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Identification of Tomato Leaf Diseases Using Deep Convolutional Neural Networks

Identification of Tomato Leaf Diseases Using Deep Convolutional Neural Networks

Ganesh Bahadur Singh, Rajneesh Rani, Nonita Sharma, Deepti Kakkar
Copyright: © 2021 |Volume: 12 |Issue: 4 |Pages: 22
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781799861614|DOI: 10.4018/IJAEIS.20211001.oa3
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MLA

Singh, Ganesh Bahadur, et al. "Identification of Tomato Leaf Diseases Using Deep Convolutional Neural Networks." IJAEIS vol.12, no.4 2021: pp.1-22. http://doi.org/10.4018/IJAEIS.20211001.oa3

APA

Singh, G. B., Rani, R., Sharma, N., & Kakkar, D. (2021). Identification of Tomato Leaf Diseases Using Deep Convolutional Neural Networks. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 12(4), 1-22. http://doi.org/10.4018/IJAEIS.20211001.oa3

Chicago

Singh, Ganesh Bahadur, et al. "Identification of Tomato Leaf Diseases Using Deep Convolutional Neural Networks," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 12, no.4: 1-22. http://doi.org/10.4018/IJAEIS.20211001.oa3

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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.