Rice Science ›› 2023, Vol. 30 ›› Issue (6): 652-660.DOI: 10.1016/j.rsci.2023.06.005

• Research Papers • Previous Articles    

Application of UAV-Based Imaging and Deep Learning in Assessment of Rice Blast Resistance

Lin Shaodan1,2, Yao Yue1,3, Li Jiayi1,3, Li Xiaobin1,3, Ma Jie1,3, Weng Haiyong1,3, Cheng Zuxin1,4(), Ye Dapeng1,3()   

  1. 1College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
    2College of Mechanical and Intelligent Manufacturing, Fujian Chuanzheng Communications College, Fuzhou 350007, China
    3Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou 350002, China
    4College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou 350002, China


Rice blast is regarded as one of the major diseases of rice. Screening rice genotypes with high resistance to rice blast is a key strategy for ensuring global food security. Unmanned aerial vehicles (UAV)-based imaging, coupled with deep learning, can acquire high-throughput imagery related to rice blast infection. In this study, we developed a segmented detection model (called RiceblastSegMask) for rice blast detection and resistance evaluation. The feasibility of different backbones and target detection models was further investigated. RiceblastSegMask is a two-stage instance segmentation model, comprising an image-denoising backbone network, a feature pyramid, a trinomial tree fine-grained feature extraction combination network, and an image pixel codec module. The results showed that the model combining the image-denoising and fine-grained feature extraction based on the Swin Transformer and the feature pixel matching feature labels with the trinomial tree recursive algorithm performed the best. The overall accuracy for instance segmentation of RiceblastSegMask reached 97.56%, and it demonstrated a satisfactory accuracy of 90.29% for grading unique resistance to rice blast. These results indicated that low-altitude remote sensing using UAV, in conjunction with the proposed RiceblastSegMask model, can efficiently calculate the extent of rice blast infection, offering a new phenotypic tool for evaluating rice blast resistance on a field scale in rice breeding programs.

Key words: rice blast, segmentation detection, trinomial tree, Swin Transformer, unmanned aerial vehicle