Citrus Huanglongbing symptom recognition based on deep learning
[Objective]Citrus Huanglongbing(HLB)poses a significant challenge to the global citrus industry,and so the early detection and control become crucial due to its long latency period and inconspicuous initial symptoms.Traditional manual diagnostic methods are time-consuming,inefficient,and prone to missing the optimal treatment window.With technological advancements,Convolutional Neural Networks(CNN)have shown great potential in crop disease and pest detection,and the integration of CNN into transformers has also garnered interest among researchers.[Method]In this study,an improved model based on the CvT(Convolutional vision Transformer)model was developed to identify and classify symptoms of citrus Huanglongbing.A citrus leaf dataset was collected and constructed,and data augmentation techniques were employed to expand the training samples.Lightweight Multi-Head Self-Attention(LMHSA)and Inverse Residual Feedforward Neural Network(IRFFN)models were designed to enhance the model's generalization ability and accuracy in symptom recognition.[Result]Experimental results demonstrated that the improved CvT model performed exceptionally well in the detection of citrus Huanglongbing,achieving a classification accuracy of 97.6%and enabling precise identification of the disease.[Conclusion]This model has the potential to become an auxiliary tool for the rapid identification of citrus Huanglongbing,and the accuracy and reliability of the model will be further optimized in the future.
万方数据
维普
