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Ma H, Zhang J, Huang W, Ruan C, Chen D, Zhang H, Zhou X, Gui Z. Monitoring yellow rust progression during spring critical wheat growth periods using multi-temporal Sentinel-2 imagery. PEST MANAGEMENT SCIENCE 2024. [PMID: 39139028 DOI: 10.1002/ps.8336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 07/03/2024] [Accepted: 07/12/2024] [Indexed: 08/15/2024]
Abstract
BACKGROUND Yellow rust (Puccinia striiformis f. sp. tritici) is a devastating hazard to wheat production, which poses a serious threat to yield and food security in the main wheat-producing areas in eastern China. It is necessary to monitor yellow rust progression during spring critical wheat growth periods to support its prediction by providing timely calibrations for disease prediction models and timely green prevention and control. RESULTS Three Sentinel-2 images for the disease during the three wheat growth periods (jointing, heading, and filling) were acquired. Spectral, texture, and color features were all extracted for each growth period disease. Then three period-specific feature sets were obtained. Given the differences in field disease epidemic status in the three periods, three period-targeted monitoring models were established to map yellow rust damage progression in spring and track its spatiotemporal change. The models' performance was then validated based on the disease field truth data during the three periods (87 for the jointing period, 183 for the heading period, and 155 for the filling period). The validation results revealed that the representation of the wheat yellow rust damage progression based on our monitoring model group was realistic and credible. The overall accuracy of the healthy and diseased pixel classification monitoring model at the jointing period reached 87.4%, and the coefficient of determination (R2) of the disease index regression monitoring models at the heading and filling periods was 0.77 (heading period) and 0.76 (filling period). The model-group-result-based spatiotemporal change detection of the yellow rust progression across the entire study area revealed that the area proportions conforming to the expected disease spatiotemporal development pattern during the jointing-to-heading period and the heading-to-filling period reached 98.2% and 84.4% respectively. CONCLUSIONS Our jointing, heading, and filling period-targeted monitoring model group overcomes the limitations of most existing monitoring models only based on single-phase remote sensing information. It performs well in revealing the wheat yellow rust spatiotemporal epidemic in spring, can timely update disease trends to optimize disease management, and provide a basis for disease prediction to timely correct model. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Huiqin Ma
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Jingcheng Zhang
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Wenjiang Huang
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Earth Observation of Hainan Province, Hainan Research Institute, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya, China
| | - Chao Ruan
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
| | - Dongmei Chen
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Hansu Zhang
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
| | - Xianfeng Zhou
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Zhiqin Gui
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
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Feng G, Gu Y, Wang C, Zhou Y, Huang S, Luo B. Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective. PLANTS (BASEL, SWITZERLAND) 2024; 13:1722. [PMID: 38999562 PMCID: PMC11243561 DOI: 10.3390/plants13131722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 07/14/2024]
Abstract
Fusarium head blight (FHB) is a major threat to global wheat production. Recent reviews of wheat FHB focused on pathology or comprehensive prevention and lacked a summary of advanced detection techniques. Unlike traditional detection and management methods, wheat FHB detection based on various imaging technologies has the obvious advantages of a high degree of automation and efficiency. With the rapid development of computer vision and deep learning technology, the number of related research has grown explosively in recent years. This review begins with an overview of wheat FHB epidemic mechanisms and changes in the characteristics of infected wheat. On this basis, the imaging scales are divided into microscopic, medium, submacroscopic, and macroscopic scales. Then, we outline the recent relevant articles, algorithms, and methodologies about wheat FHB from disease detection to qualitative analysis and summarize the potential difficulties in the practicalization of the corresponding technology. This paper could provide researchers with more targeted technical support and breakthrough directions. Additionally, this paper provides an overview of the ideal application mode of the FHB detection technologies based on multi-scale imaging and then examines the development trend of the all-scale detection system, which paved the way for the fusion of non-destructive detection technologies of wheat FHB based on multi-scale imaging.
