Aphid Recognition and Counting Based on an Improved YOLOv5 Algorithm in a Climate Chamber Environment

SIMPLE SUMMARY: The pepper [Capsicum annuum L. (Solanales: Solanaceae)] is one of the most economically important vegetable crops and the demand for peppers has been increasing. In the process of pepper crop growth, the types and quantity of insect pests are increasing, which has become the first natural biological disaster threatening crop yield and quality. In particular, the green peach aphid [Myzus persicae Sulzer (Hemiptera: Aphididae)] is one of the most threatening insect pests in pepper cultivation, which causes a lot of damage, such as necrosis, wilting, chlorosis, and defoliation. Hence, accurately recognizing and counting the aphids is essential for ensuring the excellent productivity of pepper crops. Due to the time-consuming and inefficient work of traditional methods, there have been deep learning methods for crop pest recognition and counting in recent years. The YOLO algorithm is one of the most effective deep learning algorithms used for object detection. To investigate aphid recognition and counting of pepper crop growth, this paper develops an improved YOLOv5 approach based on a deep convolutional neural network (CNN) in the climate chamber environment. Experimental results in the aphid dataset indicate that the proposed method can achieve significant improvements in terms of recognition accuracy and counting performance. ABSTRACT: Due to changes in light intensity, varying degrees of aphid aggregation, and small scales in the climate chamber environment, accurately identifying and counting aphids remains a challenge. In this paper, an improved YOLOv5 aphid detection model based on CNN is proposed to address aphid recognition and counting. First, to reduce the overfitting problem of insufficient data, the proposed YOLOv5 model uses an image enhancement method combining Mosaic and GridMask to expand the aphid dataset. Second, a convolutional block attention mechanism (CBAM) is proposed in the backbone layer to improve the recognition accuracy of aphid small targets. Subsequently, the feature fusion method of bi-directional feature pyramid network (BiFPN) is employed to enhance the YOLOv5 neck, further improving the recognition accuracy and speed of aphids; in addition, a Transformer structure is introduced in front of the detection head to investigate the impact of aphid aggregation and light intensity on recognition accuracy. Experiments have shown that, through the fusion of the proposed methods, the model recognition accuracy and recall rate can reach 99.1%, the value mAP@0.5 can reach 99.3%, and the inference time can reach 9.4 ms, which is significantly better than other YOLO series networks. Moreover, it has strong robustness in actual recognition tasks and can provide a reference for pest prevention and control in climate chambers.