Abstract
Simple SummaryTraditional manual insect grooming behavior statistical methods are time-consuming, labor-intensive, and error-prone. In response to this problem, we proposed a method for detecting the grooming behavior of Bactrocera minax based on computer vision and artificial intelligence. Using this method to detect the grooming behavior of Bactrocera minax can save a lot of manpower, the detection accuracy is above 95%, 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. At the same time, it also has a positive effect on pest control research. 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.
Highlights
Crops and stores have historically been attacked by pests [1]
The original RGB frames of Bactrocera minax was processed in two steps, the spatial information and temporal features of Bactrocera minax were fused into a new feature image, using Convolution Neural Network (CNN)
The results show that the recognition rate is improved and the convergence speed is faster when the proposed method is used to detect the grooming behavior of Bactrocera minax
Summary
Crops and stores have historically been (and will continue to be) attacked by pests [1]. Grooming is a common and habitual behavior of many insects [5] and is a very common behavior [5]. The insect groups involved in grooming behavior are different, the main functions of grooming behavior are surprisingly similar [6,7]. Remove foreign dust particles from the surface of the epidermis and sensory organs [8], remove body surface secretions and epidermal lipids [9,10], collect pollen particles as food [11], and remove external parasites or pathogens [12]. At the same time, grooming behavior plays a significant role in maintaining the sensitivity of sensory organs [13,14]. A better understanding of grooming will provide new insight toward the development of control practices, leading to less damage to beneficial insects and new possibilities for sustainable agricultural activity [6]
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