Application of convolutional neural networks for evaluation of disease severity in tomato plant. Verma, S., Chug, A., & Singh, A. P. Journal of Discrete Mathematical Sciences and Cryptography, 23(1):273–282, January, 2020. ![Application of convolutional neural networks for evaluation of disease severity in tomato plant [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex For food security in future, precise measurements of disease incidence and severity are crucial for suitable treatments and adopting preventive measures. In this paper, the authors have implemented three well known CNN models, namely, AlexNet, SqueezeNet and Inception V3, for evaluating disease severity in Tomato Late Blight disease. The images utilized were selected from the PlantVillage dataset and separated into three stages (early, middle and end) of disease severity. The CNN architectures were implemented in two different modes, i.e. transfer learning and feature extraction (where the extracted feature set was used to train a multiclass SVM). As compared to the other two networks, AlexNet achieved the highest accuracy in both approaches, 89.69% and 93.4% respectively.
@article{verma_application_2020,
title = {Application of convolutional neural networks for evaluation of disease severity in tomato plant},
volume = {23},
issn = {0972-0529, 2169-0065},
url = {https://www.tandfonline.com/doi/full/10.1080/09720529.2020.1721890},
doi = {10.1080/09720529.2020.1721890},
abstract = {For food security in future, precise measurements of disease incidence and severity are crucial for suitable treatments and adopting preventive measures. In this paper, the authors have implemented three well known CNN models, namely, AlexNet, SqueezeNet and Inception V3, for evaluating disease severity in Tomato Late Blight disease. The images utilized were selected from the PlantVillage dataset and separated into three stages (early, middle and end) of disease severity. The CNN architectures were implemented in two different modes, i.e. transfer learning and feature extraction (where the extracted feature set was used to train a multiclass SVM). As compared to the other two networks, AlexNet achieved the highest accuracy in both approaches, 89.69\% and 93.4\% respectively.},
language = {en},
number = {1},
urldate = {2020-05-14},
journal = {Journal of Discrete Mathematical Sciences and Cryptography},
author = {Verma, Shradha and Chug, Anuradha and Singh, Amit Prakash},
month = jan,
year = {2020},
keywords = {68T45, 68U10, Agriculture, Convolutional Neural Networks, Deep Learning, Disease Severity, Multiclass SVM, Plant Diseases, Tomato Late Blight},
pages = {273--282},
}
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