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Affiliation(s)
- Guoqing Feng
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- College of Agricultural Engineering, Jiangsu University, Zhenjiang 212000, China
| | - Ying Gu
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Cheng Wang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- College of Agricultural Engineering, Jiangsu University, Zhenjiang 212000, China
| | - Yanan Zhou
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Shuo Huang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Bin Luo
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- College of Agricultural Engineering, Jiangsu University, Zhenjiang 212000, China
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3
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Wu Y, Li X, Zhang Q, Zhou X, Qiu H, Wang P. Recognition of spider mite infestations in jujube trees based on spectral-spatial clustering of hyperspectral images from UAVs. FRONTIERS IN PLANT SCIENCE 2023; 14:1078676. [PMID: 36818847 PMCID: PMC9932681 DOI: 10.3389/fpls.2023.1078676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 01/06/2023] [Indexed: 06/18/2023]
Abstract
Spider mite infestations are a serious hazard for jujube trees in China. The use of remote sensing technology to evaluate the health of jujube trees in large-scale intensive agricultural production is an effective means of agricultural control. Hyperspectral remote sensing has a higher spectral resolution and richer spectral information than conventional multispectral remote sensing, which improves the detection of crop pests and diseases. We used hyperspectral remote sensing data from jujube fields infested with spider mite in Hotan Prefecture, Xinjiang to evaluate their use in monitoring this important pest. We fused spectral and spatial information from the hyperspectral images and propose a method of recognizing spider mite infestations of jujube trees. Our method is based on the construction of spectral features, the fusion of spatial information and clustering of these spectral-spatial features. We evaluated the effect of different spectral-spatial features and different clustering methods on the recognition of spider mite in jujube trees. The experimental results showed that the overall accuracy of the method for the recognition of spider mites was >93% and the overall accuracy of the band clustering-density peak clustering model for the recognition of spider mite reached 96.13%. This method can be applied to the control of jujube spider mites in agricultural production.
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Affiliation(s)
- Yue Wu
- College of Information Science and Engineering, Shandong Agricultural University, Taian, China
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Xican Li
- College of Information Science and Engineering, Shandong Agricultural University, Taian, China
| | - Qing Zhang
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Xiaozhen Zhou
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Hongbin Qiu
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Panpan Wang
- Institute of Agricultural Sciences, the 14th Division of Xinjiang Production and Construction Corps, Kunyu, China
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4
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Mustafa G, Zheng H, Li W, Yin Y, Wang Y, Zhou M, Liu P, Bilal M, Jia H, Li G, Cheng T, Tian Y, Cao W, Zhu Y, Yao X. Fusarium head blight monitoring in wheat ears using machine learning and multimodal data from asymptomatic to symptomatic periods. FRONTIERS IN PLANT SCIENCE 2023; 13:1102341. [PMID: 36726669 PMCID: PMC9885105 DOI: 10.3389/fpls.2022.1102341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 12/22/2022] [Indexed: 06/18/2023]
Abstract
The growth of the fusarium head blight (FHB) pathogen at the grain formation stage is a deadly threat to wheat production through disruption of the photosynthetic processes of wheat spikes. Real-time nondestructive and frequent proxy detection approaches are necessary to control pathogen propagation and targeted fungicide application. Therefore, this study examined the ch\lorophyll-related phenotypes or features from spectral and chlorophyll fluorescence for FHB monitoring. A methodology is developed using features extracted from hyperspectral reflectance (HR), chlorophyll fluorescence imaging (CFI), and high-throughput phenotyping (HTP) for asymptomatic to symptomatic disease detection from two consecutive years of experiments. The disease-sensitive features were selected using the Boruta feature-selection algorithm, and subjected to machine learning-sequential floating forward selection (ML-SFFS) for optimum feature combination. The results demonstrated that the biochemical parameters, HR, CFI, and HTP showed consistent alterations during the spike-pathogen interaction. Among the selected disease sensitive features, reciprocal reflectance (RR=1/700) demonstrated the highest coefficient of determination (R 2) of 0.81, with root mean square error (RMSE) of 11.1. The multivariate k-nearest neighbor model outperformed the competing multivariate and univariate models with an overall accuracy of R 2 = 0.92 and RMSE = 10.21. A combination of two to three kinds of features was found optimum for asymptomatic disease detection using ML-SFFS with an average classification accuracy of 87.04% that gradually improved to 95% for a disease severity level of 20%. The study demonstrated the fusion of chlorophyll-related phenotypes with the ML-SFFS might be a good choice for crop disease detection.
