Leaf Disease Classification Based On Edge Detection Using Training Neural Network


  • Mrs. O S Greeshma, Dr. P. Sasikala, Dr. S G Balakrishnan


Plant pathogens influence the growth of their respective species, thus, early detection is that helped to promote plant growth. This paper deals with a new strategy to design the Plant Disease Recognition Model based upon the Leaf Image Classification utilizing simple leaves images of normal and infected plants, via the Convolution Neural Network (CNN). It can be used to detect the edge, so it makes sure of impetus features extraction from image data collection on plant diseases and also aims to recognize. Augmentation and Fine Tuning approach is used to achieve the prediction of disease in an efficient manner. In addition, Peak Signal-to - Noise Ratio, Precision metrics which are measure the output of our proposed method and make comparisons it with the existing approaches. Then, proved that our model is enhanced the efficiency and accuracy of leaf disease recognition by achieving the better metrics in term of precision and PSNR.