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.