PNN based crop disease recognition with leaf image features and meteorological data

An automatic crop disease recognition method was proposed in this paper,which combined the statistical features of leaf images and meteorological data.The images of infected crop leaves were taken under different environments of the growth periods,temperature and humidity.The methods of image morphological operation,contour extraction and region growing algorithm were adopted for leaf image enhancement and spot image segmentation.From each image of infected crop leaf,the statistical features of color,texture and shape were extracted by image processing,and the optimal meteorological features with the highest accuracy rate were obtained and selected by the attribute reduction algorithm.The fusion feature vector of the image was formed by combining the statistical features and the meteorological features.Then the probabilistic neural networks(PNNs)classifier was adopted to evaluate the classification accuracy.The experimental results on three cucumber diseased leaf image datasets,i.e.,downy mildew,blight and anthracnose,showed that the crop diseases can be effectively recognized by the integrated application of leaf image processing technology,the disease meteorological data and PNNs classifier,and the recognition accuracy rate was higher than 90%,which indicated that the PNNs classifier trained on the disease feature coefficients extracted from the crop disease leaves and meteorological data could achieve higher classification accuracy.

Crop Leaf Disease Recognition Network Based on Brain Parallel Interaction Mechanism

In the actual complex environment,the recognition accuracy of crop leaf disease is often not high.Inspired by the brain parallel interaction mechanism,a two-stream parallel interactive convolutional neural network(TSPI-CNN)is proposed to improve the recognition accuracy.TSPI-CNN includes a two-stream parallel network(TSP-Net)and a parallel interactive network(PI-Net).TSP-Net simulates the ventral and dorsal stream.PI-Net simulates the interaction between two pathways in the process of human brain visual information transmission.A large number of experiments shows that the proposed TSPI-CNN performs well on MK-D2,PlantVillage,Apple-3 leaf,and Cassava leaf datasets.Furthermore,the effect of numbers of interactions on the recognition performance of TSPI-CNN is discussed.The experimental results show that as the number of interactions increases,the recognition accuracy of the network also increases.Finally,the network is visualized to show the working mechanism of the network and provide enlightenment for future research.

Crop pest image recognition based on the improved ViT method Author links open overlay panel

The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality,which threaten macroeconomic stability and sustainable development.However,the recognition method based on manual and instruments has been unable to meet the needs of scientific research and production due to its strong subjectivity and low efficiency.The recognition method based on pattern recognition and deep learning can automatically fit image features,and use features to classify and predict images.This study introduced the improved Vision Transformer(ViT)method for crop pest image recognition.Among them,the region with the most obvious features can be effectively selected by block partition.The self-attention mechanism of the transformer can better excavate the special solution that is not an obvious lesion area.In the experiment,data with 7 classes of examples are used for verification.It can be illustrated from results that this method has high accuracy and can give full play to the advantages of image processing and recognition technology,accurately judge the crop diseases and pests category,provide method reference for agricultural diseases and pests identification research,and further optimize the crop diseases and pests control work for agricultural workers in need.