Advanced Grape Leaf Disease Detection using Neural Network

In the realm of image categorization, the most recent iteration of convolutional neural networks, often known as CNNs, has produced quite excellent results. This study explores a novel method for the construction of a plant disease detection model that is based on the classification of leaf images and makes use of deep convolutional networks. The results of this research are shown below. Innovative ways of training and the methodology that is employed make it possible to put the system into operation quickly and without difficulty. The model that was constructed has the capability of distinguishing plant leaves from their surrounding environment, which enables it to identify various sorts of plant illnesses that may be seen on healthy leaves. This approach for identifying plant diseases has just been put forth for consideration for the first time. Beginning with the collection of images for the purpose of creating a database will be evaluated by agricultural specialists, every key step that is necessary for implementing this disease detection model is clearly outlined in this study. In order to perform disease prediction, a neural network has been used. The results of the experiments conducted on the newly constructed model has obtained a level of accuracy ranging from 85% to 95% for individual class assessments with an average of 96.3%.