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Abebe AM, Kim Y, Kim J, Kim SL, Baek J. Image-Based High-Throughput Phenotyping in Horticultural Crops. PLANTS (BASEL, SWITZERLAND) 2023; 12:2061. [PMID: 37653978 PMCID: PMC10222289 DOI: 10.3390/plants12102061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/12/2023] [Accepted: 05/18/2023] [Indexed: 09/02/2023]
Abstract
Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.
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Affiliation(s)
| | | | | | | | - Jeongho Baek
- Department of Agricultural Biotechnology, National Institute of Agricultural Science, Rural Development Administration, Jeonju 54874, Republic of Korea
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2
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Meno L, Escuredo O, Abuley IK, Seijo MC. Predicting Daily Aerobiological Risk Level of Potato Late Blight Using C5.0 and Random Forest Algorithms under Field Conditions. SENSORS (BASEL, SWITZERLAND) 2023; 23:3818. [PMID: 37112159 PMCID: PMC10146589 DOI: 10.3390/s23083818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 04/03/2023] [Accepted: 04/06/2023] [Indexed: 06/19/2023]
Abstract
Late blight, caused by Phytophthora infestans, is a major disease of the potato crop with a strong negative impact on tuber yield and tuber quality. The control of late blight in conventional potato production systems is often through weekly application of prophylactic fungicides, moving away from a sustainable production system. In support of integrated pest management practices, machine learning algorithms were proposed as tools to forecast aerobiological risk level (ARL) of Phytophthora infestans (>10 sporangia/m3) as inoculum to new infections. For this, meteorological and aerobiological data were monitored during five potato crop seasons in Galicia (northwest Spain). Mild temperatures (T) and high relative humidity (RH) were predominant during the foliar development (FD), coinciding with higher presence of sporangia in this phenological stage. The infection pressure (IP), wind, escape or leaf wetness (LW) of the same day also were significantly correlated with sporangia according to Spearman's correlation test. ML algorithms such as random forest (RF) and C5.0 decision tree (C5.0) were successfully used to predict daily sporangia levels, with an accuracy of the models of 87% and 85%, respectively. Currently, existing late blight forecasting systems assume a constant presence of critical inoculum. Therefore, ML algorithms offer the possibility of predicting critical levels of Phytophthora infestans concentration. The inclusion of this type of information in forecasting systems would increase the exactitude in the estimation of the sporangia of this potato pathogen.
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Affiliation(s)
- Laura Meno
- Department of Vegetal Biology and Soil Sciences, Faculty of Sciences, University of Vigo, 32004 Ourense, Spain
| | - Olga Escuredo
- Department of Vegetal Biology and Soil Sciences, Faculty of Sciences, University of Vigo, 32004 Ourense, Spain
| | - Isaac K. Abuley
- Department of Agroecology, Flakkebjerg Research Center, Aarhus University, Forsøgsvej 1, 4200 Aarhus, Denmark
| | - M. Carmen Seijo
- Department of Vegetal Biology and Soil Sciences, Faculty of Sciences, University of Vigo, 32004 Ourense, Spain
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3
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Singh RN, Krishnan P, Bhardwaj C, Das B. Improving prediction of chickpea wilt severity using machine learning coupled with model combination techniques under field conditions. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2022.101933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
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4
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AI meets UAVs: A survey on AI empowered UAV perception systems for precision agriculture. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2022.11.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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5
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Bai X, Zhou Y, Feng X, Tao M, Zhang J, Deng S, Lou B, Yang G, Wu Q, Yu L, Yang Y, He Y. Evaluation of rice bacterial blight severity from lab to field with hyperspectral imaging technique. FRONTIERS IN PLANT SCIENCE 2022; 13:1037774. [PMID: 36340356 PMCID: PMC9627309 DOI: 10.3389/fpls.2022.1037774] [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: 09/06/2022] [Accepted: 10/03/2022] [Indexed: 06/16/2023]
Abstract
Hyperspectral imaging technique combined with machine learning is a powerful tool for the evaluation of disease phenotype in rice disease-resistant breeding. However, the current studies are almost carried out in the lab environment, which is difficult to apply to the field environment. In this paper, we used visible/near-infrared hyperspectral images to analysis the severity of rice bacterial blight (BB) and proposed a novel disease index construction strategy (NDSCI) for field application. A designed long short-term memory network with attention mechanism could evaluate the BB severity robustly, and the attention block could filter important wavelengths. Best results were obtained based on the fusion of important wavelengths and color features with an accuracy of 0.94. Then, NSDCI was constructed based on the important wavelength and color feature related to BB severity. The correlation coefficient of NDSCI extended to the field data reached -0.84, showing good scalability. This work overcomes the limitations of environmental conditions and sheds new light on the rapid measurement of phenotype in disease-resistant breeding.
