Chilli Leaf Disease Detection Using Deep Learning

Deep learning is being used a lot to develop a quick, automatic and reliable means for image identification and classification in many domains. Using Deep learning techniques in the agriculture would be an enhanced practice in the history of agriculture. Chillies are one of the most popular crops in India as they are used in everyday life for cooking variety of dishes. Chilli plants are sensitive to multiple infections. Detection and prevention of these diseases to other parts of the plant is a very important and impracticable task in the case of large fields. This paper proposes a disease detection and classification model for chilli leaves using Convolution Neural Networks. And also, other pre-trained architectures like ResNet, Inception, VGG (Visual Geometry Group) and Efficient Net were used for building an optimized model which detects the diseases more accurately. Images of Chilli leaves, having various diseases named Leaf curl, Leaf spot, Yellowish, as well as Healthy leaves, from the self-made dataset were used. The results of the Efficient-Net model prevailed over other models with accuracies of CNN (Convolutional Neural Networks), ResNet, VGG and Efficient Net 70%,87%,87% and 91% respectively.