Precision detection of crop diseases based on improved YOLOv5 model

Abstract

Accurate identification of crop diseases can effectively improve crop yield. Most current crop diseases present small targets, dense numbers, occlusions and similar appearance of different diseases, and the current target detection algorithms are not effective in identifying similar crop diseases. Therefore, in this paper, an improved model based on YOLOv5s was proposed to improve the detection of crop diseases. First, the CSP structure of the original model in the feature fusion stage was improved, and a lightweight structure was used in the improved CSP structure to reduce the model parameters, while the feature information of different layers was extracted in the form of multiple branches. A structure named CAM was proposed, which can extract global and local features of each network layer separately, and the CAM structure can better fuse semantic and scale inconsistent features to enhance the extraction of global information of the network. In order to increase the number of positive samples in the model training process, one more grid was added to the original model with three grids to predict the target, and the formula for the prediction frame centroid offset was modified to obtain the better prediction frame centroid offset when the target centroid falled on the special point of the grid. To solve the problem of the prediction frame being scaled incorrectly during model training, an improved DIoU loss function was used to replace the GIoU loss function used in the original YOLOv5s. Finally, the improved model was trained using transfer learning, the results showed that the improved model had the best mean average precision (mAP) performance compared to the Faster R-CNN, SSD, YOLOv3, YOLOv4, YOLOv4-tiny, and YOLOv5s models, and the mAP, F1 score, and recall of the improved model were 95.92%, 0.91, and 87.89%, respectively. Compared with YOLOv5s, they improved by 4.58%, 5%, and 4.78%, respectively. The detection speed of the improved model was 40.01 FPS, which can meet the requirement of real-time detection. The results showed that the improved model outperformed the original model in several aspects, had stronger robustness and higher accuracy, and can provide better detection for crop diseases.

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8

“…Through experimentation, the proposed lightweight DL model for mung bean disease and pest detection demonstrated an impressive average accuracy of 93.65%. ( Zhao et al., 2023 ) introduced enhancements to the YOLOv5s model for improved crop disease detection. The modifications included refining the CSP structure in the feature fusion stage, incorporating a lightweight composition to reduce model parameters, and extracting feature information through multiple branches.…”

Maize plant detection using UAV-based RGB imaging and YOLOv5

“…Given the advantages, the YOLO algorithm has been applied in a range of object detection applications requiring both simplicity and efficiency, particularly for plant detection tasks. For example, urban plantation tree detection with high-resolution remote sensing imagery based on YOLOv4-Lite ( Zheng and Wu, 2022 ), real-time strawberry detection based on YOLOv4 ( Zhang et al., 2022 ), crop diseases detection based on YOLOv5 ( Zhao et al., 2023 ), and wheat spike detection in UAV images based on YOLOv5 ( Zhao et al., 2021 ). Recently, variant versions of YOLOv5, notably the nano (n) and small (s) versions, referred to as YOLOv5n and YOLOv5s, respectively, have become attractive, considering the real-time performance requirements of YOLOv5 applied to UAVs or field robots.…”

Securing Your Airspace: Detection of Drones Trespassing Protected Areas

“…Unmanned Aerial Vehicles (UAVs) have evolved rapidly over the past few decades [1][2][3][4][5][6][7][8][9][10] leading to mass production of affordable drones [11,12]. From kids and hobbyists to police officers [13] and firefighters [14], drones have found novel applications and use cases [15][16][17][18][19][20][21][22][23][24]. For instance, Google and Amazon trialed drones for merchandise delivery while law enforcement leverages drones for speed checks [25][26][27][28][29][30].…”