• Bikramjit Ghosh

Unidentified plant diseases cause a considerable annual crop yield loss for India each year. Manual inspection by farmers or specialists is the conventional approach of disease detection, which can be laborious and imprecise. Many small and medium-sized farms around the world are finding it to be unfeasible. A computer-aided disease recognition model is suggested as a solution to this problem. It makes use of deep convolutional networks for the classification of leaf images. In order to identify plant diseases, VGG16 and Resnet34 CNN were suggested in this work. The three processing processes are categorization, picture reduction, and feature extraction. The convolutional layer in CNN uses a plant image to extract features. The image is resized by the pooling layer.A thick layer of classification was used for diseases. Using a sample of 14 different plants, the suggested approach is able to distinguish 38 distinct plant illnesses from their surrounding foliage. It was compared how well Resnet34 and VGG16 performed. The performance metrics used were specificity, sensitivity, and accuracy. Providing farmers with tailored advice based on soil characteristics, temperature, and humidity is beneficial.

Plant disease, Convolutional neural network, VGG16, Resnet34, leaf image.

"Plant Leaf Disease Detection using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.d611-d619, November-2024, Available :http://www.jetir.org/papers/JETIR2411371.pdf

"Plant Leaf Disease Detection using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppd611-d619, November-2024, Available at : http://www.jetir.org/papers/JETIR2411371.pdf