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A lightweight model for early perception of rice diseases driven by photothermal information fusion

文献类型： 外文期刊

作者： Yang, Ning 1 ; Chen, Liang 1 ; Li, Tongge 1 ; Liu, Shuhua 2 ; Wang, Aiying 2 ; Tang, Jian 2 ; Chen, Si 3 ; Wang, Yafei 4 ; Cheng, Wei 1 ;

作者机构： 1.Jiangsu Univ, Sch Elect & Informat Engn, Zhenjiang, Peoples R China

2.China Natl Rice Res Inst, State Key Lab Rice Biol & Breeding, Hangzhou, Peoples R China

3.Jiangsu Univ, Fluid Machinery Ctr, Zhenjiang, Peoples R China

4.Jiangsu Univ, Inst Agr Engn, Zhenjiang, Peoples R China

关键词： Data fusion; FPGA; Edge detection; Low power consumption; Early prevention

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 233 卷

页码：

收录情况： SCI

摘要： Rice blast disease poses a significant threat to rice yield. The disease progresses rapidly once symptoms appear, making timely control challenging. Moreover, once lesions form, the damage becomes irreversible. Existing detection methods often suffer from delays and lack effective strategies for identifying the disease at its asymptomatic stage, hindering early diagnosis. In this study, we collected thermal and optical data from rice canopies at different infection stages and integrated physiological and biochemical analyses to investigate the infection mechanism during the early, asymptomatic phase. Additionally, we employed the SURF feature extraction algorithm to fuse thermal and optical images, developing a preliminary method for identifying asymptomatic rice regions based on thermal signatures. This approach effectively captured the spectral responses of asymptomatic rice and mitigated the limitations of single-sensor detection in early disease identification. By analyzing spectral and temperature characteristics, we applied feature dimensionality reduction techniques to construct early detection models at both the canopy and leaf levels. The models achieved overall classification accuracies (OA) of 92 % and 97 %, respectively, enabling detection 72 h prior to lesion formation. Finally, we designed fixed-point IP and multi-level register-cascade pipeline architecture, implementing low-power FPGAbased edge computing system. The leaf-level detection model deployed on the FPGA achieved an accuracy of 92 %, with a power consumption of 0.076 W and an inference speed of 0.11 ms. This study proposes an effective real-time detection method for identifying early asymptomatic rice blast, thereby facilitating timely disease monitoring and prevention.

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