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

Abstract

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.

The System Research and Implementation for Autorecognition of the Ship Draft via the UAV

“…us, in the above method (1), the least square curve fitting is carried out in segments to reduce the error interference. Method (2) also uses the cubic spline difference method [13][14][15][16][17][18] to fit the curve based on the idea of segmentation.…”

Real-Time Recognition and Detection of Bactrocera minax (Diptera: Trypetidae) Grooming Behavior Using Body Region Localization and Improved C3D Network

“…Five grooming behavior detection methods were compared on the test set, including Zhang’s method [ 21 ], based on spatiotemporal context, and Zou’s method [ 20 ], which utilizes keypoints recognition with DeepLabCut. The third and fourth methods involved experimenting with different data processing approaches, while directly employing the C3D network [ 40 ].…”

“…There are limited methods available for detecting insect grooming behavior from videos. Zhang [ 21 ] proposed a method that combines spatiotemporal context and convolutional neural networks, achieving promising results. This approach generates spatiotemporal feature images by fusing temporal and spatial features and classifies behavior based on these images.…”

“…Studying the behavioral characteristics of grooming is an effective way of controlling Bactrocera minax [ 20 ]. It also aids in the development of safe and efficient control strategies [ 21 ].…”

Key Points Tracking and Grooming Behavior Recognition of Bactrocera minax (Diptera: Trypetidae) via DeepLabCut

“…Technologies bring us a lot of convenience. Domestic and international research on animal behavior classification technology and target tracking technology has been strengthened, and certain progress has been made [34][35][36][37]. We have tried some deep learning algorithms, such as ABRS, which can only find the time point of different behavior switching in a complex environment but cannot track target or recognize the accurate grooming behavior [38].…”