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Affiliation(s)
- Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yujie Zhou
- Zhuji Agricultural Technology Extension Center, Zhuji, China
| | - Xuping Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Mingzhu Tao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Jinnuo Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Shuiguang Deng
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Binggan Lou
- College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Guofeng Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Qingguan Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Li Yu
- Agricultural Experiment Station & Agricultural Sci-Tech Park Management Committee, Zhejiang University, Hangzhou, China
| | - Yong Yang
- State Key Laboratory for Managing Biotic and Chemical Treats to the Quality and Safety of Agro-Products, Key Laboratory of Biotechnology for Plant Protection, Ministry of Agriculture, and Rural Affairs, Zhejiang Provincial Key Laboratory of Biotechnology for Plant Protection, Institute of Virology and Biotechnology, Zhejiang Academy of Agricultural Science, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
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6
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Gómez-Caro S, Mendoza-Vargas LA, Ramírez-Gil JG, Burbano-David D, Soto-Suárez M, Melgarejo LM. Close-Range Thermography and Reflectance Spectroscopy Support In Vitro and In Vivo Characterization of Colletotrichum spp. Isolates from Mango Fruits. PLANT DISEASE 2022; 106:2355-2369. [PMID: 35350902 DOI: 10.1094/pdis-08-21-1774-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: 06/14/2023]
Abstract
Colletotrichum causing anthracnose in mango is known for its variable virulence that may have an effect on disease development and efficacy of management strategies. In this study, we characterized Colletotrichum spp. isolated from mango fruits under in vitro and in vivo conditions using close-range thermography and reflectance spectroscopy. Twenty-six isolates were phylogenetically characterized to ascertain species using the internal transcribed spacer sequence. Virulence, spectral (in vivo and in vitro), and thermographic responses (in vivo) of these isolates were analyzed. Isolates were grouped into the Colletotrichum gloeosporioides species complex and classified into eight morphotypes. Mycelial growth, conidia production, sporulation abundance, and area under disease progress curve (AUDPC) varied largely among isolates. Disease symptoms were observed 4 days after inoculation (dai), and, for most morphotypes, changes in tissue temperature were registered at 11 dai, with the greatest decrease at 14 dai with pathogen sporulation. In vitro and in vivo morphotypes shared changes in the spectrum range, and main variations were found in the number of informative spectral bands. In vivo average gross reflectance was higher in disease-inoculated tissue than in healthy uninoculated tissue. Morphotype responses varied depending on AUDPC values and postinoculation time. Discriminant analysis of the spectral response using principal component analysis and partial least squares regression explained 94 to 96.3 and 98 to 99.9% of the variance from in vitro and in vivo tests, respectively. Spectral markers were obtained for four distinct morphotype groups. We found three (550 to 650, 650.1 to 790, and 1,300 to 1,400 nm) and two (520 to 830 and 1,100 to 1,450 nm) regions with highly (P < 0.05) discriminant spectral bands for diseased fruits and morphotype characterization.
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Affiliation(s)
- Sandra Gómez-Caro
- Facultad de Ciencias Agrarias, Departamento de Agronomía, Universidad Nacional de Colombia-Sede Bogotá, Bogotá, Colombia
| | - Luis Alberto Mendoza-Vargas
- Facultad de Ciencias Agrarias, Departamento de Agronomía, Universidad Nacional de Colombia-Sede Bogotá, Bogotá, Colombia
| | - Joaquín Guillermo Ramírez-Gil
- Facultad de Ciencias Agrarias, Departamento de Agronomía, Universidad Nacional de Colombia-Sede Bogotá, Bogotá, Colombia
| | - Diana Burbano-David
- Centro de Investigación Tibaitatá, Corporación Colombiana de Investigación Agropecuaria-AGROSAVIA, 250047 Mosquera, Colombia
| | - Mauricio Soto-Suárez
- Centro de Investigación Tibaitatá, Corporación Colombiana de Investigación Agropecuaria-AGROSAVIA, 250047 Mosquera, Colombia
| | - Luz Marina Melgarejo
- Facultad de Ciencias, Departamento de Biología, Laboratorio de Fisiología y Bioquímica Vegetal, Universidad Nacional de Colombia-Sede Bogotá, Bogotá, Colombia
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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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8
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Bouguettaya A, Zarzour H, Kechida A, Taberkit AM. A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images. CLUSTER COMPUTING 2022; 26:1297-1317. [PMID: 35968221 PMCID: PMC9362359 DOI: 10.1007/s10586-022-03627-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 04/12/2022] [Accepted: 05/10/2022] [Indexed: 06/15/2023]
Abstract
The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers' naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is based on the use of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) provides an effective solution to achieve agriculture applications, including plant disease identification and treatment. In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end, in this review, we analyze the recent advances in the use of computer vision techniques that are based on deep learning algorithms and UAV technologies to identify and treat crop diseases.
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Affiliation(s)
- Abdelmalek Bouguettaya
- Research Centre in Industrial Technologies (CRTI), P.O. Box 64, Cheraga, 16014 Algiers, Algeria
| | - Hafed Zarzour
- LIM Research, Department of Mathematics and Computer Science, Souk Ahras University, 41000 Souk Ahras, Algeria
| | - Ahmed Kechida
- Research Centre in Industrial Technologies (CRTI), P.O. Box 64, Cheraga, 16014 Algiers, Algeria
| | - Amine Mohammed Taberkit
- Research Centre in Industrial Technologies (CRTI), P.O. Box 64, Cheraga, 16014 Algiers, Algeria
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9
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Olave-Achury A, Cardenas D, Restrepo S, Lucca F, Fry WE, Myers KL, Danies G, Soto-Suarez M. Phenotypic and Genotypic Characterization of Phytophthora infestans Isolates Associated with Tomato and Potato Crops in Colombia. PHYTOPATHOLOGY 2022; 112:1783-1794. [PMID: 35124971 DOI: 10.1094/phyto-04-21-0158-r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Late blight disease, caused by the plant pathogen Phytophthora infestans, is one of the major threats for tomato and potato crops. Monitoring the populations of P. infestans is important to determine if there are changes in the sensitivity to fungicides and host preference. In this study, microsatellite markers and mitochondrial haplotypes were used to assess the genotype of isolates of P. infestans collected from tomato and potato plants in Colombia. Furthermore, sensitivity to the three fungicides cymoxanil (penetrant fungicide), mefenoxam, and fluopicolide (systemic fungicides), and tomato-potato host preference, were evaluated. Mitochondrial haplotyping showed that isolates collected on tomato were from the genetic groups Ia and Ib, while isolates collected on potatoes belonged to group IIa. Microsatellite analyses showed that isolates from tomato form two groups, including the Ib mitochondrial haplotype (which is genetically close to the US-1 clonal lineage) and the Ia haplotype (related to the EC-3 lineage), whereas Colombian isolates from potato formed a separate group. Furthermore, differences in sensitivity to fungicides were observed. Eighty-one percent of the isolates tested were resistant to mefenoxam with an EC50 >10 μg ml-1. Forty-two percent of the isolates showed an intermediate resistance to cymoxanil. The EC50 values ranged between 1 and 10 μg ml-1. For fluopicolide, 90% of the isolates were sensitive, with EC50 <1 μg ml-1. Host preference assays showed that potato isolates infected both host species. Thus, isolates that infect potatoes may pose a risk for tomato crops nearby.
