weevils S. oryzae exposed to different nutritional stresses. We assessed the phenotypic changes of weevils under different diets throughout their development and demonstrated the agreement of the results between the HSI method and the classically used Red-Green-Blue analysis. Then, we compared the use of both technologies in laboratory conditions and highlighted the assets of HSI to develop a simple, automated, and standardized analysis tool. This is the first study showing the reliability and feasibility of HSI for a standardized analysis of insect cuticle changes.
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Affiliation(s)
- Virginie Lacotte
- Univ Lyon, INSA Lyon, INRAE, BF2I, UMR 203, 69621 Villeurbanne, France
| | - Elisa Dell'Aglio
- Univ Lyon, INSA Lyon, INRAE, BF2I, UMR 203, 69621 Villeurbanne, France
| | - Sergio Peignier
- Univ Lyon, INSA Lyon, INRAE, BF2I, UMR 203, 69621 Villeurbanne, France
| | - Fadéla Benzaoui
- Univ Lyon, INSA Lyon, INRAE, BF2I, UMR 203, 69621 Villeurbanne, France
| | - Abdelaziz Heddi
- Univ Lyon, INSA Lyon, INRAE, BF2I, UMR 203, 69621 Villeurbanne, France
| | - Rita Rebollo
- Univ Lyon, INSA Lyon, INRAE, BF2I, UMR 203, 69621 Villeurbanne, France
| | - Pedro Da Silva
- Univ Lyon, INSA Lyon, INRAE, BF2I, UMR 203, 69621 Villeurbanne, France
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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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22
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Barreto A, Ispizua Yamati FR, Varrelmann M, Paulus S, Mahlein AK. Disease Incidence and Severity of Cercospora Leaf Spot in Sugar Beet Assessed by Multispectral Unmanned Aerial Images and Machine Learning. PLANT DISEASE 2023; 107:188-200. [PMID: 35581914 DOI: 10.1094/pdis-12-21-2734-re] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Disease incidence (DI) and metrics of disease severity are relevant parameters for decision making in plant protection and plant breeding. To develop automated and sensor-based routines, a sugar beet variety trial was inoculated with Cercospora beticola and monitored with a multispectral camera system mounted to an unmanned aerial vehicle (UAV) over the vegetation period. A pipeline based on machine learning methods was established for image data analysis and extraction of disease-relevant parameters. Features based on the digital surface model, vegetation indices, shadow condition, and image resolution improved classification performance in comparison with using single multispectral channels in 12 and 6% of diseased and soil regions, respectively. With a postprocessing step, area-related parameters were computed after classification. Results of this pipeline also included extraction of DI and disease severity (DS) from UAV data. The calculated area under disease progress curve of DS was 2,810.4 to 7,058.8%.days for human visual scoring and 1,400.5 to 4,343.2%.days for UAV-based scoring. Moreover, a sharper differentiation of varieties compared with visual scoring was observed in area-related parameters such as area of complete foliage (AF), area of healthy foliage (AH), and mean area of lesion by unit of foliage ([Formula: see text]). These advantages provide the option to replace the laborious work of visual disease assessments in the field with a more precise, nondestructive assessment via multispectral data acquired by UAV flights.[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)
- Abel Barreto
- Institute of Sugar Beet Research, 37079 Göttingen, Germany
| | | | | | - Stefan Paulus
- Institute of Sugar Beet Research, 37079 Göttingen, Germany
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23
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Oerke EC, Juraschek L, Steiner U. Hyperspectral mapping of the response of grapevine cultivars to Plasmopara viticola infection at the tissue scale. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:377-395. [PMID: 36173350 DOI: 10.1093/jxb/erac390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 09/28/2022] [Indexed: 06/16/2023]
Abstract
Resistance of grapevine to Plasmopara viticola is associated with the hypersensitive reaction, accumulation of stilbenoids, and formation of callose depositions. Spectral characterization of infected leaf tissue of cvs 'Regent' and 'Solaris' with resistance genes Rpv 3-1 and Rpv 10 and Rpv 3-3, respectively, suggested that resistance is not dependent on large-scale necrotization of host tissue. Reactions of the resistant cultivars and a reference susceptible to P. viticola were studied using hyperspectral imaging (range 400-1000 nm) at the tissue level and microscopic techniques. Resistance of both cultivars was incomplete and allowed pathogen reproduction. Spectral vegetation indices characterized the host response to pathogen invasion; the vitality of infected and necrotic leaf tissue differed significantly. Resistance depended on local accumulation of polyphenols in response to haustorium formation and was more effective for cv. 'Solaris'. Although hypersensitive reaction of some cells prevented colonization of palisade parenchyma, resistance was not associated with extensive necrotization of tissue, and the biotrophic pathogen survived localized death of penetrated host cells. Hyperspectral imaging was suitable to characterize and differentiate the resistance reactions of grapevine cultivars by mapping of the cellular response to pathogen attack on the tissue level and yields useful information on host-pathogen interactions.
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Affiliation(s)
- Erich-Christian Oerke
- Rheinische Friedrich-Wilhelms-Universitaet Bonn, INRES - Plant Pathology, Nussallee 9, D-53115 Bonn, Germany
| | - Lena Juraschek
- Rheinische Friedrich-Wilhelms-Universitaet Bonn, INRES - Plant Pathology, Nussallee 9, D-53115 Bonn, Germany
| | - Ulrike Steiner
- Rheinische Friedrich-Wilhelms-Universitaet Bonn, INRES - Plant Pathology, Nussallee 9, D-53115 Bonn, Germany
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24
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laghari AA, Jumani AK, Laghari RA, Nawaz H. Unmanned Aerial Vehicles: A Review. COGNITIVE ROBOTICS 2022. [DOI: 10.1016/j.cogr.2022.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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25
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Benfenati A, Bolzi D, Causin P, Oberti R. A deep learning generative model approach for image synthesis of plant leaves. PLoS One 2022; 17:e0276972. [DOI: 10.1371/journal.pone.0276972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 10/17/2022] [Indexed: 11/19/2022] Open
Abstract
Objectives
A well-known drawback to the implementation of Convolutional Neural Networks (CNNs) for image-recognition is the intensive annotation effort for large enough training dataset, that can become prohibitive in several applications. In this study we focus on applications in the agricultural domain and we implement Deep Learning (DL) techniques for the automatic generation of meaningful synthetic images of plant leaves, which can be used as a virtually unlimited dataset to train or validate specialized CNN models or other image-recognition algorithms.
