# An Enhancement of CNN Algorithm for Rice Leaf Disease Image Classification in Mobile Applications

Source: [An Enhancement of CNN Algorithm for Rice Leaf Disease Image Classification in Mobile Applications](<https://www.sota2.com/research/paper/68fa77071e209131cea709a9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:08:06.137Z

Authors: Kayne Uriel K. Rodrigo, Jerriane Hillary Heart S. Marcial, Samuel C. Brillo, Khatalyn E. Mata, Jonathan C. Morano

Published: 2024-12-10T04:41:10.000Z

## Abstract

This study focuses on enhancing rice leaf disease image classification algorithms, which have traditionally relied on Convolutional Neural Network (CNN) models. We employed transfer learning with MobileViTV2\_050 using ImageNet-1k weights, a lightweight model that integrates CNN's local feature extraction with Vision Transformers' global context learning through a separable self-attention mechanism. Our approach resulted in a significant 15.66% improvement in classification accuracy for MobileViTV2\_050-A, our first enhanced model trained on the baseline dataset, achieving 93.14%. Furthermore, MobileViTV2\_050-B, our second enhanced model trained on a broader rice leaf dataset, demonstrated a 22.12% improvement, reaching 99.6% test accuracy. Additionally, MobileViTV2-A attained an F1-score of 93% across four rice labels and a Receiver Operating Characteristic (ROC) curve ranging from 87% to 97%. In terms of resource consumption, our enhanced models reduced the total parameters of the baseline CNN model by up to 92.50%, from 14 million to 1.1 million. These results indicate that MobileViTV2\_050 not only improves computational efficiency through its separable self-attention mechanism but also enhances global context learning. Consequently, it offers a lightweight and robust solution suitable for mobile deployment, advancing the interpretability and practicality of models in precision agriculture.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2412.07182>)

[PDF](<https://arxiv.org/pdf/2412.07182.pdf>)