Plant Disease Detection and Crop Recommendation using Deep Learning

Agricultural productivity is a key component of the Indian economy, for over 70% of India's agricultural people, agriculture is their main source of income. Plant diseases are the main reason why agricultural productivity has declined in both quality and volume. It is extremely difficult for a farmer to recognize and manage plant illnesses. The majority of plant illnesses manifest in the leaves of the affected plant. The illness that harms a plant is recognized using leaf photos. This proposed system focuses on certain deep-learning techniques to identify plant illnesses while also proposing which crop variety is optimal for production in that specific area. To make the proposed system more robust, proper training and testing have been shown with leaf datasets. The information set contains both healthy and unhealthy leaf images which are reported. First, the model suggests the best crop for the farmer using machine learning methods like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), and Decision Tree (DT). Lastly, the disease is detected by using a deep learning technique called Convolutional Neural Network (CNN) by comparing the input image and trained image. This proposed system would be capable of solving real-world problems and be a better approach concerning other state-of-the-art methods.