Detection of<i>Listronotus maculicollis</i>(Coleoptera: Curculionidae) Turfgrass Canopy Activity With the Use of a Novel Fluorescent Marking System Suggests Opportunities for Improved Mechanical Control

Czyzewski, Benjamin D; McGraw, Benjamin A.

doi:10.1093/ee/nvy156

Cited by 2 publications

(1 citation statement)

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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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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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Refining Gastrophysa atrocyanea (Coleoptera: Chrysomelidae) identification using UV marking in whole-plant assessment

Park

2024

Journal of Asia-Pacific Entomology

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Detection ofListronotus maculicollis(Coleoptera: Curculionidae) Turfgrass Canopy Activity With the Use of a Novel Fluorescent Marking System Suggests Opportunities for Improved Mechanical Control

Cited by 2 publications

References 17 publications

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

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

Refining Gastrophysa atrocyanea (Coleoptera: Chrysomelidae) identification using UV marking in whole-plant assessment

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