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Affiliation(s)
- Ghulam Mustafa
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Hengbiao Zheng
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Wei Li
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Yuming Yin
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Yongqing Wang
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Meng Zhou
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Peng Liu
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Muhammad Bilal
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Haiyan Jia
- Crop Genomics and Bioinformatics Center and National Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Guoqiang Li
- Crop Genomics and Bioinformatics Center and National Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Tao Cheng
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Yongchao Tian
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Weixing Cao
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Yan Zhu
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
| | - Xia Yao
- National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
- National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Ministry of Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China
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5
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Bai X, Fang H, He Y, Zhang J, Tao M, Wu Q, Yang G, Wei Y, Tang Y, Tang L, Lou B, Deng S, Yang Y, Feng X. Dynamic UAV Phenotyping for Rice Disease Resistance Analysis Based on Multisource Data. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0019. [PMID: 37040287 PMCID: PMC10076055 DOI: 10.34133/plantphenomics.0019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 12/09/2022] [Indexed: 05/27/2023]
Abstract
Bacterial blight poses a threat to rice production and food security, which can be controlled through large-scale breeding efforts toward resistant cultivars. Unmanned aerial vehicle (UAV) remote sensing provides an alternative means for the infield phenotype evaluation of crop disease resistance to relatively time-consuming and laborious traditional methods. However, the quality of data acquired by UAV can be affected by several factors such as weather, crop growth period, and geographical location, which can limit their utility for the detection of crop disease and resistant phenotypes. Therefore, a more effective use of UAV data for crop disease phenotype analysis is required. In this paper, we used time series UAV remote sensing data together with accumulated temperature data to train the rice bacterial blight severity evaluation model. The best results obtained with the predictive model showed an R p 2 of 0.86 with an RMSEp of 0.65. Moreover, model updating strategy was used to explore the scalability of the established model in different geographical locations. Twenty percent of transferred data for model training was useful for the evaluation of disease severity over different sites. In addition, the method for phenotypic analysis of rice disease we built here was combined with quantitative trait loci (QTL) analysis to identify resistance QTL in genetic populations at different growth stages. Three new QTLs were identified, and QTLs identified at different growth stages were inconsistent. QTL analysis combined with UAV high-throughput phenotyping provides new ideas for accelerating disease resistance breeding.
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Affiliation(s)
- Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Hui Fang
- Huzhou Institute of Zhejiang University, Huzhou 313000, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Jinnuo Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Mingzhu Tao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Qingguan Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Guofeng Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Yuzhen Wei
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Yu Tang
- Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou 510665, China
| | - Lie Tang
- Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011-3270, USA
| | - Binggan Lou
- College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
| | - Shuiguang Deng
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, 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 31002, China
| | - Xuping Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
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6
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Xu P, Sun W, Xu K, Zhang Y, Tan Q, Qing Y, Yang R. Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning. Foods 2022; 12:foods12010144. [PMID: 36613360 PMCID: PMC9818215 DOI: 10.3390/foods12010144] [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: 11/14/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 12/29/2022] Open
Abstract
Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hyperspectral imaging (HSI) technology combined with deep learning. Raw spectra collected from maize seeds (200 each healthy and worm-eaten) were pre-processed using detrending (DE) and multiple scattering correction (MSC) to highlight the spectral differences between samples. A convolutional neural network architecture (CNN-FES) based on a feature selection mechanism was proposed according to the importance of wavelength in the target classification task. The results show that the subset of 24 feature wavelengths selected by the proposed CNN-FES can capture important feature information in the spectral data more effectively than the conventional successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithms. In addition, a convolutional neural network architecture (CNN-ATM) based on an attentional classification mechanism was designed for one-dimensional spectral data classification and compared with three commonly used machine learning methods, linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). The results show that the classification performance of the designed CNN-ATM on the full wavelength does not differ much from the above three methods, and the classification accuracy is above 90% on both the training and test sets. Meanwhile, the accuracy, sensitivity, and specificity of CNN-ATM based on feature wavelength modeling can reach up to 97.50%, 98.28%, and 96.77% at the highest, respectively. The study shows that hyperspectral imaging-based defect detection of maize seed is feasible and effective, and the proposed method has great potential for the processing and analysis of complex hyperspectral data.
