Plant Leaf Disease Diagnosis Using CNN and Fertilizer Recommendation

Agriculture is the most popular employment in our country. Most people are employed in agriculture and completely dependent on agricultural goods. When a plant becomes infected, both the quality and amount of grain produced are reduced. As an outcome, disease detection and analysis are necessary. Accurate crop disease detection and exposure are crucial for disease management and prevention in profitable agriculture and food preservation. Hence, early disease identification and diagnosis are critical for farmers. Using a convolutional neural network, this proposed approach provides a technique for identifying leaf disease and choosing fertilizers. This proposed system includes numerous image processing techniques such as image acquisition, image pre-processing, CNN-based training, classification, diagnosis, and medication using convolutional neural networks. We can now acquire the disease's name as well as the particular fertilizers for that disease using the present method. However, previous attempts failed, so we proposed this idea, which is easier and gives more performance than other processing systems.