Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques

Abdulridha, Jaafar; Ampatzidis, Yiannis; Kakarla, Sri Charan; Roberts, Pamela D.

doi:10.1007/s11119-019-09703-4

Cited by 142 publications

(94 citation statements)

References 76 publications

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“…It is widely used in agriculture, oceanography, geology, atmosphere, and environmental sensing [ 19 ]. In agriculture, hyperspectral techniques can be used for pest and disease monitoring [ 20 , 21 , 22 , 23 , 24 , 25 ], biomass estimation [ 26 ], and biological stress monitoring [ 27 ]. In the field of pest and disease monitoring, it has been applied to many plants, including tomatoes, grapes, and wheat [ 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Research Progress on the Early Monitoring of Pine Wilt Disease Using Hyperspectral Techniques

Zhang

Zheng

et al. 2020

Sensors

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Pine wilt disease (PWD) caused by pine wood nematode (PWN, Bursaphelenchus xylophilus) originated in North America and has since spread to Asia and Europe. PWN is currently a quarantine object in 52 countries. In recent years, pine wilt disease has caused considerable economic losses to the pine forest production industry in China, as it is difficult to control. Thus, one of the key strategies for controlling pine wilt disease is to identify epidemic points as early as possible. The use of hyperspectral cameras mounted on drones is expected to enable PWD monitoring over large areas of forest, and hyperspectral images can reflect different stages of PWD. The trend of applying hyperspectral techniques to the monitoring of pine wilt disease is analyzed, and the corresponding strategies to address the existing technical problems are proposed, such as data collection of early warning stages, needs of using unmanned aerial vehicles (UAVs), and establishment of models after preprocessing.

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Section: Introductionmentioning

confidence: 99%

Research Progress on the Early Monitoring of Pine Wilt Disease Using Hyperspectral Techniques

Zhang

Zheng

et al. 2020

Sensors

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“…We’re going to process video data more efficiently. At present, computer vision technology has been widely used in agricultural research [ 30 , 31 , 32 , 33 ], such as crop pest detection [ 34 , 35 , 36 ] or pest activity detection [ 37 ], crop disease detection [ 38 ], identification of crop growth [ 39 , 40 ], crop yield prediction [ 41 ], and animal behavior detection [ 26 , 27 , 42 ]. The first four kinds of applications can get good results by processing and analyzing only a few clear images.…”

Section: Discussionmentioning

confidence: 99%

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

Zhang

Zhan

et al. 2020

Insects

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Statistical analysis and research on insect grooming behavior can find more effective methods for pest control. Traditional manual insect grooming behavior statistical methods are time-consuming, labor-intensive, and error-prone. Based on computer vision technology, this paper uses spatio-temporal context to extract video features, uses self-built Convolution Neural Network (CNN) to train the detection model, and proposes a simple and effective Bactrocera minax grooming behavior detection method, which automatically detects the grooming behaviors of the flies and analysis results by a computer program. Applying the method training detection model proposed in this paper, the videos of 22 adult flies with a total of 1320 min of grooming behavior were detected and analyzed, and the total detection accuracy was over 95%, the standard error of the accuracy of the behavior detection of each adult flies was less than 3%, and the difference was less than 15% when compared with the results of manual observation. The experimental results show that the method in this paper greatly reduces the time of manual observation and at the same time ensures the accuracy of insect behavior detection and analysis, which proposes a new informatization analysis method for the behavior statistics of Bactrocera minax and also provides a new idea for related insect behavior identification research.

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“…Hyperspectral or multispectral sensors can distinguish diseased leaves from healthy ones much earlier than imaging in the visible spectrum only. Abdulridha et al [96] were able to detect and distinguish the onset of target spot caused by the fungus Corynespora cassicola and bacterial spot caused by Xanthomonas perforans on tomato in the asymptomatic phase. The multi-spectral imaging approach was successful both under laboratory and field conditions.…”

Section: Introductionmentioning

confidence: 99%

Tackling microbial threats in agriculture with integrative imaging and computational approaches

Singh

Dutta

Puccetti

et al. 2021

Computational and Structural Biotechnology Journal

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Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques

Cited by 142 publications

References 76 publications

Research Progress on the Early Monitoring of Pine Wilt Disease Using Hyperspectral Techniques

Research Progress on the Early Monitoring of Pine Wilt Disease Using Hyperspectral Techniques

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

Tackling microbial threats in agriculture with integrative imaging and computational approaches

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