Tomato Pest Recognition Algorithm Based on Improved YOLOv4

Tomato plants are infected by diseases and insect pests in the growth process, which will lead to a reduction in tomato production and economic benefits for growers. At present, tomato pests are detected mainly through manual collection and classification of field samples by professionals. This manual classification method is expensive and time-consuming. The existing automatic pest detection methods based on a computer require a simple background environment of the pests and cannot locate pests. To solve these problems, based on the idea of deep learning, a tomato pest identification algorithm based on an improved YOLOv4 fusing triplet attention mechanism (YOLOv4-TAM) was proposed, and the problem of imbalances in the number of positive and negative samples in the image was addressed by introducing a focal loss function. The K-means + + clustering algorithm is used to obtain a set of anchor boxes that correspond to the pest dataset. At the same time, a labeled dataset of tomato pests was established. The proposed algorithm was tested on the established dataset, and the average recognition accuracy reached 95.2%. The experimental results show that the proposed method can effectively improve the accuracy of tomato pests, which is superior to the previous methods. Algorithmic performance on practical images of healthy and unhealthy objects shows that the proposed method is feasible for the detection of tomato pests.

Automated summary

This research proposes a tomato pest identification algorithm based on an improved YOLOv4 fusing triplet attention mechanism, addressing the issue of imbalances in sample numbers and demonstrating high recognition accuracy in experiments. The algorithm's performance on practical images also supports its feasibility for tomato pest detection.