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Affiliation(s)
- Peng Xu
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
| | - Wenbin Sun
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
| | - Kang Xu
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
| | - Yunpeng Zhang
- College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
| | - Qian Tan
- College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
| | - Yiren Qing
- College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
| | - Ranbing Yang
- College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
- Correspondence: ; Tel.: +86-0898-66267576
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Zhang F, Shi L, Xin L, Li L, Tian L, Cui X, Gao Y. Study on drying characteristics and moisture content prediction model of Panax notoginseng taproot by using segmented drying of microwave vacuum combined with hot air. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Fujie Zhang
- Faculty of Modern Agriculture Engineering Kunming University of Science and Technology Kunming China
| | - Lei Shi
- Faculty of Modern Agriculture Engineering Kunming University of Science and Technology Kunming China
| | - Lidong Xin
- Faculty of Modern Agriculture Engineering Kunming University of Science and Technology Kunming China
| | - Lixia Li
- Faculty of Modern Agriculture Engineering Kunming University of Science and Technology Kunming China
- Key Laboratory of Crop Harvesting Equipment Technology of Zhejiang Province Jinhua China
| | - Liquan Tian
- Key Laboratory of Crop Harvesting Equipment Technology of Zhejiang Province Jinhua China
| | - Xiuming Cui
- Yunnan Key Laboratory of Sustainable Utilization of Panax Notoginseng Kunming University of Science and Technology Kunming China
| | - Yongping Gao
- Yixintang Pharmaceutical Group Ltd. Kunming China
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8
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Zhang H, Huang L, Huang W, Dong Y, Weng S, Zhao J, Ma H, Liu L. Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion. FRONTIERS IN PLANT SCIENCE 2022; 13:1004427. [PMID: 36212329 PMCID: PMC9535335 DOI: 10.3389/fpls.2022.1004427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 08/29/2022] [Indexed: 06/16/2023]
Abstract
Infection caused by Fusarium head blight (FHB) has severely damaged the quality and yield of wheat in China and threatened the health of humans and livestock. Inaccurate disease detection increases the use cost of pesticide and pollutes farmland, highlighting the need for FHB detection in wheat fields. The combination of spectral and spatial information provided by image analysis facilitates the detection of infection-related damage in crops. In this study, an effective detection method for wheat FHB based on unmanned aerial vehicle (UAV) hyperspectral images was explored by fusing spectral features and image features. Spectral features mainly refer to band features, and image features mainly include texture and color features. Our aim was to explain all aspects of wheat infection through multi-class feature fusion and to find the best FHB detection method for field wheat combining current advanced algorithms. We first evaluated the quality of the two acquired UAV images and eliminated the excessively noisy bands in the images. Then, the spectral features, texture features, and color features in the images were extracted. The random forest (RF) algorithm was used to optimize features, and the importance value of the features determined whether the features were retained. Feature combinations included spectral features, spectral and texture features fusion, and the fusion of spectral, texture, and color features to combine support vector machine, RF, and back propagation neural network in constructing wheat FHB detection models. The results showed that the model based on the fusion of spectral, texture, and color features using the RF algorithm achieved the best performance, with a prediction accuracy of 85%. The method proposed in this study may provide an effective way of FHB detection in field wheat.