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Affiliation(s)
- Andrea Olave-Achury
- Department of Food and Chemical Engineering, Laboratory of Mycology and Phytopathology, Universidad de los Andes, Bogotá, Colombia 27991
- Corporación Colombiana de Investigación Agropecuaria-AGROSAVIA, Centro de Investigación Tibaitatá, Km 14 Via Mosquera-Bogotá, Cundinamarca, Colombia 250047
| | - Dixon Cardenas
- Department of Food and Chemical Engineering, Laboratory of Mycology and Phytopathology, Universidad de los Andes, Bogotá, Colombia 27991
| | - Silvia Restrepo
- Department of Food and Chemical Engineering, Laboratory of Mycology and Phytopathology, Universidad de los Andes, Bogotá, Colombia 27991
| | - Florencia Lucca
- Instituto Nacional de Tecnología Agropecuaria, Grupo de Investigación en Papa, Estación Experimental Agropecuaria Balcarce, 7620 República Argentina
| | - William E Fry
- School of Integrative Plant Science, Plant Pathology and Plant-Microbe Biology Section, Cornell University, Ithaca, NY 14850, U.S.A
| | - Kevin L Myers
- School of Integrative Plant Science, Plant Pathology and Plant-Microbe Biology Section, Cornell University, Ithaca, NY 14850, U.S.A
| | - Giovanna Danies
- Department of Design, Universidad de los Andes, Bogotá, Colombia 111711
| | - Mauricio Soto-Suarez
- Corporación Colombiana de Investigación Agropecuaria-AGROSAVIA, Centro de Investigación Tibaitatá, Km 14 Via Mosquera-Bogotá, Cundinamarca, Colombia 250047
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Sun C, Zhou J, Ma Y, Xu Y, Pan B, Zhang Z. A review of remote sensing for potato traits characterization in precision agriculture. FRONTIERS IN PLANT SCIENCE 2022; 13:871859. [PMID: 35923874 PMCID: PMC9339983 DOI: 10.3389/fpls.2022.871859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 06/29/2022] [Indexed: 06/15/2023]
Abstract
Potato is one of the most significant food crops globally due to its essential role in the human diet. The growing demand for potato, coupled with severe environmental losses caused by extensive farming activities, implies the need for better crop protection and management practices. Precision agriculture is being well recognized as the solution as it deals with the management of spatial and temporal variability to improve agricultural returns and reduce environmental impact. As the initial step in precision agriculture, the traditional methods of crop and field characterization require a large input in labor, time, and cost. Recent developments in remote sensing technologies have facilitated the process of monitoring crops and quantifying field variations. Successful applications have been witnessed in the area of precision potato farming. Thus, this review reports the current knowledge on the applications of remote sensing technologies in precision potato trait characterization. We reviewed the commonly used imaging sensors and remote sensing platforms with the comparisons of their strengths and limitations and summarized the main applications of the remote sensing technologies in potato. As a result, this review could update potato agronomists and farmers with the latest approaches and research outcomes, as well as provide a selective list for those who have the intentions to apply remote sensing technologies to characterize potato traits for precision agriculture.
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Affiliation(s)
- Chen Sun
- Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States
- Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optics and Precision Mechanics, Xi’an, China
| | - Jing Zhou
- Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Yuchi Ma
- Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Yijia Xu
- Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Bin Pan
- School of Statistics and Data Science, Nankai University, Tianjin, China
| | - Zhou Zhang
- Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States
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Zhang Z, Cheng S, Solis-Lemus C. Towards a robust out-of-the-box neural network model for genomic data. BMC Bioinformatics 2022; 23:125. [PMID: 35397517 PMCID: PMC8994362 DOI: 10.1186/s12859-022-04660-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 03/21/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
The accurate prediction of biological features from genomic data is paramount for precision medicine and sustainable agriculture. For decades, neural network models have been widely popular in fields like computer vision, astrophysics and targeted marketing given their prediction accuracy and their robust performance under big data settings. Yet neural network models have not made a successful transition into the medical and biological world due to the ubiquitous characteristics of biological data such as modest sample sizes, sparsity, and extreme heterogeneity.
Results
Here, we investigate the robustness, generalization potential and prediction accuracy of widely used convolutional neural network and natural language processing models with a variety of heterogeneous genomic datasets. Mainly, recurrent neural network models outperform convolutional neural network models in terms of prediction accuracy, overfitting and transferability across the datasets under study.
Conclusions
While the perspective of a robust out-of-the-box neural network model is out of reach, we identify certain model characteristics that translate well across datasets and could serve as a baseline model for translational researchers.
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Ninomiya S. High-throughput field crop phenotyping: current status and challenges. BREEDING SCIENCE 2022; 72:3-18. [PMID: 36045897 PMCID: PMC8987842 DOI: 10.1270/jsbbs.21069] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 12/16/2021] [Indexed: 05/03/2023]
Abstract
In contrast to the rapid advances made in plant genotyping, plant phenotyping is considered a bottleneck in plant science. This has promoted high-throughput plant phenotyping (HTP) studies, resulting in an exponential increase in phenotyping-related publications. The development of HTP was originally intended for use as indoor HTP technologies for model plant species under controlled environments. However, this subsequently shifted to HTP for use in crops in fields. Although HTP in fields is much more difficult to conduct due to unstable environmental conditions compared to HTP in controlled environments, recent advances in HTP technology have allowed these difficulties to be overcome, allowing for rapid, efficient, non-destructive, non-invasive, quantitative, repeatable, and objective phenotyping. Recent HTP developments have been accelerated by the advances in data analysis, sensors, and robot technologies, including machine learning, image analysis, three dimensional (3D) reconstruction, image sensors, laser sensors, environmental sensors, and drones, along with high-speed computational resources. This article provides an overview of recent HTP technologies, focusing mainly on canopy-based phenotypes of major crops, such as canopy height, canopy coverage, canopy biomass, and canopy stressed appearance, in addition to crop organ detection and counting in the fields. Current topics in field HTP are also presented, followed by a discussion on the low rates of adoption of HTP in practical breeding programs.