Methods
Following an approach based on DL generative models, we introduce a Leaf-to-Leaf Translation (L2L) algorithm, able to produce collections of novel synthetic images in two steps: first, a residual variational autoencoder architecture is used to generate novel synthetic leaf skeletons geometry, starting from binarized skeletons obtained from real leaf images. Second, a translation via Pix2pix framework based on conditional generator adversarial networks (cGANs) reproduces the color distribution of the leaf surface, by preserving the underneath venation pattern and leaf shape.
Results
The L2L algorithm generates synthetic images of leaves with meaningful and realistic appearance, indicating that it can significantly contribute to expand a small dataset of real images. The performance was assessed qualitatively and quantitatively, by employing a DL anomaly detection strategy which quantifies the anomaly degree of synthetic leaves with respect to real samples. Finally, as an illustrative example, the proposed L2L algorithm was used for generating a set of synthetic images of healthy end diseased cucumber leaves aimed at training a CNN model for automatic detection of disease symptoms.
Conclusions
Generative DL approaches have the potential to be a new paradigm to provide low-cost meaningful synthetic samples. Our focus was to dispose of synthetic leaves images for smart agriculture applications but, more in general, they can serve for all computer-aided applications which require the representation of vegetation. The present L2L approach represents a step towards this goal, being able to generate synthetic samples with a relevant qualitative and quantitative resemblance to real leaves.
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Zhao G, Pei Y, Yang R, Xiang L, Fang Z, Wang Y, Yin D, Wu J, Gao D, Yu D, Li X. A non-destructive testing method for early detection of ginseng root diseases using machine learning technologies based on leaf hyperspectral reflectance. FRONTIERS IN PLANT SCIENCE 2022; 13:1031030. [PMID: 36466253 PMCID: PMC9714554 DOI: 10.3389/fpls.2022.1031030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 11/02/2022] [Indexed: 06/17/2023]
Abstract
Ginseng is an important medicinal plant benefiting human health for thousands of years. Root disease is the main cause of ginseng yield loss. It is difficult to detect ginseng root disease by manual observation on the changes of leaves, as it takes a long time until symptoms appear on leaves after the infection on roots. In order to detect root diseases at early stages and limit their further spread, an efficient and non-destructive testing (NDT) method is urgently needed. Hyperspectral remote sensing technology was performed in this study to discern whether ginseng roots were diseased. Hyperspectral reflectance of leaves at 325-1,075 nm were collected from the ginsengs with no symptoms on leaves at visual. These spectra were divided into healthy and diseased groups according to the symptoms on roots after harvest. The hyperspectral data were used to construct machine learning classification models including random forest, extreme random tree (ET), adaptive boosting and gradient boosting decision tree respectively to identify diseased ginsengs, while calculating the vegetation indices and analyzing the region of specific spectral bands. The precision rates of the ET model preprocessed by savitzky golay method for the identification of healthy and diseased ginsengs reached 99% and 98%, respectively. Combined with the preliminary analysis of band importance, vegetation indices and physiological characteristics, 690-726 nm was screened out as a specific band for early detection of ginseng root diseases. Therefore, underground root diseases can be effectively detected at an early stage by leaf hyperspectral reflectance. The NDT method for early detection of ginsengs root diseases is proposed in this study. The method is helpful in the prevention and control of root diseases of ginsengs to prevent the reduction of ginseng yield.
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Affiliation(s)
- Guiping Zhao
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, China
| | - Yifei Pei
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ruoqi Yang
- School of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Li Xiang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zihan Fang
- TCM Department, China National Center for Biotechnology Development, Beijing, China
| | - Ye Wang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Dou Yin
- School of Basic Medical Sciences, Anhui Medical University, Hefei, Anhui, China
| | - Jie Wu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, China
| | - Dan Gao
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Dade Yu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xiwen Li
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
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Lacotte V, Peignier S, Raynal M, Demeaux I, Delmotte F, da Silva P. Spatial-Spectral Analysis of Hyperspectral Images Reveals Early Detection of Downy Mildew on Grapevine Leaves. Int J Mol Sci 2022; 23:ijms231710012. [PMID: 36077411 PMCID: PMC9456054 DOI: 10.3390/ijms231710012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/10/2022] [Accepted: 08/25/2022] [Indexed: 11/30/2022] Open
Abstract
Downy mildew is a highly destructive disease of grapevine. Currently, monitoring for its symptoms is time-consuming and requires specialist staff. Therefore, an automated non-destructive method to detect the pathogen before the visible symptoms appear would be beneficial for early targeted treatments. The aim of this study was to detect the disease early in a controlled environment, and to monitor the disease severity evolution in time and space. We used a hyperspectral image database following the development from 0 to 9 days post inoculation (dpi) of three strains of Plasmopara viticola inoculated on grapevine leaves and developed an automatic detection tool based on a Support Vector Machine (SVM) classifier. The SVM obtained promising validation average accuracy scores of 0.96, a test accuracy score of 0.99, and it did not output false positives on the control leaves and detected downy mildew at 2 dpi, 2 days before the clear onset of visual symptoms at 4 dpi. Moreover, the disease area detected over time was higher than that when visually assessed, providing a better evaluation of disease severity. To our knowledge, this is the first study using hyperspectral imaging to automatically detect and show the spatial distribution of downy mildew on grapevine leaves early over time.