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Affiliation(s)
- Hansu Zhang
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
| | - Linsheng Huang
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
| | - Wenjiang Huang
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Key Laboratory for Earth Observation of Hainan Province, Sanya, China
| | - Yingying Dong
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
| | - Jinling Zhao
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
| | - Huiqin Ma
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Linyi Liu
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
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9
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Genetic Diversity and Population Structure of Head Blight Disease Causing Fungus Fusarium graminearum in Northern Wheat Belt of India. J Fungi (Basel) 2022; 8:jof8080820. [PMID: 36012808 PMCID: PMC9409692 DOI: 10.3390/jof8080820] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/22/2022] [Accepted: 07/26/2022] [Indexed: 02/04/2023] Open
Abstract
Head blight or scab caused by Fusarium graminearum (FG), once ranked as a minor disease in wheat, is now emerging as one of the economically important diseases in India. The present study represents the first in-depth population genetic analysis of the FG from the northern wheat belt of India. In this study, multiple conserved gene sequences comprised of β-tubulin (TUB), translation elongation factor 1-α (TEF), and histone-3 (HIS) regions were used for multi-locus phylogenetic analysis of 123 geographically distinct F. graminearum isolates collected from four different states (Haryana (HR), Punjab (PB), Rajasthan (RJ) and West Bengal (WB)) of India. The phylogenetic and haplotype analysis showed the presence of thirty haplotypes in all the analyzed populations. The haplotypic diversity in the RJ population (Hd = 0.981) was higher than in the HR (Hd = 0.972), PB (Hd = 0.965) and WB population (Hd = 0.962). Recombination events (Rm = 12) and mutation events (485) were also detected. Analysis of molecular variance (AMOVA) indicated that genetic diversity was exclusively due to the differences within populations. The haplotype network was widely dispersed and not associated with specific populations, as a single common haplotype was not detected. The PB population contained both unique (H9, H10 and H11) and shared haplotypes (27 haplotypes) in a higher number in comparison to other geographical locations. Except for haplotype H22 (contains highly aggressive isolates), there was no specific linkage noticed between the isolate aggressiveness and haplotype. The concatenated sequences of all the three genes demonstrated a low level of genetic differentiation (Fst = −0.014 to 0.02) in the analyzed population. Positive values for the neutrality tests in PB, HR and RJ reveal a balancing selection mechanism behind the FG population structure. The WB population showed both positive and negative values of neutrality indices, indicating the role of both population expansion as well as balancing selection in structuring the FG population.
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Fusion of electronic nose and hyperspectral imaging for mutton freshness detection using input-modified convolution neural network. Food Chem 2022; 385:132651. [DOI: 10.1016/j.foodchem.2022.132651] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 02/01/2022] [Accepted: 03/04/2022] [Indexed: 01/19/2023]
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11
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Combining Disease Mechanism and Machine Learning to Predict Wheat Fusarium Head Blight. REMOTE SENSING 2022. [DOI: 10.3390/rs14122732] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Wheat Fusarium head blight (FHB) can be effectively controlled through prediction. To address the low accuracy and poor stability of model predictions of wheat FHB, a prediction method of wheat FHB that couples a logistic regression mechanism-based model and k-nearest neighbours (KNN) model is proposed in this paper. First, we selected predictive factors, including remote sensing-based and meteorological factors. Then, we quantitatively expressed the factor weights of the disease occurrence and development mechanisms in the disease prediction model by using a logistic model. Subsequently, we integrated the obtained factor weights into the predictive factors and input the predictive factors with weights into the KNN model to predict the incidence of wheat FHB. Finally, the accuracy and generalizability of the models were evaluated. Wheat fields in Changfeng, Dingyuan, Fengyuan, and Feidong counties, Anhui Province, where wheat FHB often occurs, were used as the study area. The incidences of wheat FHB on 29 April and 10 May 2021 were predicted. Compared with a model that did not consider disease mechanism, the accuracy of our model increased by approximately 13%. The overall accuracies of the models for the two dates were 0.88 and 0.92, and the F1 index was 0.86 and 0.94, respectively. The results show that the predictions made with the logistic-KNN model had higher accuracy and better stability than those made with the KNN model, thus achieving remote sensing-based high-precision prediction of wheat FHB.
