Agriculture is the backbone of Indian economy. Early detection of plant is very important for preventing the economic losses and increasing the productivity of the lemon fruits. This is the one of the reasons that disease detection in lemon plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area, then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases i.e., when they appear on plant leaves. This paper presents different deep learning (DL) technique for lemon plant disease detection to achieve a great potential in terms of increasing accuracy. In this we have used different conventional neural networks named GoogleNet, ResNet, and SqueezeNet models with and without data augmentation.Dataset used in this is collected from Mendeley data. The dataset of plant images that are collected under categories namely leaves. These images are further categorized using the diseases found in the plant. The dataset considers only the diseases that are common in most of the lemon plants. The dataset contains a total of 609 images and contain 227*227 dimensions. In each category, 70% images were used for training and 30% images are given into the testing process. The trained models give the best results with 97.66% with ResNet model.

Deep learning; Convolutional Neural Networks; Lemon leaf; Diseased and Healthy leaf; Training; Classification.