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Affiliation(s)
- Seishi Ninomiya
- Graduate School of Agriculture and Life Sciences, The University of Tokyo, Nishitokyo, Tokyo 188-0002, Japan
- Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, China
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13
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Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors. REMOTE SENSING 2021. [DOI: 10.3390/rs13224562] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). However, the correlation between the severity of the yellow leaf disease of arecanut and LVV must be demonstrated through research. Therefore, this study aims to apply the multispectral data obtained by the UAV along with the high-resolution UAV remote sensing images to obtain five vegetation indexes such as the normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), leaf chlorophyll index (LCI), green normalized difference vegetation index (GNDVI), and normalized difference red edge (NDRE) index, and establish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut, which is expressed by the proportion of the yellowing area of a single areca crown (in percentage). The traditional qualitative expression of this disease is transformed into the quantitative expression of the yellow leaf disease of arecanut per plant. The results demonstrate that the classification accuracy of the test set of the BPNN algorithm and SVM algorithm is the highest, at 86.57% and 86.30%, respectively. Additionally, the UAV structure from motion technology is used to measure the LVV of a single areca tree and establish a model of the correlation between the LVV and the severity of the yellow leaf disease of arecanut. The results show that the relative root mean square error is between 34.763% and 39.324%. This study presents the novel quantitative expression of the severity of the yellow leaf disease of arecanut, along with the correlation between the LVV of areca and the severity of the yellow leaf disease of arecanut. Significant development is expected in the degree of integration of multispectral software and hardware, observation accuracy, and ease of use of UAVs owing to the rapid progress of spectral sensing technology and the image processing and analysis algorithms.
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14
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A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images. SENSORS 2021; 21:s21196540. [PMID: 34640873 PMCID: PMC8513082 DOI: 10.3390/s21196540] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/24/2021] [Accepted: 09/26/2021] [Indexed: 11/17/2022]
Abstract
Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identifying wheat yellow rust from unmanned aerial vehicle (UAV) images. The method was based on the pyramid scene parsing network (PSPNet) semantic segmentation model to classify healthy wheat, yellow rust wheat, and bare soil in small-scale UAV images, and to investigate the spatial generalization of the model. In addition, it was proposed to use the high-accuracy classification results of traditional algorithms as weak samples for wheat yellow rust identification. The recognition accuracy of the PSPNet model in this study reached 98%. On this basis, this study used the trained semantic segmentation model to recognize another wheat field. The results showed that the method had certain generalization ability, and its accuracy reached 98%. In addition, the high-accuracy classification result of a support vector machine was used as a weak label by weak supervision, which better solved the labeling problem of large-size images, and the final recognition accuracy reached 94%. Therefore, the present study method facilitated timely control measures to reduce economic losses.
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15
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Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research. REMOTE SENSING 2021. [DOI: 10.3390/rs13132486] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Crop diseases constitute a serious issue in agriculture, affecting both quality and quantity of agriculture production. Disease control has been a research object in many scientific and technologic domains. Technological advances in sensors, data storage, computing resources and artificial intelligence have shown enormous potential to control diseases effectively. A growing body of literature recognizes the importance of using data from different types of sensors and machine learning approaches to build models for detection, prediction, analysis, assessment, etc. However, the increasing number and diversity of research studies requires a literature review for further developments and contributions in this area. This paper reviews state-of-the-art machine learning methods that use different data sources, applied to plant disease detection. It lists traditional and deep learning methods associated with the main data acquisition modalities, namely IoT, ground imaging, unmanned aerial vehicle imaging and satellite imaging. In addition, this study examines the role of data fusion for ongoing research in the context of disease detection. It highlights the advantage of intelligent data fusion techniques, from heterogeneous data sources, to improve plant health status prediction and presents the main challenges facing this field. The study concludes with a discussion of several current issues and research trends.
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Guo W, Carroll ME, Singh A, Swetnam TL, Merchant N, Sarkar S, Singh AK, Ganapathysubramanian B. UAS-Based Plant Phenotyping for Research and Breeding Applications. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9840192. [PMID: 34195621 PMCID: PMC8214361 DOI: 10.34133/2021/9840192] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 04/29/2021] [Indexed: 05/19/2023]
Abstract
Unmanned aircraft system (UAS) is a particularly powerful tool for plant phenotyping, due to reasonable cost of procurement and deployment, ease and flexibility for control and operation, ability to reconfigure sensor payloads to diversify sensing, and the ability to seamlessly fit into a larger connected phenotyping network. These advantages have expanded the use of UAS-based plant phenotyping approach in research and breeding applications. This paper reviews the state of the art in the deployment, collection, curation, storage, and analysis of data from UAS-based phenotyping platforms. We discuss pressing technical challenges, identify future trends in UAS-based phenotyping that the plant research community should be aware of, and pinpoint key plant science and agronomic questions that can be resolved with the next generation of UAS-based imaging modalities and associated data analysis pipelines. This review provides a broad account of the state of the art in UAS-based phenotyping to reduce the barrier to entry to plant science practitioners interested in deploying this imaging modality for phenotyping in plant breeding and research areas.
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Affiliation(s)
- Wei Guo
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Japan
| | | | - Arti Singh
- Department of Agronomy, Iowa State University, Ames, Iowa, USA
| | | | - Nirav Merchant
- Data Science Institute, University of Arizona, Tucson, USA
| | - Soumik Sarkar
- Department of Mechanical Engineering, Iowa State University, Ames, Iowa, USA
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17
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Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases. REMOTE SENSING 2021. [DOI: 10.3390/rs13071238] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.