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Unlersen MF, Sonmez ME, Aslan MF, Demir B, Aydin N, Sabanci K, Ropelewska E. CNN–SVM hybrid model for varietal classification of wheat based on bulk samples. Eur Food Res Technol 2022. [DOI: 10.1007/s00217-022-04029-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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13
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Shi L, Li L, Zhang F, Lin Y. Nondestructive detection of Panax notoginseng saponins by using hyperspectral imaging. Int J Food Sci Technol 2022. [DOI: 10.1111/ijfs.15790] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Lei Shi
- Faculty of Modern Agriculture Engineering Kunming University of Science and Technology Kunming 650500 China
| | - Lixia Li
- Faculty of Modern Agriculture Engineering Kunming University of Science and Technology Kunming 650500 China
| | - Fujie Zhang
- Faculty of Modern Agriculture Engineering Kunming University of Science and Technology Kunming 650500 China
| | - Yuhao Lin
- Faculty of Modern Agriculture Engineering Kunming University of Science and Technology Kunming 650500 China
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Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery. REMOTE SENSING 2022. [DOI: 10.3390/rs14020396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The accurate and automated diagnosis of potato late blight disease, one of the most destructive potato diseases, is critical for precision agricultural control and management. Recent advances in remote sensing and deep learning offer the opportunity to address this challenge. This study proposes a novel end-to-end deep learning model (CropdocNet) for accurate and automated late blight disease diagnosis from UAV-based hyperspectral imagery. The proposed method considers the potential disease-specific reflectance radiation variance caused by the canopy’s structural diversity and introduces multiple capsule layers to model the part-to-whole relationship between spectral–spatial features and the target classes to represent the rotation invariance of the target classes in the feature space. We evaluate the proposed method with real UAV-based HSI data under controlled and natural field conditions. The effectiveness of the hierarchical features is quantitatively assessed and compared with the existing representative machine learning/deep learning methods on both testing and independent datasets. The experimental results show that the proposed model significantly improves accuracy when considering the hierarchical structure of spectral–spatial features, with average accuracies of 98.09% for the testing dataset and 95.75% for the independent dataset, respectively.
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Zhao D, Feng S, Cao Y, Yu F, Guan Q, Li J, Zhang G, Xu T. Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm. FRONTIERS IN PLANT SCIENCE 2022; 13:879668. [PMID: 35599890 PMCID: PMC9120945 DOI: 10.3389/fpls.2022.879668] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 04/19/2022] [Indexed: 05/03/2023]
Abstract
Leaf blast is a disease of rice leaves caused by the Pyricularia oryzae. It is considered a significant disease is affecting rice yield and quality and causing economic losses to food worldwide. Early detection of rice leaf blast is essential for early intervention and limiting the spread of the disease. To quickly and non-destructively classify rice leaf blast levels for accurate leaf blast detection and timely control. This study used hyperspectral imaging technology to obtain hyperspectral image data of rice leaves. The descending dimension methods got rice leaf disease characteristics of different disease classes, and the disease characteristics obtained by screening were used as model inputs to construct a model for early detection of leaf blast disease. First, three methods, ElasticNet, principal component analysis loadings (PCA loadings), and successive projections algorithm (SPA), were used to select the wavelengths of spectral features associated with leaf blast, respectively. Next, the texture features of the images were extracted using a gray level co-occurrence matrix (GLCM), and the texture features with high correlation were screened by the Pearson correlation analysis. Finally, an adaptive-weight immune particle swarm optimization extreme learning machine (AIPSO-ELM) based disease level classification method is proposed to further improve the model classification accuracy. It was also compared and analyzed with a support vector machine (SVM) and extreme learning machine (ELM). The results show that the disease level classification model constructed using a combination of spectral characteristic wavelengths and texture features is significantly better than a single disease feature in terms of classification accuracy. Among them, the model built with ElasticNet + TFs has the highest classification accuracy, with OA and Kappa greater than 90 and 87%, respectively. Meanwhile, the AIPSO-ELM proposed in this study has higher classification accuracy for leaf blast level classification than SVM and ELM classification models. In particular, the AIPSO-ELM model constructed with ElasticNet+TFs as features obtained the best classification performance, with OA and Kappa of 97.62 and 96.82%, respectively. In summary, the combination of spectral characteristic wavelength and texture features can significantly improve disease classification accuracy. At the same time, the AIPSO-ELM classification model proposed in this study has sure accuracy and stability, which can provide a reference for rice leaf blast disease detection.
