山东农业大学学报（自然科学版）2026，Vol.57Issue(1)：56-65,10.DOI:10.3969/j.issn.1000-2324.2026.01.006
高光谱成像结合BiTCN-SA的马铃薯晚疫病早期识别
Early Identification of Potato Late Blight Using Hyperspectral Imaging Combined with BiTCN-SA
摘要
Abstract
Early identification of potato late blight is crucial for controlling its development.To fully utilize the inter-band characteristic information of hyperspectral data and improve the accuracy of models in the early identification of potato late blight,this study proposes a potato late blight early identification model(BiTCN-SA)based on a Bidirectional Temporal Convolutional Network(BiTCN)fused with a Self-Attention(SA)mechanism.The BiTCN captures inter-band correlation features through forward and backward convolution branches,and fully exploits the associations between preceding and subsequent bands.The self-attention mechanism dynamically assigns importance weights to different bands,enhancing the contribution of key bands to model classification.The BiTCN-SA model integrates self-attention with BiTCN to achieve a combination of local convolutional features and global attention weights in both directions,realizing dual feature extraction and improving the model's identification accuracy.This study collects hyperspectral potato leaf data from three stages(healthy,asymptomatic,and early symptomatic),conducts modeling and analysis.It verifies the superiority of the proposed model by comparing machine learning methods such as SVM and RF,and deep learning models including CNN,LSTM,TCN,and BiTCN.The results show that the BiTCN-SA model converges faster than standalone TCN and BiTCN models,with significantly improved accuracy.It demonstrates stronger feature extraction capability than other machine learning and deep learning methods,achieving an overall accuracy of 98%and an identification rate of 96%for asymptomatic diseased leaves.This method fully utilizes deep inter-band information from hyperspectral data,and its identification rate shows substantial improvement over other machine learning and deep learning methods,providing technical support for early warning and control of potato late blight.关键词
马铃薯晚疫病/高光谱成像/早期识别/双向时间卷积网络/自注意力机制/特征提取Key words
Potato late blight/hyperspectral imaging/early identification/bidirectional temporal convolutional network/self-attention mechanism/feature extraction分类
数理科学引用本文复制引用
罗祖升,刘雨琛,王晓丹,张巧杰..高光谱成像结合BiTCN-SA的马铃薯晚疫病早期识别[J].山东农业大学学报（自然科学版）,2026,57(1):56-65,10.基金项目
科技创新2030-"新一代人工智能"重大项目(2021ZD0113603) （2021ZD0113603）