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Gao J, Westergaard JC, Sundmark EHR, Bagge M, Liljeroth E, Alexandersson E. Automatic late blight lesion recognition and severity quantification based on field imagery of diverse potato genotypes by deep learning. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106723] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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19
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Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms. BIG DATA AND COGNITIVE COMPUTING 2021. [DOI: 10.3390/bdcc5010002] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Every year, plant diseases cause a significant loss of valuable food crops around the world. The plant and crop disease management practice implemented in order to mitigate damages have changed considerably. Today, through the application of new information and communication technologies, it is possible to predict the onset or change in the severity of diseases using modern big data analysis techniques. In this paper, we present an analysis and classification of research studies conducted over the past decade that forecast the onset of disease at a pre-symptomatic stage (i.e., symptoms not visible to the naked eye) or at an early stage. We examine the specific approaches and methods adopted, pre-processing techniques and data used, performance metrics, and expected results, highlighting the issues encountered. The results of the study reveal that this practice is still in its infancy and that many barriers need to be overcome.
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20
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A High-Resolution Spatial and Time-Series Labeled Unmanned Aerial Vehicle Image Dataset for Middle-Season Rice. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2020. [DOI: 10.3390/ijgi9120728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The existing remote sensing image datasets target the identification of objects, features, or man-made targets but lack the ability to provide the date and spatial information for the same feature in the time-series images. The spatial and temporal information is important for machine learning methods so that networks can be trained to support precision classification, particularly for agricultural applications of specific crops with distinct phenological growth stages. In this paper, we built a high-resolution unmanned aerial vehicle (UAV) image dataset for middle-season rice. We scheduled the UAV data acquisition in five villages of Hubei Province for three years, including 11 or 13 growing stages in each year that were accompanied by the annual agricultural surveying business. We investigated the accuracy of the vector maps for each field block and the precise information regarding the crops in the field by surveying each village and periodically arranging the UAV flight tasks on a weekly basis during the phenological stages. Subsequently, we developed a method to generate the samples automatically. Finally, we built a high-resolution UAV image dataset, including over 500,000 samples with the location and phenological growth stage information, and employed the imagery dataset in several machine learning algorithms for classification. We performed two exams to test our dataset. First, we used four classical deep learning networks for the fine classification of spatial and temporal information. Second, we used typical models to test the land cover on our dataset and compared this with the UCMerced Land Use Dataset and RSSCN7 Dataset. The results showed that the proposed image dataset supported typical deep learning networks in the classification task to identify the location and time of middle-season rice and achieved high accuracy with the public image dataset.
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21
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Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing. REMOTE SENSING 2020. [DOI: 10.3390/rs12142194] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Eight years after the first record in Italy, Kiwifruit Decline (KD), a destructive disease causing root rot, has already affected more than 25% of the area under kiwifruit cultivation in Italy. Diseased plants are characterised by severe decay of the fine roots and sudden wilting of the canopy, which is only visible after the season’s first period of heat (July–August). The swiftness of symptom appearance prevents correct timing and positioning for sampling of the disease, and is therefore a barrier to aetiological studies. The aim of this study is to test the feasibility of thermal and multispectral imaging for the detection of KD using an unsupervised classifier. Thus, RGB, multispectral and thermal data from a kiwifruit orchard, with healthy and diseased plants, were acquired simultaneously during two consecutive growing seasons (2017–2018) using an Unmanned Aerial Vehicle (UAV) platform. Data reduction was applied to the clipped areas of the multispectral and thermal data from the 2017 survey. Reduced data were then classified with two unsupervised algorithms, a K-means and a hierarchical method. The plant vigour (canopy size and presence/absence of wilted leaves) and the health shifts exhibited by asymptomatic plants between 2017 and 2018 were evaluated from RGB data via expert assessment and used as the ground truth for cluster interpretation. Multispectral data showed a high correlation with plant vigour, while temperature data demonstrated a good potential use in predicting health shifts, especially in highly vigorous plants that were asymptomatic in 2017 and became symptomatic in 2018. The accuracy of plant vigour assessment was above 73% when using multispectral data, while clustering of the temperature data allowed the prediction of disease outbreak one year in advance, with an accuracy of 71%. Based on our results, the unsupervised clustering of remote sensing data could be a reliable tool for the identification of sampling areas, and can greatly improve aetiological studies of this new disease in kiwifruit.
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Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”. REMOTE SENSING 2020. [DOI: 10.3390/rs12060940] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
High-throughput crop phenotyping is harnessing the potential of genomic resources for the genetic improvement of crop production under changing climate conditions. As global food security is not yet assured, crop phenotyping has received increased attention during the past decade. This spectral issue (SI) collects 30 papers reporting research on estimation of crop phenotyping traits using unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV) imagery. Such platforms were previously not widely available. The special issue includes papers presenting recent advances in the field, with 22 UAV-based papers and 12 UGV-based articles. The special issue covers 16 RGB sensor papers, 11 papers on multi-spectral imagery, and further 4 papers on hyperspectral and 3D data acquisition systems. A total of 13 plants’ phenotyping traits, including morphological, structural, and biochemical traits are covered. Twenty different data processing and machine learning methods are presented. In this way, the special issue provides a good overview regarding potential applications of the platforms and sensors, to timely provide crop phenotyping traits in a cost-efficient and objective manner. With the fast development of sensors technology and image processing algorithms, we expect that the estimation of crop phenotyping traits supporting crop breeding scientists will gain even more attention in the future.
