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Insect identification is the basis of insect research and disaster control and is of great importance for the design of pest control strategies and the protection of beneficial insects. Due to human subjective limitations and the small size and uneven distribution of pests, traditional methods of distinguishing and counting pest types based on experience cannot quickly and accurately detect and identify pests. Therefore, this paper proposes an object detection algorithm based on the improved Mask R-CNN model, aiming to improve the accuracy and efficiency in pest identification and counting. The algorithm improves the FPN structure in the feature extraction network and increases the weight coefficient when fusing feature layers of different scales. Based on the task of target detection and recognition, weight coefficient is adjusted to a proper parameter so that the semantic information and positioning information can be made full use to achieve more accurate recognition and positioning. The results of the experimental analysis of 1000 sample images show that the improved Mask R-CNN model has a recognition and detection accuracy of 99.4%, which is 2.7% higher than that of the unimproved Mask R-CNN model. The main contribution of this method is to improve the detection speed, and at the same time, the recognition accuracy has been significantly improved. This algorithm provides technical support for pest detection in the agricultural field and makes a contribution to the intellectualization of agricultural management.

Pest Identification and Counting of Yellow Plate in Field Based on Improved Mask R-CNN