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Affiliation(s)
- Dongxue Zhao
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
| | - Shuai Feng
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
| | - Yingli Cao
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
- Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang, China
| | - Fenghua Yu
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
- Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang, China
| | - Qiang Guan
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
| | - Jinpeng Li
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
| | - Guosheng Zhang
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
| | - Tongyu Xu
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
- Liaoning Engineering Research Center for Information Technology in Agriculture, Shenyang, China
- *Correspondence: Tongyu Xu,
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Rice Leaf Blast Classification Method Based on Fused Features and One-Dimensional Deep Convolutional Neural Network. REMOTE SENSING 2021. [DOI: 10.3390/rs13163207] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Rice leaf blast, which is seriously affecting the yield and quality of rice around the world, is a fungal disease that easily develops under high temperature and humidity conditions. Therefore, the use of accurate and non-destructive diagnostic methods is important for rice production management. Hyperspectral imaging technology is a type of crop disease identification method with great potential. However, a large amount of redundant information mixed in hyperspectral data makes it more difficult to establish an efficient disease classification model. At the same time, the difficulty and small scale of agricultural hyperspectral imaging data acquisition has resulted in unrepresentative features being acquired. Therefore, the focus of this study was to determine the best classification features and classification models for the five disease classes of leaf blast in order to improve the accuracy of grading the disease. First, the hyperspectral imaging data were pre-processed in order to extract rice leaf samples of five disease classes, and the number of samples was increased by data augmentation methods. Secondly, spectral feature wavelengths, vegetation indices and texture features were obtained based on the amplified sample data. Thirdly, seven one-dimensional deep convolutional neural networks (DCNN) models were constructed based on spectral feature wavelengths, vegetation indices, texture features and their fusion features. Finally, the model in this paper was compared and analyzed with the Inception V3, ZF-Net, TextCNN and bidirectional gated recurrent unit (BiGRU); support vector machine (SVM); and extreme learning machine (ELM) models in order to determine the best classification features and classification models for different disease classes of leaf blast. The results showed that the classification model constructed using fused features was significantly better than the model constructed with a single feature in terms of accuracy in grading the degree of leaf blast disease. The best performance was achieved with the combination of the successive projections algorithm (SPA) selected feature wavelengths and texture features (TFs). The modeling results also show that the DCNN model provides better classification capability for disease classification than the Inception V3, ZF-Net, TextCNN, BiGRU, SVM and ELM classification models. The SPA + TFs-DCNN achieved the best classification accuracy with an overall accuracy (OA) and Kappa of 98.58% and 98.22%, respectively. In terms of the classification of the specific different disease classes, the F1-scores for diseases of classes 0, 1 and 2 were all 100%, while the F1-scores for diseases of classes 4 and 5 were 96.48% and 96.68%, respectively. This study provides a new method for the identification and classification of rice leaf blast and a research basis for assessing the extent of the disease in the field.
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Wheat Fusarium Head Blight Detection Using UAV-Based Spectral and Texture Features in Optimal Window Size. REMOTE SENSING 2021. [DOI: 10.3390/rs13132437] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
By combining the spectral and texture features of images captured by unmanned aerial vehicles (UAVs), the accurate and timely detection of wheat Fusarium head blight (FHB) can be realized. This study presents a methodology to select the optimal window size of the gray-level co-occurrence matrix (GLCM) to extract texture features from UAV images for FHB detection. Host conditions and the disease distribution were combined to construct the model, and its overall accuracy, sensitivity, and generalization ability were evaluated. First, the sensitive spectral features and bands of the UAV-derived hyperspectral images were obtained, and then texture features were selected. Subsequently, spectral features and texture features extracted from windows of different sizes were input to classify the area of severe FHB. According to the model comparison, the optimal window size was obtained. With the collinearity between features eliminated, the best performance of the logistic model reached, with an accuracy, F1 score, and area under the receiver operating characteristic curve of 0.90, 0.79, and 0.79, respectively, when the window size of the GLCM was 5 × 5 pixels on May 3, and of 0.90, 0.83, and 0.82, respectively, when the size was 17 × 17 pixels on May 8. The results showed that the selection of an appropriate GLCM window size for texture feature extraction enabled more accurate disease detection.
