Rice Leaf Disease Detection: A Comparative Study Between CNN, Transformer and Non-neural Network Architectures

TL;DR

This study compares CNN, Transformer, and traditional machine learning methods for rice leaf disease detection in Bangladesh, highlighting ResNet50's superior performance using transfer learning on a local dataset.

Contribution

It provides a comparative analysis of deep learning architectures and traditional methods for rice disease detection, emphasizing the effectiveness of ResNet50 with transfer learning.

Findings

ResNet50 outperformed other models in accuracy.

Transformers are competitive with CNNs in this task.

Traditional SVM lagged behind deep learning models.

Abstract

In nations such as Bangladesh, agriculture plays a vital role in providing livelihoods for a significant portion of the population. Identifying and classifying plant diseases early is critical to prevent their spread and minimize their impact on crop yield and quality. Various computer vision techniques can be used for such detection and classification. While CNNs have been dominant on such image classification tasks, vision transformers has become equally good in recent time also. In this paper we study the various computer vision techniques for Bangladeshi rice leaf disease detection. We use the Dhan-Shomadhan -- a Bangladeshi rice leaf disease dataset, to experiment with various CNN and ViT models. We also compared the performance of such deep neural network architecture with traditional machine learning architecture like Support Vector Machine(SVM). We leveraged transfer learning for better generalization with lower amount of training data. Among the models tested, ResNet50 exhibited the best performance over other CNN and transformer-based models making it the optimal choice for this task.