JutePest-YOLO: A Deep Learning Network for Jute Pest Identification and Detection

In recent years, jute, as an important natural fiber crop, has become more and more significant in the production process of insect pests, causing serious harm to agricultural production. Especially in the field of crop pest identification with complex backgrounds, fuzzy features, and multiple small targets, the lack of datasets specifically for jute pests has led to the large limitations of traditional pest identification models in terms of generalization. At the same time, the research on models specifically for jute pest detection is still in its infancy. To solve this problem, we constructed a large-scale image dataset containing nine types of jute pests, which was highly targeted and could effectively support model training and evaluation. In this study, we developed a deep convolutional neural network model based on YOLOv7, namely JutePest-YOLO. The model has optimized the Backbone, Head, and loss functions of the baseline model, and introduced the new ELAN-P module and P6 detection layer, which effectively improved the model's ability to identify jute pests in complex backgrounds. The experimental results showed that compared with the baseline model, the Precision, Recall, and F1 scores of the JutePest-YOLO model were improved by 3.45%, 1.76%, and 2.58%, respectively; the mAP@0.5 and mAP@0.5:0.95 was improved by 2.24% and 3.25%, and the overall model's computation (GFLOPS) was reduced by 16.05%. Compared to other advanced methods such as YOLOv8s, JutePest-YOLO has achieved superior performance in terms of detection accuracy, with a precision of 98.7% and mAP@0.5 reaching 95.68%. As a result, JutePest-YOLO not only achieved significant improvement in recognition accuracy but also optimized computational efficiency. It's a high-performance, lightweight solution for jute pest detection.