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Carmona SL, Burbano-David D, Gómez MR, Lopez W, Ceballos N, Castaño-Zapata J, Simbaqueba J, Soto-Suárez M. Characterization of Pathogenic and Nonpathogenic Fusarium oxysporum Isolates Associated with Commercial Tomato Crops in the Andean Region of Colombia. Pathogens 2020; 9:E70. [PMID: 31968574 PMCID: PMC7168637 DOI: 10.3390/pathogens9010070] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 01/07/2020] [Accepted: 01/15/2020] [Indexed: 01/03/2023] Open
Abstract
In Colombia, tomato production under protected conditions represents an important economic contribution to the agricultural sector. Fusarium wilt diseases, caused by pathogenic formae speciales of the soil-borne fungus Fusarium oxysporum Schltdl., cause significant yield losses in tomatoes throughout the world. Investigation of the F. oxysporum-tomato pathosystem in Colombia is required to develop appropriate alternative disease management. In this study, 120 fungal isolates were obtained from four different departments in the Central Andean Region in Colombia from tomato crops with symptoms of wilt disease. A molecular characterization of the fungal isolates was performed using the SIX1, SIX3, and SIX4 effector genes of Fusarium oxysporum f. sp. lycopersici W.C. Snyder & H.N. Hansen (Fol). Additionally, we developed a new specific marker to distinguish between Fusarium oxysporum f. sp. radicis-lycopersici Jarvis & Shoemaker (Forl) and Fol isolates. Furthermore, a phylogenetic analysis using the Translation Elongation Factor 1-alpha (EF1a) gene was performed with the collected isolates. Two isolates (named Fol59 and Fol-UDC10) were identified as Fol race 2, four isolates were identified as Forl, six isolates were identified as F. solani, and most of the isolates were grouped within the F. oxysporum species complex. The phylogenetic tree of EF1a showed that most of the isolates could potentially correspond to nonpathogenic strains of F. oxysporum. Additional pathogenicity assays carried out with Fol59 and Fol-UDC10 confirmed that both isolates were highly virulent strains. This study represents a contribution to the understanding of the local interaction between tomatoes and F. oxysporum in Colombia.
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Affiliation(s)
- Sandra L. Carmona
- Corporación Colombiana de Investigación Agropecuaria. AGROSAVIA, Km 14 vía Mosquera-Bogotá, Mosquera 250047, Colombia; (S.L.C.); (D.B.-D.); (M.R.G.); (J.S.)
| | - Diana Burbano-David
- Corporación Colombiana de Investigación Agropecuaria. AGROSAVIA, Km 14 vía Mosquera-Bogotá, Mosquera 250047, Colombia; (S.L.C.); (D.B.-D.); (M.R.G.); (J.S.)
| | - Magda R. Gómez
- Corporación Colombiana de Investigación Agropecuaria. AGROSAVIA, Km 14 vía Mosquera-Bogotá, Mosquera 250047, Colombia; (S.L.C.); (D.B.-D.); (M.R.G.); (J.S.)
| | - Walter Lopez
- Departamento de Física y Química, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Colombia sede Manizales, Manizales 170004, Colombia;
| | - Nelson Ceballos
- Facultad de Ciencias Agropecuarias, Universidad de Caldas, Manizales 170004, Colombia; (N.C.); (J.C.-Z.)
| | - Jairo Castaño-Zapata
- Facultad de Ciencias Agropecuarias, Universidad de Caldas, Manizales 170004, Colombia; (N.C.); (J.C.-Z.)
| | - Jaime Simbaqueba
- Corporación Colombiana de Investigación Agropecuaria. AGROSAVIA, Km 14 vía Mosquera-Bogotá, Mosquera 250047, Colombia; (S.L.C.); (D.B.-D.); (M.R.G.); (J.S.)
| | - Mauricio Soto-Suárez
- Corporación Colombiana de Investigación Agropecuaria. AGROSAVIA, Km 14 vía Mosquera-Bogotá, Mosquera 250047, Colombia; (S.L.C.); (D.B.-D.); (M.R.G.); (J.S.)
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24
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Hyperspectral Measurements Enable Pre-Symptomatic Detection and Differentiation of Contrasting Physiological Effects of Late Blight and Early Blight in Potato. REMOTE SENSING 2020. [DOI: 10.3390/rs12020286] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
In-vivo foliar spectroscopy, also known as contact hyperspectral reflectance, enables rapid and non-destructive characterization of plant physiological status. This can be used to assess pathogen impact on plant condition both prior to and after visual symptoms appear. Challenging this capacity is the fact that dead tissue yields relatively consistent changes in leaf optical properties, negatively impacting our ability to distinguish causal pathogen identity. Here, we used in-situ spectroscopy to detect and differentiate Phytophthora infestans (late blight) and Alternaria solani (early blight) on potato foliage over the course of disease development and explored non-destructive characterization of contrasting disease physiology. Phytophthora infestans, a hemibiotrophic pathogen, undergoes an obligate latent period of two–seven days before disease symptoms appear. In contrast, A. solani, a necrotrophic pathogen, causes symptoms to appear almost immediately when environmental conditions are conducive. We found that respective patterns of spectral change can be related to these differences in underlying disease physiology and their contrasting pathogen lifestyles. Hyperspectral measurements could distinguish both P. infestans-infected and A. solani-infected plants with greater than 80% accuracy two–four days before visible symptoms appeared. Individual disease development stages for each pathogen could be differentiated from respective controls with 89–95% accuracy. Notably, we could distinguish latent P. infestans infection from both latent and symptomatic A. solani infection with greater than 75% accuracy. Spectral features important for late blight detection shifted over the course of infection, whereas spectral features important for early blight detection remained consistent, reflecting their different respective pathogen biologies. Shortwave infrared wavelengths were important for differentiation between healthy and diseased, and between pathogen infections, both pre- and post-symptomatically. This proof-of-concept work supports the use of spectroscopic systems as precision agriculture tools for rapid and early disease detection and differentiation tools, and highlights the importance of careful consideration of underlying pathogen biology and disease physiology for crop disease remote sensing.
