Plant disease classification using deep learning
Akshai KP, J. Anitha
Abstract
Agriculture plays a crucial role in the Indian economy. Early detection of plant diseases is very much essential to prevent crop loss and further spread of diseases. Most plants such as apple, tomato, cherry, grapes show visible symptoms of the disease on the leaf. These visible patterns can be identified to correctly predict the disease and take early actions to prevent it. The conventional method is the farmers or plant pathologists manually observe the plant leaf and identify the type of disease. In this project, a deep learning model is trained to classify the different plant diseases. The convolutional neural network (CNN) model is used due to its massive success in image-based classification. The deep learning model provides faster and more accurate predictions than manual observation of the plant leaf. In this work, the CNN model and pre-trained models such as VGG, ResNet, and DenseNet models are trained using the dataset. Among them, the DenseNet model achieves the highest accuracy.