Sweetgum Leaf Spot Image Segmentation and Grading Detection Based on an Improved DeeplabV3+ Network

Abstract

Leaf spot disease and brown spot disease are common diseases affecting maple leaves. Accurate and efficient detection of these diseases is crucial for maintaining the photosynthetic efficiency and growth quality of maple leaves. However, existing segmentation methods for plant diseases often fail to accurately and rapidly detect disease areas on plant leaves. This paper presents a novel solution to accurately and efficiently detect common diseases in maple leaves. We propose a deep learning approach based on an enhanced version of DeepLabV3+ specifically designed for detecting common diseases in maple leaves. To construct the maple leaf spot dataset, we employed image annotation and data enhancement techniques. Our method incorporates the CBAM-FF module to fuse gradual features and deep features, enhancing the detection performance. Furthermore, we leverage the SANet attention mechanism to improve the feature extraction capabilities of the MobileNetV2 backbone network for spot features. The utilization of the focal loss function further enhances the detection accuracy of the affected areas. Experimental results demonstrate the effectiveness of our improved algorithm, achieving a mean intersection over union (MIoU) of 90.23% and a mean pixel accuracy (MPA) of 94.75%. Notably, our method outperforms traditional semantic segmentation methods commonly used for plant diseases, such as DeeplabV3+, Unet, Segnet, and others. The proposed approach significantly enhances the segmentation performance for detecting diseased spots on Liquidambar formosana leaves. Additionally, based on pixel statistics, the segmented lesion image is graded for accurate detection.