Cucumber Disease Detection Method Based on Visible Light Spectrum and Improved YOLOv5 in Natural Scenes
LI Shu-fei1, LI Kai-yu1, QIAO Yan2, ZHANG Ling-xian1*
1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2. Beijing Plant Protection Station, Beijing 100029, China
Abstract：The cucumber disease images acquired in natural scenes have noise, such as light and soil, which seriously affects the accuracy of cucumber disease recognition. The existing detection models occupy a large memory, making it difficult to achieve real-time detection of cucumber diseases.The visible spectral images of three diseases of cucumber, namely downy mildew, powdery mildew and anthracnose, in the natural environment are used as the research object. In this paper, a cucumber disease identification model based on the visible spectrum and an improved YOLOv5 object detection network is proposed to explore the accurate real-time detection of cucumber diseases in the natural environment and to reduce the storage cost of the detection model. The lightweight network structure YOLOv5s is used as the baseline model. The SE attention mechanism is introduced to extract the feature dimensional information to reduce the influence of complex background on the detection results and improve the detection accuracy of the model. The depth separable convolution is introduced to replace the standard convolution in the baseline model to reduce the computational burden caused by the model parameters and improve the detection speed. The network receives visible spectral images of arbitrary pixels and adjusts them to 640×640 pixels as input, outputs the cucumber disease occurrence region and disease category, initializes the detection method and trains the detection network using pre-trained weights on the COCO dataset.The experimental results show that the improved YOLOv5s-SE-DW model achieves 78.0%, 80.9%, and 83.6% detection accuracy for cucumber downy mildew, powdery mildew, and anthracnose, respectively, with mAP as high as 80.9%. The storage space of the model is only 9.45 MB, and the number of floating point operations is only 11.8 G. Compared with the baseline model, the mAP is improved by 2.4%, 4.6 G reduces the number of floating point operations, and the storage space required for the model is reduced by 4.95 MB. The improved model improves disease detection accuracy while reducing the storage memory. Further comparison with the classical two-stage target detection network Faster-RCNN and single-stage target detection networks YOLOv3, YOLOv3-tiny, YOLOv3-SPP, and YOLOv4 shows that the proposed YOLOv5s-SE-DW model improves the mAP by 3.8% compared with the best-performing YOLOv4 model among the comparison models, and the detection time and storage space are significantly reduced. The detection time and storage space are substantially reduced. The comprehensive results show that the proposed YOLOv5s-SE-DW network has good accuracy and real-time performance for cucumber disease detection in natural scenarios, which can meet the demand for disease detection in actual cucumber growing environments and provide a reference for cucumber disease detection in practical application scenarios.
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