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Affiliation(s)
- Virginie Lacotte
- Univ Lyon, INSA Lyon, INRAE, BF2I, UMR203, F-69621 Villeurbanne, France
| | - Sergio Peignier
- Univ Lyon, INSA Lyon, INRAE, BF2I, UMR203, F-69621 Villeurbanne, France
- Correspondence: (S.P.); (P.d.S.)
| | - Marc Raynal
- IFV, UMT Seven, F-33140 Villenave d’Ornon, France
| | - Isabelle Demeaux
- SAVE, INRAE, Bordeaux Sciences Agro, Univ. Bordeaux, F-33140 Villenave d’Ornon, France
| | - François Delmotte
- SAVE, INRAE, Bordeaux Sciences Agro, Univ. Bordeaux, F-33140 Villenave d’Ornon, France
| | - Pedro da Silva
- Univ Lyon, INSA Lyon, INRAE, BF2I, UMR203, F-69621 Villeurbanne, France
- Correspondence: (S.P.); (P.d.S.)
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28
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Garrett KA, Bebber DP, Etherton BA, Gold KM, Plex Sulá AI, Selvaraj MG. Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation. ANNUAL REVIEW OF PHYTOPATHOLOGY 2022; 60:357-378. [PMID: 35650670 DOI: 10.1146/annurev-phyto-021021-042636] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Plant pathology has developed a wide range of concepts and tools for improving plant disease management, including models for understanding and responding to new risks from climate change. Most of these tools can be improved using new advances in artificial intelligence (AI), such as machine learning to integrate massive data sets in predictive models. There is the potential to develop automated analyses of risk that alert decision-makers, from farm managers to national plant protection organizations, to the likely need for action and provide decision support for targeting responses. We review machine-learning applications in plant pathology and synthesize ideas for the next steps to make the most of these tools in digital agriculture. Global projects, such as the proposed global surveillance system for plant disease, will be strengthened by the integration of the wide range of new data, including data from tools like remote sensors, that are used to evaluate the risk ofplant disease. There is exciting potential for the use of AI to strengthen global capacity building as well, from image analysis for disease diagnostics and associated management recommendations on farmers' phones to future training methodologies for plant pathologists that are customized in real-time for management needs in response to the current risks. International cooperation in integrating data and models will help develop the most effective responses to new challenges from climate change.
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Affiliation(s)
- K A Garrett
- Plant Pathology Department, University of Florida, Gainesville, Florida, USA;
- Food Systems Institute, University of Florida, Gainesville, Florida, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
| | - D P Bebber
- Department of Biosciences, University of Exeter, Exeter, United Kingdom
| | - B A Etherton
- Plant Pathology Department, University of Florida, Gainesville, Florida, USA;
- Food Systems Institute, University of Florida, Gainesville, Florida, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
| | - K M Gold
- Plant Pathology and Plant Microbe Biology Section, School of Integrative Plant Sciences, Cornell AgriTech, Cornell University, Geneva, New York, USA
| | - A I Plex Sulá
- Plant Pathology Department, University of Florida, Gainesville, Florida, USA;
- Food Systems Institute, University of Florida, Gainesville, Florida, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
| | - M G Selvaraj
- The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT), Cali, Colombia
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29
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Zhang C, Zhou L, Xiao Q, Bai X, Wu B, Wu N, Zhao Y, Wang J, Feng L. End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses. PLANT PHENOMICS 2022; 2022:9851096. [PMID: 36059603 PMCID: PMC9394116 DOI: 10.34133/2022/9851096] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 07/03/2022] [Indexed: 11/07/2022]
Abstract
Herbicides and heavy metals are hazardous substances of environmental pollution, resulting in plant stress and harming humans and animals. Identification of stress types can help trace stress sources, manage plant growth, and improve stress-resistant breeding. In this research, hyperspectral imaging (HSI) and chlorophyll fluorescence imaging (Chl-FI) were adopted to identify the rice plants under two types of herbicide stresses (butachlor (DCA) and quinclorac (ELK)) and two types of heavy metal stresses (cadmium (Cd) and copper (Cu)). Visible/near-infrared spectra of leaves (L-VIS/NIR) and stems (S-VIS/NIR) extracted from HSI and chlorophyll fluorescence kinetic curves of leaves (L-Chl-FKC) and stems (S-Chl-FKC) extracted from Chl-FI were fused to establish the models to detect the stress of the hazardous substances. Novel end-to-end deep fusion models were proposed for low-level, middle-level, and high-level information fusion to improve identification accuracy. Results showed that the high-level fusion-based convolutional neural network (CNN) models reached the highest detection accuracy (97.7%), outperforming the models using a single data source (<94.7%). Furthermore, the proposed end-to-end deep fusion models required a much simpler training procedure than the conventional two-stage deep learning fusion. This research provided an efficient alternative for plant stress phenotyping, including identifying plant stresses caused by hazardous substances of environmental pollution.
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Affiliation(s)
- Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Lei Zhou
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China
| | - Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Baohua Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Na Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Yiying Zhao
- Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Junmin Wang
- Institute of Crop Science and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
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Yang JF, Chen WJ, Zhou LM, Hewage KAH, Fu YX, Chen MX, He B, Pei RJ, Song K, Zhang JH, Yin J, Hao GF, Yang GF. Real-Time Fluorescence Imaging of the Abscisic Acid Receptor Allows Nondestructive Visualization of Plant Stress. ACS APPLIED MATERIALS & INTERFACES 2022; 14:28489-28500. [PMID: 35642545 DOI: 10.1021/acsami.2c02156] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Environmental stress greatly decreases crop yield. The application of noninvasive techniques is one of the most practical and feasible ways of monitoring the health condition of plants under stress. However, it remains largely unsolved. A chemical fluorescent probe can be applied as a typical nondestructive method, but it has not been applied in living plants for stress detection to date. The abscisic acid (ABA) receptor plays a central role in conferring tolerance to environmental stresses and is an excellent target for developing fluorescent probes. Herein, we developed a fluorescence molecular imaging technology to monitor live plant stress by visualizing the protein expression level of the ABA receptor PYR1. A computer-aided designed indicator dye, flubactin, exhibited an 8-fold enhancement in fluorescence intensity upon interaction with PYR1. In vitro and in vivo experiments showed that flubactin is suitable to be used to detect salt stress in plants in real time. Moreover, the low toxicity of flubactin promotes its application in the future. Our work opens a new era for the nondestructive visualization of plant stress in vivo.
