The effectiveness and feasibility of the proposed wavelength combination were verified by identifying the resistant cultivar, assessing the resistant ability, and spectral image visualization. On the strength of the attention mechanism inside LPnet, the most informative spectral features related to lesion proportion were further extracted and combined into a novel and refined leaf spectral index. The growth status of BB lesion at the time scale was fully revealed. In this paper, with the aim of alleviating the labor burden of plant breeding experts in the resistant cultivar screening processing and exploring the disease resistance phenotyping variation pattern, visible/near-infrared (VIS–NIR) hyperspectral images of rice leaves from three varieties after inoculation were collected and sent into a self-built deep learning model LPnet for disease severity assessment. And dynamic rice BB phenotyping study is absent from exploring the pattern of BB growth with different genotypes. However, the BB resistance cultivar selection suffers tremendous labor cost, low efficiency, and subjective human error. Breeding disease resistant cultivar becomes the eco-friendliest and most effective alternative to regulate its outburst, since the propagation of pathogenic bacteria is restrained. Rice bacterial blight (BB) has caused serious damage in rice yield and quality leading to huge economic loss and food safety problems.
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