Utilizing Resnet - 50 for Accurate Plant Disease Identification

In emerging nations like India, where agriculture is significant, food security is still a critical concern.Due to transportation issues, plant diseases, and a lack of storage facilities, most crops are squandered.The Food and Agriculture Organization of the United Nations (FAO) estimates that illnesses cause the loss of 20% or more of the crops farmed in India.It would take a lot of food to feed everyone in this area.therefore, making it one of the most important issues to be tackled.Deep Learning is the most advantageous approach to begin solving this problem because of its vast libraries, user-and developer-friendly environment, and developer-friendly tools.To enhance the information from the photos of diseased leaves, pixel-based techniques are done to the photographs.Next, feature extraction is carried out, then picture segmentation, and finally, crop illnesses are classified based on the patterns retrieved from the sick leaves.The CNN-ResNet is employed for illness categorization.A deep learning model called Residual Network (ResNet) is applied in computer vision applications.It is an architecture for a convolutional neural network (CNN) that can accommodate hundreds or even thousands of convolutional layers.