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Affiliation(s)
- Jing-Fang Yang
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
- International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
| | - Wei-Jie Chen
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
- International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
| | - Li-Ming Zhou
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
- International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
| | - Kamalani Achala H Hewage
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
- International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
| | - Yi-Xuan Fu
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
- International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
| | - Mo-Xian Chen
- Co-Innovation Center for Sustainable Forestry in Southern China & Key Laboratory of National Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China
| | - Bo He
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
- International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
| | - Rong-Jie Pei
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
- International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
| | - Ke Song
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
- International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
| | - Jian-Hua Zhang
- Department of Biology, Hong Kong Baptist University and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong 300072, China
| | - Jun Yin
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
- International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
| | - Ge-Fei Hao
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
- International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
| | - Guang-Fu Yang
- Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
- International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan 430079, China
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Günder M, Ispizua Yamati FR, Kierdorf J, Roscher R, Mahlein AK, Bauckhage C. Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision. Gigascience 2022; 11:6610009. [PMID: 35715875 PMCID: PMC9205758 DOI: 10.1093/gigascience/giac054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 04/06/2022] [Accepted: 05/16/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Unmanned aerial vehicle (UAV)-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. RESULTS In this work, we present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs abbreviated as "cataloging" based on comprehensible computer vision methods. We evaluate the workflow on 2 real-world datasets. One dataset is recorded for observation of Cercospora leaf spot-a fungal disease-in sugar beet over an entire growing cycle. The other one deals with harvest prediction of cauliflower plants. The plant catalog is utilized for the extraction of single plant images seen over multiple time points. This gathers a large-scale spatiotemporal image dataset that in turn can be applied to train further machine learning models including various data layers. CONCLUSION The presented approach improves analysis and interpretation of UAV data in agriculture significantly. By validation with some reference data, our method shows an accuracy that is similar to more complex deep learning-based recognition techniques. Our workflow is able to automatize plant cataloging and training image extraction, especially for large datasets.
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Affiliation(s)
- Maurice Günder
- Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven, 53757 Sankt Augustin, Germany.,Institute for Computer Science III, University of Bonn, Friedrich-Hirzebruch-Allee 5, 53115 Bonn, Germany
| | | | - Jana Kierdorf
- Institute for Geodesy and Geoinformation, University of Bonn, Niebuhrstraße 1a, 53113 Bonn, Germany
| | - Ribana Roscher
- Institute for Geodesy and Geoinformation, University of Bonn, Niebuhrstraße 1a, 53113 Bonn, Germany.,Department of Aerospace and Geodesy, Data Science in Earth Observation, Technical University of Munich, Lise-Meitner-Straße 9, 85521 Ottobrunn, Germany
| | - Anne-Katrin Mahlein
- Institute for Sugar Beet Research (IfZ), Holtenser Landstraße 77, 37079 Göttingen, Germany
| | - Christian Bauckhage
- Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven, 53757 Sankt Augustin, Germany.,Institute for Computer Science III, University of Bonn, Friedrich-Hirzebruch-Allee 5, 53115 Bonn, Germany
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32
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Early Detection of Bacterial Wilt in Tomato with Portable Hyperspectral Spectrometer. REMOTE SENSING 2022. [DOI: 10.3390/rs14122882] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
As a kind of soil-borne epidemic disease, bacterial wilt (BW) is one of the most serious diseases in tomatoes in southern China, which may significantly reduce food quality and the total amount of yield. Hyperspectral remote sensing can detect crop diseases in the early stages and offers potential for BW detection in tomatoes. Tomatoes in southern China are commonly cultivated in greenhouses or bird nets, limiting the application of remote sensing based on natural sunlight. To resolve these issues, we collected the spectrum of tomatoes firstly using the HS-VN1000B Portable Intelligent Spectrometer, which is equipped with a simulated solar light source. We then proposed a tomato BW detection model based on some optimal spectral features. Specifically, these optimal features, including vegetation indexes and principal components (PCs), were extracted by the sequential forward selection (SFS), the simulated annealing (SA), and the genetic algorithm (GA) and were finally fed into the support vector machine (SVM) classifier to detect diseased tomatoes. The results showed that the infected and healthy tomatoes exhibit different spectral characteristics for both leave and stem spectra, especially for near-infrared bands. In addition, the BW detecting model built by the combination of GA and SVM (GA-SVM) achieved the best performance with overall accuracies (OA) of 90.7% for leaves and 92.6% for stems. Compared with the results based on leaves, spectral features of stems provided better accuracy, indicating that the symptom of early infection of BW is more significant in tomato stems than in leaves. Further, the reliability of the GA-SVM tomato stem model was verified in our 2022 experiment with an OA of 88.6% and an F1 score of 0.80. Our study provides an effective means to detect BW disease of tomatoes in the early stages, which could help farmers manage their tomato production and effectively prevent pesticide abuse.
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Hall RD, D'Auria JC, Silva Ferreira AC, Gibon Y, Kruszka D, Mishra P, van de Zedde R. High-throughput plant phenotyping: a role for metabolomics? TRENDS IN PLANT SCIENCE 2022; 27:549-563. [PMID: 35248492 DOI: 10.1016/j.tplants.2022.02.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 01/18/2022] [Accepted: 02/02/2022] [Indexed: 05/17/2023]
Abstract
High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype-phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.
