Research and Development of Grapevine Leaf Disease Diagnosis System Based on Convolutional Neural Network
A novel convolutional neural network recognition model was proposed to address the current problems of lack of large datasets and low quality of datasets for grape leaf diseases.First,a preliminary feature extraction strategy was implemented by alternating the structure of a 3 × 3 convolutional kernel with a pooling layer.Subsequently,a multi-branching module aimed at capturing lesion features at multi-ple scales was constructed through a null convolution technique.In the deeper layers of the network, a dense connectivity strategy was introduced to further refine the hierarchical model for recognizing grape leaf diseases.This model not only accurately recognizes common grape leaf diseases,but also provides ap-propriate treatment suggestions according to the severity of the diseases.Finally,it is demonstrated through experiments and simulations that the proposed novel convolutional neural network recognition model facili-tates growers to accurately diagnose grape leaf disease conditions and effectively reduce economic losses.
deep learningdisease controlfeature recognitionspot segmentationneural network
万方数据
维普
