Rice Leaf Disease Detection Using VGG-16 Based CNN with Transfer Learning
Year: 2025
Type: article
Abstract: Rice is one of the principal crop plants in India which is plagued by numerous diseases at different phases of its cultivation. It becomes extremely challenging for the farmers to do this manually accurately with their limited knowledge. Recent advances in Deep Learning indicate that Automatic Image Recognition systems based on Convolutional Neural Network (CNN) models can be highly useful in such problems. Given that rice leaf disease image dataset is not readily available, we have developed our own dataset which is small in size thus we have utilized Transfer Learning to train our deep learning model. The proposed CNN structure is derived from VGG-16 and trained and tested on the dataset that we gathered from rice fields and the internet. The proposed model's accuracy is 92.46%.
Authors: Thati Venkata M.lakshmi, K.Venkata Rao
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W6893399344
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