A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion - Xia, Fei; Xie, Xiaojun; Wang, Zongqin; Jin, Shichao; Yan, Ke; Ji, Zhiwei (Frontiers in Plant Science, 2022)
Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision provides great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage patterns in the disease progression. In this study, we proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, WDM-tSNE. To verify the effectiveness, we applied our method on four public datasets of images. The results demonstrated that the new developed tool is capable of identifying the plant disease and further uncover the underlying subtypes associated with pathogenic resistance. In summary, the current framework provides great clustering performance for root or leave images of diseased plant with pronounced disease spots or symptoms.