Rice Leaf Disease Detection: A Comparative Study Between CNN, Transformer and Non-neural Network Architectures
This work addresses early disease detection for rice farmers in Bangladesh, but it is incremental as it applies existing methods to a new dataset.
The paper tackled rice leaf disease detection in Bangladesh by comparing CNN, transformer, and non-neural network architectures on the Dhan-Shomadhan dataset, finding that ResNet50 performed best among the models tested.
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.