LT-DeepLab: an improved DeepLabV3+ cross-scale segmentation algorithm for Zanthoxylum bungeanum Maxim leaf-trunk diseases in real-world environments

• DOI: 10.3389/fpls.2024.1423238
• Publication Date: 2024-10-22T04:40:36Z
• Authors: Tao Yang; Jingjing Wei; Yongjun Xiao; Shuyang Wang; Jingxuan Tan; Yupeng Niu; Xuliang Duan; Fei Pan; Haibo Pu
• Keywords: Pooling; Pyramid (geometry)

Introduction Zanthoxylum bungeanum Maxim is an economically significant crop in Asia, but large-scale cultivation often threatened by frequent diseases, leading to yield declines. Deep learning-based methods for disease recognition have emerged as a vital research area agriculture. Methods This paper presents novel model, LT-DeepLab, the semantic segmentation of leaf spot (folium macula), rust, frost damage (gelu damnum), and diseased leaves trunks complex field environments. The proposed model enhances DeepLabV3+ with innovative Fission Depth Separable CRCC Atrous Spatial Pyramid Pooling module, which reduces structural parameters module improves cross-scale extraction capability. Incorporating Criss-Cross Attention Convolutional Block Module provides complementary boost channel feature extraction. Additionally, deformable convolution low-dimensional features, Fully Network auxiliary header integrated optimize network enhance accuracy without increasing parameter count. Results LT-DeepLab mean Intersection over Union (mIoU) 3.59%, Pixel Accuracy (mPA) 2.16%, Overall (OA) 0.94% compared baseline DeepLabV3+. It also computational demands 11.11% decreases count 16.82%. Discussion These results indicate that demonstrates excellent capabilities environments while maintaining high efficiency, offering promising solution improving management efficiency.