Detection of fruit tree diseases in natural environments: A novel approach based on stereo camera and deep learning

The occurrence of diseases in orchards has a significant impact on fruit yield and quality. Inspection devices equipped with cameras can effectively replace manual intervention in the process of orchard management by swiftly detecting diseases. However, the images captured by such devices often exhibit a wide vision field and contain a significant amount of extraneous information. This paper presented a method for detecting diseases in natural environments based on binocular cameras and deep learning techniques applied to fruit tree leaf images with a wide visual field. Firstly, the ZED2i binocular camera was utilized to capture image pairs from a long distance, simulating the visual field of an inspection device. These image pairs were then processed using the Unimatch stereo matching algorithm to obtain a disparity map and calculated the corresponding depth map. The depth information was used to create a mask, eliminating irrelevant background information from the images. Secondly, a lightweight disease detection (LDD) model was proposed based on the advanced YOLOv5 framework for detecting pear rust and plum perforation diseases. The backbone network consisted of shuffle channel block, inverted shuffle channel block, and convolutional block attention module, with only one detection head used in the classifier part. The final experiments evaluated the segmentation, model improvement, and disease spots detection performance. The results showed that the depth map obtained using Unimatch for stereo matching was more accurate than that obtained using the ZED software development kit. In ablation experiments, LDD achieved a mean average precision of 93.0%, with a model size of only 3.9 MB, outperforming the original YOLOv5-s model. Preprocessed images with depth information exhibited improved detection performance, achieved a F1 score of 93.62%, which was a 10.92% improvement over direct detection of the original images. Overall, the presented method successfully addresses the issue of background interference when detecting diseases of fruit tree leaf with a wide visual field, providing a technical basis for automated orchard inspection operations. •Presented an efficient method for detecting fruit tree diseases in wide visual field images.•Used the stereo matching algorithm Unimatch to preprocess the image according to the depth information.•Proposed a lightweight disease detection model.•Addressed the issue of background interference when detecting diseases