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Handayani T, Gilani SA, Watanabe KN. Climatic changes and potatoes: How can we cope with the abiotic stresses? BREEDING SCIENCE 2019; 69:545-563. [PMID: 31988619 PMCID: PMC6977456 DOI: 10.1270/jsbbs.19070] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 09/10/2019] [Indexed: 05/06/2023]
Abstract
Climate change triggers increases in temperature, drought, and/or salinity that threaten potato production, because they necessitate specific amounts and quality of water, meanwhile lower temperatures generally support stable crop yields. Various cultivation techniques have been developed to reduce the negative effects of drought, heat and/or salinity stresses on potato. Developing innovative varieties with relevant tolerance to abiotic stress is absolutely necessary to guarantee competitive production under sub-optimal environments. Commercial varieties are sensitive to abiotic stresses, and substantial changes to their higher tolerance levels are not easily achieved because their genetic base is narrow. Nonetheless, there are several other possibilities for genetic enhancement using landraces and wild relatives. The complexity of polysomic genetics and heterozygosity in potato hamper the phenotype evaluation over abiotic stresses and consequent conventional introgression of tolerance traits, which are more challenging than previous successes shown over diseases and insects resistances. Today, potatoes face more challenges with severe abiotic stresses. Potato wild relatives can be explored further using innovative genomic, transcriptomic, proteomic, and metabolomic approaches. At the field level, appropriate cultivation techniques must be applied along with precision farming technology and tolerant varieties developed from various breeding techniques, in order to realize high yield under multiple stresses.
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Affiliation(s)
- Tri Handayani
- Graduate School of Life & Environmental Sciences, University of Tsukuba,
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572,
Japan
- Indonesian Vegetable Research Institute,
Jl. Tangkuban Perahu 517, Lembang, West Bandung, West Java, 40391,
Indonesia
| | - Syed Abdullah Gilani
- Department of Biological Sciences and Chemistry, University of Nizwa,
P. O. Box 33, PC 616, Birkat Al Mouz, Nizwa,
Sultanate of Oman
| | - Kazuo N. Watanabe
- Tsukuba-Plant Innovation Research Center, University of Tsukuba,
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572,
Japan
- Corresponding author (e-mail: )
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26
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Machine Learning-Based Spectral Library for Crop Classification and Status Monitoring. AGRONOMY-BASEL 2019. [DOI: 10.3390/agronomy9090496] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The establishment and application of a spectral library is a critical step in the standardization and automation of remote sensing interpretation and mapping. Currently, most spectral libraries are designed to support the classification of land cover types, whereas few are dedicated to agricultural remote sensing monitoring. Here, we gathered spectral observation data on plants in multiple experimental scenarios into a spectral database to investigate methods for crop classification (16 crop species) and status monitoring (tea plant and rice growth). We proposed a set of screening methods for spectral features related to plant classification and status monitoring (band reflectance, vegetation index, spectral differentiation, spectral continuum characteristics) that are based on ISODATA and JM distance. Next, we investigated the performance of different machine learning classifiers in the spectral library application, including K-nearest neighbor (KNN), Random Forest (RF), and a genetic algorithm coupled with a support vector machine (GA-SVM). The optimal combination of spectral features and the classifier with the highest classification accuracy were selected for crop classification and status monitoring scenarios. The GA-SVM classifier performed the best, which produced an accuracy of OAA = 0.94, Kappa = 0.93 for crop classification in a complex scenario (crops mixed with 71 non-crop plant species), and promising accuracies for tea plant growth monitoring (OAA = 0.98, Kappa = 0.97) and rice growth stage monitoring (OAA = 0.92, Kappa = 0.90). Therefore, the establishment of a plant spectral library combined with relevant feature extraction and a classification algorithm effectively supports agricultural monitoring by remote sensing.
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Potato Yield Prediction Using Machine Learning Techniques and Sentinel 2 Data. REMOTE SENSING 2019. [DOI: 10.3390/rs11151745] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Traditional potato growth models evidence certain limitations, such as the cost of obtaining the input data required to run the models, the lack of spatial information in some instances, or the actual quality of input data. In order to address these issues, we develop a model to predict potato yield using satellite remote sensing. In an effort to offer a good predictive model that improves the state of the art on potato precision agriculture, we use images from the twin Sentinel 2 satellites (European Space Agency—Copernicus Programme) over three growing seasons, applying different machine learning models. First, we fitted nine machine learning algorithms with various pre-processing scenarios using variables from July, August and September based on the red, red-edge and infra-red bands of the spectrum. Second, we selected the best performing models and evaluated them against independent test data. Finally, we repeated the previous two steps using only variables corresponding to July and August. Our results showed that the feature selection step proved vital during data pre-processing in order to reduce multicollinearity among predictors. The Regression Quantile Lasso model (11.67% Root Mean Square Error, RMSE; R2 = 0.88 and 9.18% Mean Absolute Error, MAE) and Leap Backwards model (10.94% RMSE, R2 = 0.89 and 8.95% MAE) performed better when predictors with a correlation coefficient > 0.5 were removed from the dataset. In contrast, the Support Vector Machine Radial (svmRadial) performed better with no feature selection method (11.7% RMSE, R2 = 0.93 and 8.64% MAE). In addition, we used a random forest model to predict potato yields in Castilla y León (Spain) 1–2 months prior to harvest, and obtained satisfactory results (11.16% RMSE, R2 = 0.89 and 8.71% MAE). These results demonstrate the suitability of our models to predict potato yields in the region studied.