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Affiliation(s)
- Robert D Hall
- BU Bioscience, Wageningen University & Research, 6700 AA, Wageningen, The Netherlands; Laboratory of Plant Physiology, Wageningen University, 6700 AA, Wageningen, The Netherlands; Netherlands Metabolomics Centre, Einsteinweg 55, Leiden, The Netherlands.
| | - John C D'Auria
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK Gatersleben), Gatersleben, Corrensstraße 3, 06466 Seeland, Germany
| | - Antonio C Silva Ferreira
- Universidade Católica Portuguesa, CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Rua Arquiteto Lobão Vital, Apartado 2511, 4202-401 Porto, Portugal; Faculty of AgriSciences, University of Stellenbosch, Matieland 7602, South Africa; Cork Supply Portugal, S.A., Rua Nova do Fial, 4535, Portugal
| | - Yves Gibon
- UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, INRAE Nouvelle Aquitaine - Bordeaux, Avenue Edouard Bourlaux, Villenave d'Ornon, France; Bordeaux Metabolome, MetaboHUB, INRAE, Univ. Bordeaux, Avenue Edouard Bourlaux, Villenave d'Ornon, France PMB-Metabolome, INRAE, Centre INRAE de Nouvelle, Aquitaine-Bordeaux, Villenave d'Ornon, France
| | - Dariusz Kruszka
- Institute of Plant Genetics, Polish Academy of Sciences, 60-479 Poznan, Poland
| | - Puneet Mishra
- Food and Biobased Research, Wageningen University & Research, 6708 WE, Wageningen, The Netherlands
| | - Rick van de Zedde
- Plant Sciences Group, Wageningen University & Research, 6700 AA, Wageningen, The Netherlands
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Assessment of kernel presence in winter wheat ears at spikelet scale using near-infrared hyperspectral imaging. J Cereal Sci 2022. [DOI: 10.1016/j.jcs.2022.103497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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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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Abstract
Approximately half of the world’s apple production occurs in East Asia, where apple Valsa canker (AVC) is a prominent disease. This disease affects the bark of the tree, ultimately killing it and resulting in significant economic loss. Visual identification of the diseased area of the bark, particularly in the early stages, is extremely difficult. In this study, we conducted hyperspectral imaging of the trunks and branches of AVC-infected apple trees and revealed that the diseased area can be identified in the near-infrared reflectance, even when it is difficult to distinguish visually. A discriminant analysis using the Mahalanobis distance was performed on the normalized difference spectral index (NDSI) obtained from the measured spectral reflectance. A diagnostic model for discriminating between the healthy and diseased areas was created using the threshold value of NDSI. An accuracy assessment of the diagnostic model presented the overall accuracy as >0.94 for the combinations of spectral bands at 660–690 nm and 720–760 nm. This simple diagnostic model can be applied to other tree bark canker diseases.
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McNish IG, Smith KP. Oat Crown Rust Disease Severity Estimated at Many Time Points Using Multispectral Aerial Photos. PHYTOPATHOLOGY 2022; 112:682-690. [PMID: 34384242 DOI: 10.1094/phyto-09-20-0442-r] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
All plant breeding programs are dependent on plant phenotypic and genotypic data, but the development of phenotyping technology has been slow relative to that of genotyping. Crown rust (Puccinia coronata f. sp. avenae Erikss.) is the most important disease of cultivated oat (Avena sativa L.), making the development of disease-resistant oat cultivars an important breeding objective. Visual observation is the most common scoring method, but it can be laborious and subjective. We visually scored a diverse collection of 256 oat lines at a total of 27 time points in three disease nursery environments. Multispectral aerial photos were collected using an unmanned aerial vehicle at the same time points as the visual observations. The photos were analyzed, and subsets of the spectral properties of each plot were measured. Random forest modeling was used to model the relationship between the spectral properties of the plots and visually observed disease severity. The ability of the photo data and the random forest model to estimate visually observed disease severity was evaluated using three different cross-validation analyses. We specifically addressed the issue of assessing phenotyping accuracy across and within time points. The accuracy of the photo estimates was greatest for adult plants shortly before they began to senesce. Accuracy outside of that time frame was generally low but statistically significant. Unmanned aerial vehicle-mounted sensors could increase disease scoring efficiency, but additional investigation into the spectral signature of disease severity at all plant growth stages may be necessary to automate accurate full-season measurements.
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Affiliation(s)
| | - Kevin P Smith
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108
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Nguyen VD, Sarić R, Burge T, Berkowitz O, Trtilek M, Whelan J, Lewsey MG, Čustović E. Noninvasive imaging technologies in plant phenotyping. TRENDS IN PLANT SCIENCE 2022; 27:316-317. [PMID: 34257004 DOI: 10.1016/j.tplants.2021.06.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 06/09/2021] [Accepted: 06/17/2021] [Indexed: 06/13/2023]
Affiliation(s)
- Viet D Nguyen
- Department of Animal, Plant, and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Rijad Sarić
- Department of Animal, Plant, and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
| | - Timothy Burge
- Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Oliver Berkowitz
- Department of Animal, Plant, and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Martin Trtilek
- Photon Systems Instruments plant phenotyping research centre, Photon System Instruments, 664 24 Drasov, Brno, Czech Republic
| | - James Whelan
- Department of Animal, Plant, and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia.
| | - Mathew G Lewsey
- Department of Animal, Plant, and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Edhem Čustović
- Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
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Sun D, Robbins K, Morales N, Shu Q, Cen H. Advances in optical phenotyping of cereal crops. TRENDS IN PLANT SCIENCE 2022; 27:191-208. [PMID: 34417079 DOI: 10.1016/j.tplants.2021.07.015] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 07/22/2021] [Accepted: 07/24/2021] [Indexed: 06/13/2023]
Abstract
Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.
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Affiliation(s)
- Dawei Sun
- College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China
| | - Kelly Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Nicolas Morales
- Section of Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Qingyao Shu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Zhejiang University, Hangzhou, PR China; State Key Laboratory of Rice Biology, Zhejiang University, Hangzhou 310058, PR China
| | - Haiyan Cen
- College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China.
