Abstract
Plant disease detection is an important field of study since early detection can drastically minimize crop losses and enhance agricultural productivity. Pathogens like fungi, bacteria, and viruses are responsible for most plant diseases, which can seriously affect plant health and yield. In this research, a pre-trained convolutional neural network (CNN) algorithm, VGG 16 is used to classify various leaf diseases with very high accuracy, taking advantage of deep learning methods in observing visual symptoms on leaves. The model takes the input image of a diseased leaf, extracts hierarchical features using its multi-layered architecture, and determines the type of disease, allowing for early and accurate diagnosis. Moreover, the system is designed to recommend fertilizer based on the disease identified, enabling farmers to take necessary action to reduce damage and enhance crop yield. By combining cutting-edge AI with agricultural knowledge, this method presents a scalable and effective solution to disease management, enabling sustainable agriculture and food security.
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