Improvement of the YOLOv5 Model in the Optimization of the Brown Spot Disease Recognition Algorithm of Kidney Bean

Abstract

The kidney bean is an important cash crop whose growth and yield are severely affected by brown spot disease. Traditional target detection models cannot effectively screen out key features, resulting in model overfitting and weak generalization ability. In this study, a Bi-Directional Feature Pyramid Network (BiFPN) and Squeeze and Excitation (SE) module were added to a YOLOv5 model to improve the multi-scale feature fusion and key feature extraction abilities of the improved model. The results show that the BiFPN and SE modules show higher heat in the target location region and pay less attention to irrelevant environmental information in the non-target region. The detection Precision, Recall, and mean average Precision (mAP@0.5) of the improved YOLOv5 model are 94.7%, 88.2%, and 92.5%, respectively, which are 4.9% higher in Precision, 0.5% higher in Recall, and 25.6% higher in the mean average Precision compared to the original YOLOv5 model. Compared with the YOLOv5-SE, YOLOv5-BiFPN, FasterR-CNN, and EfficientDet models, detection Precision improved by 1.8%, 3.0%, 9.4%, and 9.5%, respectively. Moreover, the rate of missed and wrong detection in the improved YOLOv5 model is only 8.16%. Therefore, the YOLOv5-SE-BiFPN model can more effectively detect the brown spot area of kidney beans.

A Lightweight Algorithm for Recognizing Pear Leaf Diseases in Natural Scenes Based on an Improved YOLOv5 Deep Learning Model

“…However, detecting leaf diseases can be time-consuming, labor-intensive, and prone to inaccuracy when using the naked eye [2]. Fortunately, with the development of computer technology, image recognition technology has shown potential for the efficient detection of plant diseases [3]. High flexibility and real-time automatic identification are required to accurately detect leaf diseases in complex and variable growth environments [4].…”

Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm

“…To provide a clear comparison, Table 1 summarizes the algorithms, key features, and results highlighted in the existing literature. [16][17][18][19][20]. The Yolo algorithm has evolved into a series, comprising Yolov1, Yolov2, Yolov3, Yolov4, Yolov5, and Yolox, among others [21][22][23][24][25][26].…”

Phenotypic detection of flax plants based on improved Flax-YOLOv5

“…(2023) proposed a method for detecting strawberry fruit planted in fields under different shade levels. Su et al. (2023) proposed an improved YOLOv5-SE-BiFPN model, which could more effectively detect brown spot lesion areas in kidney beans.…”