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Francesconi S, Harfouche A, Maesano M, Balestra GM. UAV-Based Thermal, RGB Imaging and Gene Expression Analysis Allowed Detection of Fusarium Head Blight and Gave New Insights Into the Physiological Responses to the Disease in Durum Wheat. FRONTIERS IN PLANT SCIENCE 2021; 12:628575. [PMID: 33868331 PMCID: PMC8047627 DOI: 10.3389/fpls.2021.628575] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 03/12/2021] [Indexed: 05/24/2023]
Abstract
Wheat is one of the world's most economically important cereal crop, grown on 220 million hectares. Fusarium head blight (FHB) disease is considered a major threat to durum (Triticum turgidum subsp. durum (Desfontaines) Husnache) and bread wheat (T. aestivum L.) cultivars and is mainly managed by the application of fungicides at anthesis. However, fungicides are applied when FHB symptoms are clearly visible and the spikes are almost entirely bleached (% of diseased spikelets > 80%), by when it is too late to control FHB disease. For this reason, farmers often react by performing repeated fungicide treatments that, however, due to the advanced state of the infection, cause a waste of money and pose significant risks to the environment and non-target organisms. In the present study, we used unmanned aerial vehicle (UAV)-based thermal infrared (TIR) and red-green-blue (RGB) imaging for FHB detection in T. turgidum (cv. Marco Aurelio) under natural field conditions. TIR and RGB data coupled with ground-based measurements such as spike's temperature, photosynthetic efficiency and molecular identification of FHB pathogens, detected FHB at anthesis half-way (Zadoks stage 65, ZS 65), when the percentage (%) of diseased spikelets ranged between 20% and 60%. Moreover, in greenhouse experiments the transcripts of the key genes involved in stomatal closure were mostly up-regulated in F. graminearum-inoculated plants, demonstrating that the physiological mechanism behind the spike's temperature increase and photosynthetic efficiency decrease could be attributed to the closure of the guard cells in response to F. graminearum. In addition, preliminary analysis revealed that there is differential regulation of genes between drought-stressed and F. graminearum-inoculated plants, suggesting that there might be a possibility to discriminate between water stress and FHB infection. This study shows the potential of UAV-based TIR and RGB imaging for field phenotyping of wheat and other cereal crop species in response to environmental stresses. This is anticipated to have enormous promise for the detection of FHB disease and tremendous implications for optimizing the application of fungicides, since global food crop demand is to be met with minimal environmental impacts.
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Affiliation(s)
- Sara Francesconi
- Department of Agriculture and Forest Sciences (DAFNE), University of Tuscia, Viterbo, Italy
| | - Antoine Harfouche
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
| | - Mauro Maesano
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
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Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision. REMOTE SENSING 2020. [DOI: 10.3390/rs13010026] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.
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Monitoring Wheat Fusarium Head Blight Using Unmanned Aerial Vehicle Hyperspectral Imagery. REMOTE SENSING 2020. [DOI: 10.3390/rs12223811] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The monitoring of winter wheat Fusarium head blight via rapid and non-destructive measures is important for agricultural production and disease control. Images of unmanned aerial vehicles (UAVs) are particularly suitable for the monitoring of wheat diseases because they feature high spatial resolution and flexible acquisition time. This study evaluated the potential to monitor Fusarium head blight via UAV hyperspectral imagery. The field site investigated by this study is located in Lujiang County, Anhui Province, China. The hyperspectral UAV images were acquired on 3 and 8 May 2019, when wheat was at the grain filling stage. Several features, including original spectral bands, vegetation indexes, and texture features, were extracted from these hyperspectral images. Based on these extracted features, univariate Fusarium monitoring models were developed, and backward feature selection was applied to filter these features. The backpropagation (BP) neural network was improved by integrating a simulated annealing algorithm in the experiment. A multivariate Fusarium head blight monitoring model was developed using the improved BP neural network. The results showed that bands in the red region provide important information for discriminating between wheat canopies that are either slightly or severely Fusarium-head-blight-infected. The modified chlorophyll absorption reflectance index performed best among all features, with an area under the curve and standard deviation of 1.0 and 0.0, respectively. Five commonly used methods were compared with this improved BP neural network. The results showed that the developed Fusarium head blight monitoring model achieved the highest overall accuracy of 98%. In addition, the difference between the producer accuracy and user accuracy of the improved BP neural network was smallest among all models, indicating that this model achieved better stability. These results demonstrate that hyperspectral images of UAVs can be used to monitor Fusarium head blight in winter wheat.
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