Precision Agriculture: Using Deep Learning to Detect Tomato Crop Diseases

Modern farming practices such as precision agriculture make crop production more efficient. The earlier detection of plant diseases is one of the major challenges in the agricultural domain. There is currently a substantial time and accuracy gap between manual plant disease classification and counting. In order to prevent the damage that may be caused to plants, farmers, and the agricultural ecosystem at large, it is essential to detect different diseases of plants. The purpose of this project was to classify and detect plant diseases, especially those that affect tomato plants. We propose a deep convolutional neural network-based architecture for detecting and classifying leaf disease. Images from Unmanned Aerial Vehicles (UAVs) are used in the experiment. There is also a dataset of plant village images, a dataset of UAV images, real-time UAV images, and an image from the internet used as well. Hence, detection of diseases is done with more accuracy as multiple datasets are used.