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Sun D, Xu Y, Cen H. Optical sensors: deciphering plant phenomics in breeding factories. TRENDS IN PLANT SCIENCE 2022; 27:209-210. [PMID: 34257005 DOI: 10.1016/j.tplants.2021.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 06/17/2021] [Indexed: 06/13/2023]
Affiliation(s)
- Dawei Sun
- College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China
| | - Yang Xu
- School of Micro-nano Electronics, State Key Laboratory of Silicon Materials, Hangzhou Global Scientific and Technological Innovation Center, ZJU-UIUC Joint Institute, Zhejiang University, Hangzhou 310027, PR China
| | - Haiyan Cen
- College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China.
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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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Linking the Spectra of Decomposing Litter to Ecosystem Processes: Tandem Close-Range Hyperspectral Imagery and Decomposition Metrics. REMOTE SENSING 2022. [DOI: 10.3390/rs14020370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Efforts to monitor terrestrial decomposition dynamics at broad spatial scales are hampered by the lack of a cost-effective and scalable means to track the decomposition process. Recent advances in remote sensing have enabled the simulation of litter spectra throughout decomposition for grasses in general, yet unique decomposition pathways are hypothesized to create subtly different litter spectral signatures with unique ecosystem functional significance. The objectives of this study were to improve spectra–decomposition linkages and thereby enable the more comprehensive monitoring of ecosystem processes such as nutrient and carbon cycles. Using close-range hyperspectral imaging, litter spectra and multiple decomposition metrics were concurrently monitored in four classes of naturally decayed litter under four decomposition treatments. The first principal component accounted for approximately 94% of spectral variation in the close-range imagery and was attributed to the progression of decomposition. Decomposition-induced spectral changes were moderately correlated with the leaf carbon to nitrogen ratio (R2 = 0.52) and sodium hydroxide extractables (R2 = 0.45) but had no correlation with carbon dioxide flux. Temperature and humidity strongly influenced the decomposition process but did not influence spectral variability or the patterns of surface decomposition. The outcome of the study is that litter spectra are linked to important metrics of decomposition and thus remote sensing could be utilized to assess decomposition dynamics and the implications for nutrient recycling at broad spatial scales. A secondary study outcome is the need to resolve methodological challenges related to inducing unique decomposition pathways in a lab environment. Improving decomposition treatments that mimic real-world conditions of temperature, humidity, insolation, and the decomposer community will enable an improved understanding of the impacts of climatic change, which are expected to strongly affect microbially mediated decomposition.
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González Guzmán M, Cellini F, Fotopoulos V, Balestrini R, Arbona V. New approaches to improve crop tolerance to biotic and abiotic stresses. PHYSIOLOGIA PLANTARUM 2022; 174:e13547. [PMID: 34480798 PMCID: PMC9290814 DOI: 10.1111/ppl.13547] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 08/24/2021] [Accepted: 08/31/2021] [Indexed: 05/24/2023]
Abstract
During the last years, a great effort has been dedicated at the development and employment of diverse approaches for achieving more stress-tolerant and climate-flexible crops and sustainable yield increases to meet the food and energy demands of the future. The ongoing climate change is in fact leading to more frequent extreme events with a negative impact on food production, such as increased temperatures, drought, and soil salinization as well as invasive arthropod pests and diseases. In this review, diverse "green strategies" (e.g., chemical priming, root-associated microorganisms), and advanced technologies (e.g., genome editing, high-throughput phenotyping) are described on the basis of the most recent research evidence. Particularly, attention has been focused on the potential use in a context of sustainable and climate-smart agriculture (the so called "next agriculture generation") to improve plant tolerance and resilience to abiotic and biotic stresses. In addition, the gap between the results obtained in controlled experiments and those from application of these technologies in real field conditions (lab to field step) is also discussed.
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Affiliation(s)
- Miguel González Guzmán
- Departament de Ciències Agràries i del Medi NaturalUniversitat Jaume ICastelló de la PlanaSpain
- The OPTIMUS PRIME consortium, European Union Partnership for Research and Innovation in the Mediterranean Area (PRIMA) Program
| | - Francesco Cellini
- The OPTIMUS PRIME consortium, European Union Partnership for Research and Innovation in the Mediterranean Area (PRIMA) Program
- Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA)MetapontoItaly
- Consiglio Nazionale delle Ricerche, Istituto per la Protezione Sostenibile delle Piante (CNR, IPSP)TorinoItaly
| | - Vasileios Fotopoulos
- The OPTIMUS PRIME consortium, European Union Partnership for Research and Innovation in the Mediterranean Area (PRIMA) Program
- Department of Agricultural Sciences, Biotechnology & Food ScienceCyprus University of TechnologyLemesosCyprus
| | - Raffaella Balestrini
- The OPTIMUS PRIME consortium, European Union Partnership for Research and Innovation in the Mediterranean Area (PRIMA) Program
- Consiglio Nazionale delle Ricerche, Istituto per la Protezione Sostenibile delle Piante (CNR, IPSP)TorinoItaly
| | - Vicent Arbona
- Departament de Ciències Agràries i del Medi NaturalUniversitat Jaume ICastelló de la PlanaSpain
- The OPTIMUS PRIME consortium, European Union Partnership for Research and Innovation in the Mediterranean Area (PRIMA) Program
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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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Grishina A, Sherstneva O, Grinberg M, Zdobnova T, Ageyeva M, Khlopkov A, Sukhov V, Brilkina A, Vodeneev V. Pre-Symptomatic Detection of Viral Infection in Tobacco Leaves Using PAM Fluorometry. PLANTS (BASEL, SWITZERLAND) 2021; 10:2782. [PMID: 34961253 PMCID: PMC8707847 DOI: 10.3390/plants10122782] [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: 11/18/2021] [Revised: 12/11/2021] [Accepted: 12/13/2021] [Indexed: 06/14/2023]
Abstract
Chlorophyll fluorescence imaging was used to study potato virus X (PVX) infection of Nicotiana benthamiana. Infection-induced changes in chlorophyll fluorescence parameters (quantum yield of photosystem II photochemistry (ΦPSII) and non-photochemical fluorescence quenching (NPQ)) in the non-inoculated leaf were recorded and compared with the spatial distribution of the virus detected by the fluorescence of GFP associated with the virus. We determined infection-related changes at different points of the light-induced chlorophyll fluorescence kinetics and at different days after inoculation. A slight change in the light-adapted steady-state values of ΦPSII and NPQ was observed in the infected area of the non-inoculated leaf. In contrast to the steady-state parameters, the dynamics of ΦPSII and NPQ caused by the dark-light transition in healthy and infected areas differed significantly starting from the second day after the detection of the virus in a non-inoculated leaf. The coefficients of correlation between chlorophyll fluorescence parameters and virus localization were 0.67 for ΦPSII and 0.76 for NPQ. In general, the results demonstrate the possibility of reliable pre-symptomatic detection of the spread of a viral infection using chlorophyll fluorescence imaging.
