Improved Deeplabv3+ Method for the Panax Notoginseng Disease Segmentation

Panax notoginseng diseases seriously affect Panax notoginseng yield and quality. The Panax notoginseng diseases precise identification and segmentation can provide the basis for disease control and treatment. Deep learning technologies gradually displace manual plant disease identification techniques due to their time-consuming, labor-intensive, inefficient, and subjective disadvantages. Consequently, it is essential to explore the potential application of deep learning-based semantic segmentation models in Panax notoginseng disease monitoring. However, due to the complexity of symptoms, ranging from small spots to large-scale diseases, achieving accurate disease segmentation is challenging. To address the above problem, we propose an improved Deeplabv3+ model for Panax notoginseng disease segmentation. Two modules are introduced to improve the performance of the algorithm. Specially, a Dual Attention Module (DAM) is added to improve multiscale feature extraction for achieving accurate segmentation of small disease regions. Moreover, we introduce a Squeeze and Excitation Module (SEM) into the backbone network, which can enhance the ability of feature extraction for large disease regions. Finally, a segmentation dataset of Panax notoginseng diseases is constructed to provide a benchmark for the segmentation of Panax notoginseng diseases. Experimental results show that our method can achieve 92.60% PA and 73.22% mIoU. This study can offer a valuable guide for future monitoring of Panax notoginseng diseases in cultivation trials.