Agriculture plays a crucial role in the economic growth of a country as it is one of the main means of subsistence. Recently, technological methods have been designed for the identification of plants and detection of their diseases in order to meet the new challenges facing farmers and their learning needs. Crop diseases are a noteworthy risk to sustenance security, however their quick distinguishing proof stays troublesome in numerous parts of the world because of the non attendance of the important foundation. Our proposed project includes various phases of implementation namely dataset importing, feature extraction, training the classifier and classification In this project, convolutional neural network models are developed to perform plant disease detection through deep learning methodologies. Training of the models was performed with the use of an open database from plant village website that consists of a set of 38 distinct classes of plant, disease combinations, including healthy plants. Several model architectures were trained, with the best performance in identifying the corresponding plant, disease combination (or healthy plant).
1. Modified Alexnet
2. Modified Inception
3. Modified VGG
- Plant Disease Detection Reference Paper 00:00:00
- Plant Disease Detection Synopsis 00:00:00
- Plant Disease Detection Project Video 00:00:00