Journal of Food, Agriculture and Environment

Vol 20, Issue 1,2022
Online ISSN: 1459-0263
Print ISSN: 1459-0263

A comparative study of paddy crop stresses using different models


Ashly Elizabeth Joshy,   S. Sreelakshmi, Fousia M. Shamsudeen

Recieved Date: 2021-10-29, Accepted Date: 2021-12-11


Early management and on-time recognition of the stresses within the paddy crops at the booting growth stage is the key to forestall qualitative and quantitative loss of agricultural yield. Traditional paddy crop stress recognition and classification activities invariably rely on human experts identifying visual symptoms as a means of categorization. At present deep learning is a trending research area in pattern recognition. A framework to design Deep Convolutional Neural Network (DCNN) for automated recognition and classification of yield affecting paddy crop stresses using field images is proposed. Four different classifiers, the Convolutional Neural Network (CNN), pre-trained VGG-16, Mobile Net and Inception V3 models have been deployed to distinguish biotic stresses such as Bacterial leaf blight, Fungal blast and Brown spot. The average stress classification accuracies 92%, 95%, 85% and 98% have been achieved using the CNN, VGG-16, MobileNet and Inception V3 classifiers, respectively.


Deep learning, classification, image processing, CNN, VGG-16, Mobile Net, Inception V3.

Journal: Journal of Food, Agriculture and Environment
Year: 2022
Volume: 20
Issue: 1
Category: Environment
Pages: 103-109

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