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Affiliation(s)
- Alyona Grishina
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (O.S.); (M.G.); (T.Z.); (A.K.); (V.S.)
| | - Oksana Sherstneva
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (O.S.); (M.G.); (T.Z.); (A.K.); (V.S.)
| | - Marina Grinberg
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (O.S.); (M.G.); (T.Z.); (A.K.); (V.S.)
| | - Tatiana Zdobnova
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (O.S.); (M.G.); (T.Z.); (A.K.); (V.S.)
| | - Maria Ageyeva
- Department of Biochemistry and Biotechnology, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (M.A.); (A.B.)
| | - Andrey Khlopkov
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (O.S.); (M.G.); (T.Z.); (A.K.); (V.S.)
| | - Vladimir Sukhov
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (O.S.); (M.G.); (T.Z.); (A.K.); (V.S.)
| | - Anna Brilkina
- Department of Biochemistry and Biotechnology, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (M.A.); (A.B.)
| | - Vladimir Vodeneev
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (O.S.); (M.G.); (T.Z.); (A.K.); (V.S.)
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Detecting fumonisin B1 in black beans (Phaseolus vulgaris L.) by near-infrared spectroscopy (NIRS). Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108335] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Sott MK, Nascimento LDS, Foguesatto CR, Furstenau LB, Faccin K, Zawislak PA, Mellado B, Kong JD, Bragazzi NL. A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure. SENSORS 2021; 21:s21237889. [PMID: 34883903 PMCID: PMC8659853 DOI: 10.3390/s21237889] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 11/17/2021] [Accepted: 11/24/2021] [Indexed: 12/21/2022]
Abstract
The agriculture sector is one of the backbones of many countries’ economies. Its processes have been changing to enable technology adoption to increase productivity, quality, and sustainable development. In this research, we present a scientific mapping of the adoption of precision techniques and breakthrough technologies in agriculture, so-called Digital Agriculture. To do this, we used 4694 documents from the Web of Science database to perform a Bibliometric Performance and Network Analysis of the literature using SciMAT software with the support of the PICOC protocol. Our findings presented 22 strategic themes related to Digital Agriculture, such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAV) and Climate-smart Agriculture (CSA), among others. The thematic network structure of the nine most important clusters (motor themes) was presented and an in-depth discussion was performed. The thematic evolution map provides a broad perspective of how the field has evolved over time from 1994 to 2020. In addition, our results discuss the main challenges and opportunities for research and practice in the field of study. Our findings provide a comprehensive overview of the main themes related to Digital Agriculture. These results show the main subjects analyzed on this topic and provide a basis for insights for future research.
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Affiliation(s)
- Michele Kremer Sott
- Business School, Unisinos University, Porto Alegre 91330-002, RS, Brazil; (C.R.F.); (K.F.)
- Correspondence: (M.K.S.); (N.L.B.)
| | - Leandro da Silva Nascimento
- School of Management, Federal University of Rio Grande do Sul, Porto Alegre 90040-060, RS, Brazil; (L.d.S.N.); (P.A.Z.)
| | | | - Leonardo B. Furstenau
- Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre 90040-060, RS, Brazil;
| | - Kadígia Faccin
- Business School, Unisinos University, Porto Alegre 91330-002, RS, Brazil; (C.R.F.); (K.F.)
| | - Paulo Antônio Zawislak
- School of Management, Federal University of Rio Grande do Sul, Porto Alegre 90040-060, RS, Brazil; (L.d.S.N.); (P.A.Z.)
| | - Bruce Mellado
- School of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg 2050, South Africa;
| | - Jude Dzevela Kong
- Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada;
| | - Nicola Luigi Bragazzi
- Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada;
- Correspondence: (M.K.S.); (N.L.B.)
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Jia Y, Xue L, Xu P, Luo B, Chen KN, Zhu L, Liu Y, Yan M. A novel hyperspectral compressive sensing framework of plant leaves based on multiple arbitrary-shape regions of interest. PeerJ Comput Sci 2021; 7:e802. [PMID: 34909466 PMCID: PMC8641574 DOI: 10.7717/peerj-cs.802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 11/11/2021] [Indexed: 06/14/2023]
Abstract
Massive plant hyperspectral images (HSIs) result in huge storage space and put a heavy burden for the traditional data acquisition and compression technology. For plant leaf HSIs, useful plant information is located in multiple arbitrary-shape regions of interest (MAROIs), while the background usually does not contain useful information, which wastes a lot of storage resources. In this paper, a novel hyperspectral compressive sensing framework for plant leaves with MAROIs (HCSMAROI) is proposed to alleviate these problems. HCSMAROI only compresses and reconstructs MAROIs by discarding the background to achieve good reconstructed performance. But for different plant leaf HSIs, HCSMAROI has the potential to be applied in other HSIs. Firstly, spatial spectral decorrelation criterion (SSDC) is used to obtain the optimal band of plant leaf HSIs; Secondly, different leaf regions and background are distinguished by the mask image of the optimal band; Finally, in order to improve the compression efficiency, after discarding the background region the compressed sensing technology based on blocking and expansion is used to compress and reconstruct the MAROIs of plant leaves one by one. Experimental results of soybean leaves and tea leaves show that HCSMAROI can achieve 3.08 and 5.05 dB higher PSNR than those of blocking compressive sensing (BCS) at the sampling rate of 5%, respectively. The reconstructed spectra of HCSMAROI are especially closer to the original ones than that of BCS. Therefore, HCSMAROI can achieve significantly higher reconstructed performance than that of BCS. Moreover, HCSMAROI can provide a flexible way to compress and reconstruct different MAROIs with different sampling rates, while achieving good reconstruction performance in the spatial and spectral domains.
