Abstract
Crop disease is a major issue now days; as it drastically reduces food production rate. Tomato is cultivated in major part of the world. The most common diseases that affect tomato crops are bacterial spot, early blight, septoria leaf spot, late blight, leaf mold, target spot, etc. In order to increase the production rate of tomato, early identification of diseases is highly required. The existing work contains very less accurate system for identification of tomato crop diseases. The goal of our work is to propose cost effective and efficient deep learning model inspired from Alexnet for identification of tomato crop diseases. To validate the performance of proposed model, experiments have also been done on standard pretrained models. The plantVillage dataset is used for the same, which contains 18,160 images of diseased and non-diseased tomato leaf. The disease identification accuracy of proposed model is compared with standard pretrained models and found that proposed model gave more promising results for tomato crop diseases identification.
Highlights
The crop of Tomato is mostly cultivated in a wide area of the globe as it contains three major antioxidants vitamin E, beta-carotene, and vitamin C
The disease identification accuracy of the proposed model is compared with standard pretrained models, and it is found that proposed model gave more promising results for tomato crop disease identification
3.6 Contribution The major contribution of this paper are as follows We proposed light weight deep convolutional neural network for identification of various tomato crop disease
Summary
The crop of Tomato is mostly cultivated in a wide area of the globe as it contains three major antioxidants vitamin E, beta-carotene, and vitamin C. Tomato leaf diseases cause a major loss in quality and production rates. Some common diseases that affect tomato crops are early blight, septoria leaf spot, two-spotted spider mite, target spot, bacterial spot, mosaic virus, late blight, curl virus, etc. It is very difficult for a human to recognize an accurate class of diseases. Incorrect prediction of diseases causes inaccurate usage of pesticides, which causes loss of quality and production of food. The recognition of diseases plays a major role in the field of agriculture
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