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Marzougui A, Ma Y, Zhang C, McGee RJ, Coyne CJ, Main D, Sankaran S. Advanced Imaging for Quantitative Evaluation of Aphanomyces Root Rot Resistance in Lentil. FRONTIERS IN PLANT SCIENCE 2019; 10:383. [PMID: 31057562 PMCID: PMC6477098 DOI: 10.3389/fpls.2019.00383] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 03/13/2019] [Indexed: 05/08/2023]
Abstract
Aphanomyces root rot (ARR) is a soil-borne disease that results in severe yield losses in lentil. The development of resistant cultivars is one of the key strategies to control this pathogen. However, the evaluation of disease severity is limited to visual scores that can be subjective. This study utilized image-based phenotyping approaches to evaluate Aphanomyces euteiches resistance in lentil genotypes in greenhouse (351 genotypes from lentil single plant/LSP derived collection and 191 genotypes from recombinant inbred lines/RIL using digital Red-Green-Blue/RGB and hyperspectral imaging) and field (173 RIL genotypes using unmanned aerial system-based multispectral imaging) conditions. Moderate to strong correlations were observed between RGB, multispectral, and hyperspectral derived features extracted from lentil shoots/roots and visual scores. In general, root features extracted from RGB imaging were found to be strongly associated with disease severity. With only three root traits, elastic net regression model was able to predict disease severity across and within multiple datasets (R 2 = 0.45-0.73 and RMSE = 0.66-1.00). The selected features could represent visual disease scores. Moreover, we developed twelve normalized difference spectral indices (NDSIs) that were significantly correlated with disease scores: two NDSIs for lentil shoot section - computed from wavelengths of 1170, 1160, 1270, and 1280 nm (0.12 ≤ |r| ≤ 0.24, P < 0.05) and ten NDSIs for lentil root sections - computed from wavelengths in the range of 630-670, 700-840, and 1320-1530 nm (0.10 ≤ |r| ≤ 0.50, P < 0.05). Root-derived NDSIs were more accurate in predicting disease scores with an R 2 of 0.54 (RMSE = 0.86), especially when the model was trained and tested on LSP accessions, compared to R 2 of 0.25 (RMSE = 1.64) when LSP and RIL genotypes were used as train and test datasets, respectively. Importantly, NDSIs - computed from wavelengths of 700, 710, 730, and 790 nm - had strong positive correlations with disease scores (0.35 ≤r ≤ 0.50, P < 0.0001), which was confirmed in field phenotyping with similar correlations using vegetation index with red edge wavelength (normalized difference red edge, 0.36 ≤ |r| ≤ 0.57, P < 0.0001). The adopted image-based phenotyping approaches can help plant breeders to objectively quantify ARR resistance and reduce the subjectivity in selecting potential genotypes.
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Affiliation(s)
- Afef Marzougui
- Department of Biological Systems Engineering, Washington State University, Pullman, WA, United States
| | - Yu Ma
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Chongyuan Zhang
- Department of Biological Systems Engineering, Washington State University, Pullman, WA, United States
| | - Rebecca J. McGee
- United States Department of Agriculture-Agricultural Research Service, Grain Legume Genetics and Physiology Research Unit, Washington State University, Pullman, WA, United States
| | - Clarice J. Coyne
- United States Department of Agriculture-Agricultural Research Service, Plant Germplasm Introduction and Testing Unit, Washington State University, Pullman, WA, United States
| | - Dorrie Main
- Department of Horticulture, Washington State University, Pullman, WA, United States
| | - Sindhuja Sankaran
- Department of Biological Systems Engineering, Washington State University, Pullman, WA, United States
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Robinia pseudoacacia L. in Short Rotation Coppice: Seed and Stump Shoot Reproduction as well as UAS-based Spreading Analysis. FORESTS 2019. [DOI: 10.3390/f10030235] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Varying reproduction strategies are an important trait that tree species need in order both to survive and to spread. Black locust is able to reproduce via seeds, stump shoots, and root suckers. However, little research has been conducted on the reproduction and spreading of black locust in short rotation coppices. This research study focused on seed germination, stump shoot resprout, and spreading by root suckering of black locust in ten short rotation coppices in Germany. Seed experiments and sample plots were analyzed for the study. Spreading was detected and measured with unmanned aerial system (UAS)-based images and classification technology—object-based image analysis (OBIA). Additionally, the classification of single UAS images was tested by applying a convolutional neural network (CNN), a deep learning model. The analyses showed that seed germination increases with increasing warm-cold variety and scarification. Moreover, it was found that the number of shoots per stump decreases as shoot age increases. Furthermore, spreading increases with greater light availability and decreasing tillage. The OBIA and CNN image analysis technologies achieved 97% and 99.5% accuracy for black locust classification in UAS images. All in all, the three reproduction strategies of black locust in short rotation coppices differ with regards to initialization, intensity, and growth performance, but all play a role in the survival and spreading of black locust.
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Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in Potato. REMOTE SENSING 2019. [DOI: 10.3390/rs11030224] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Assessment of disease incidence and severity at farm scale or in agronomic trials is frequently performed based on visual crop inspection, which is a labor intensive task prone to errors associated with its subjectivity. Therefore, alternative methods to relate disease incidence and severity with changes in crop traits are of great interest. Optical imagery in the visible and near-infrared (Vis-NIR) can potentially be used to detect changes in crop traits caused by pathogen development. Also, cameras on-board of Unmanned Aerial Vehicles (UAVs) have flexible data collection capabilities allowing adjustments considering the trade-off between data throughput and its resolution. However, studies focusing on the use of UAV imagery to describe changes in crop traits related to disease infection are still lacking. More specifically, evaluation of late blight (Phytophthora infestans) incidence in potato concerning early discrimination of different disease severity levels has not been extensively reported. In this article, the description of spectral changes related to the development of potato late blight under low disease severity levels is performed using sub-decimeter UAV optical imagery. The main objective was to evaluate the sensitivity of the data acquired regarding early changes in crop traits related to disease incidence. For that, UAV images were acquired on four dates during the growing season (from 37 to 78 days after planting), before and after late blight was detected in the field. The spectral variability observed in each date was summarized using Simplex Volume Maximization (SiVM), and its relationship with experimental treatments (different crop systems) and disease severity levels (evaluated by visual assessment) was determined based on pixel-wise log-likelihood ratio (LLR) calculation. Using this analytical framework it was possible to identify considerable spectral changes related to late blight incidence in different treatments and also to disease severity level as low as between 2.5 and 5.0% of affected leaf area. Comparison of disease incidence and spectral information acquired using UAV (with 4-5 cm of spatial resolution) and ground-based imagery (with 0.1-0.2 cm of spatial resolution) indicate that UAV data allowed identification of patterns comparable to those described by ground-based images, despite some differences concerning the distribution of affected areas detected within the sampling units and an attenuation in the signal measured. Finally, although aggregated information at sampling unit level provided discriminative potential for higher levels of disease development, focusing on spectral information related to disease occurrence increased the discriminative potential of the data acquired.
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