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Affiliation(s)
- Yuewei Jia
- College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang Province, China
| | - Lingyun Xue
- College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang Province, China
| | - Ping Xu
- College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang Province, China
| | - Bin Luo
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Ke-nan Chen
- College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang Province, China
| | - Lei Zhu
- College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang Province, China
| | - Yian Liu
- College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang Province, China
| | - Ming Yan
- College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang Province, China
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49
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Determination of Main Spectral and Luminescent Characteristics of Winter Wheat Seeds Infected with Pathogenic Microflora. PHOTONICS 2021. [DOI: 10.3390/photonics8110494] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In connection with the constant growth of demand for high-quality food products, there is a need to develop effective methods for storing agricultural products, and the registration and predicting infection in the early stages. The studying of the physical properties of infected plants and seeds has fundamental importance for determining crop losses, conducting a survey of diseases, and assessing the effectiveness of their control (assessment of the resistance of crops and varieties, the effect of fungicides, etc.). Presently, photoluminescent methods for diagnosing seeds in the ultraviolet and visible ranges have not been studied. For research, seeds of winter wheat were selected, and were infected with one of the most common and dangerous diseases for plants—fusarium. The research of luminescence was carried out based on a hardware–software complex consisting of a multifunctional spectrofluorometer “Fluorat-02-Panorama”, a computer with software “Panorama Pro” installed, and an external camera for the samples under study. Spectra were obtained with a diagnostic range of winter wheat seeds of 220–400 nm. Based on the results obtained for winter wheat seeds, it is possible to further develop a method for determining the degree of fusarium infection.
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50
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Tsai NC, Hsu TS, Kuo SC, Kao CT, Hung TH, Lin DG, Yeh CS, Chu CC, Lin JS, Lin HH, Ko CY, Chang TH, Su JC, Lin YCJ. Large-scale data analysis for robotic yeast one-hybrid platforms and multi-disciplinary studies using GateMultiplex. BMC Biol 2021; 19:214. [PMID: 34560855 PMCID: PMC8461970 DOI: 10.1186/s12915-021-01140-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 09/03/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Yeast one-hybrid (Y1H) is a common technique for identifying DNA-protein interactions, and robotic platforms have been developed for high-throughput analyses to unravel the gene regulatory networks in many organisms. Use of these high-throughput techniques has led to the generation of increasingly large datasets, and several software packages have been developed to analyze such data. We previously established the currently most efficient Y1H system, meiosis-directed Y1H; however, the available software tools were not designed for processing the additional parameters suggested by meiosis-directed Y1H to avoid false positives and required programming skills for operation. RESULTS We developed a new tool named GateMultiplex with high computing performance using C++. GateMultiplex incorporated a graphical user interface (GUI), which allows the operation without any programming skills. Flexible parameter options were designed for multiple experimental purposes to enable the application of GateMultiplex even beyond Y1H platforms. We further demonstrated the data analysis from other three fields using GateMultiplex, the identification of lead compounds in preclinical cancer drug discovery, the crop line selection in precision agriculture, and the ocean pollution detection from deep-sea fishery. CONCLUSIONS The user-friendly GUI, fast C++ computing speed, flexible parameter setting, and applicability of GateMultiplex facilitate the feasibility of large-scale data analysis in life science fields.
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Affiliation(s)
- Ni-Chiao Tsai
- Department of Life Science and Institute of Plant Biology, College of Life Science, National Taiwan University, Taipei, 10617, Taiwan
| | - Tzu-Shu Hsu
- Department of Pharmacy, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
| | - Shang-Che Kuo
- Department of Pharmacy, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei, 10617, Taiwan
| | - Chung-Ting Kao
- Department of Life Science and Institute of Plant Biology, College of Life Science, National Taiwan University, Taipei, 10617, Taiwan
| | - Tzu-Huan Hung
- Biotechnology Division, Taiwan Agricultural Research Institute, Taichung, 41362, Taiwan
| | - Da-Gin Lin
- Biotechnology Division, Taiwan Agricultural Research Institute, Taichung, 41362, Taiwan
| | - Chung-Shu Yeh
- Genomics Research Center, Academia Sinica, Taipei, 11529, Taiwan
| | - Chia-Chen Chu
- Department of Life Science and Institute of Plant Biology, College of Life Science, National Taiwan University, Taipei, 10617, Taiwan
| | - Jeng-Shane Lin
- Department of Life Sciences, National Chung Hsing University, Taichung, 40227, Taiwan
| | - Hsin-Hung Lin
- Department of Horticulture and Biotechnology, Chinese Culture University, Taipei, 11114, Taiwan
| | - Chia-Ying Ko
- Department of Life Sciences and Institute of Fisheries Science, National Taiwan University, Taipei, 10617, Taiwan
| | - Tien-Hsien Chang
- Genomics Research Center, Academia Sinica, Taipei, 11529, Taiwan.
| | - Jung-Chen Su
- Department of Pharmacy, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan.
| | - Ying-Chung Jimmy Lin
- Department of Life Science and Institute of Plant Biology, College of Life Science, National Taiwan University, Taipei, 10617, Taiwan.
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei, 10617, Taiwan.
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