Application of UAV-Based Imaging and Deep Learning in Assessment of Rice Blast Resistance
Lin Shaodan,
Yao Yue,
Li Jiayi,
Li Xiaobin,
Ma Jie,
Weng Haiyong,
Cheng Zuxin,
Ye Dapeng
Affiliations
Lin Shaodan
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China; College of Mechanical and Intelligent Manufacturing, Fujian Chuanzheng Communications College, Fuzhou 350007, China
Yao Yue
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China; Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou 350002, China
Li Jiayi
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China; Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou 350002, China
Li Xiaobin
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China; Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou 350002, China
Ma Jie
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China; Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou 350002, China
Weng Haiyong
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China; Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou 350002, China
Cheng Zuxin
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China; College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou 350002, China; Corresponding authors.
Ye Dapeng
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China; Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou 350002, China; Corresponding authors.
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.
