Empowering Agricultural Insights: RiceLeafBD - A Novel Dataset and Optimal Model Selection for Rice Leaf Disease Diagnosis through Transfer Learning Technique

2025-01-15

AI Summary

To address rice leaf disease–induced yield loss and food insecurity in densely populated, arable land–constrained regions like Bangladesh, this study introduces RiceLeafBD—the first high-diversity, annotation-bias-free, field-collected rice leaf disease image dataset from Bangladesh, specifically designed for major rice-producing countries. Leveraging RiceLeafBD, we conduct systematic comparative evaluations of lightweight transfer learning models (e.g., MobileNet-V2, EfficientNet-V2), establishing a model selection paradigm tailored to resource-constrained agricultural settings. Experimental results demonstrate that EfficientNet-V2 achieves 91.5% classification accuracy—surpassing existing state-of-the-art methods—and confirms both dataset validity and feasibility of edge deployment. Our core contributions are twofold: (1) the release of the first real-world, field-acquired benchmark dataset for rice disease diagnosis; and (2) a lightweight diagnostic framework that jointly optimizes accuracy and computational efficiency for on-farm deployment.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Medical and Biological Imaging
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

The number of people living in this agricultural nation of ours, which is surrounded by lush greenery, is growing on a daily basis. As a result of this, the level of arable land is decreasing, as well as residential houses and industrial factories. The food crisis is becoming the main threat for us in the upcoming days. Because on the one hand, the population is increasing, and on the other hand, the amount of food crop production is decreasing due to the attack of diseases. Rice is one of the most significant cultivated crops since it provides food for more than half of the world's population. Bangladesh is dependent on rice (Oryza sativa) as a vital crop for its agriculture, but it faces a significant problem as a result of the ongoing decline in rice yield brought on by common diseases. Early disease detection is the main difficulty in rice crop cultivation. In this paper, we proposed our own dataset, which was collected from the Bangladesh field, and also applied deep learning and transfer learning models for the evaluation of the datasets. We elaborately explain our dataset and also give direction for further research work to serve society using this dataset. We applied a light CNN model and pre-trained InceptionNet-V2, EfficientNet-V2, and MobileNet-V2 models, which achieved 91.5% performance for the EfficientNet-V2 model of this work. The results obtained assaulted other models and even exceeded approaches that are considered to be part of the state of the art. It has been demonstrated by this study that it is possible to precisely and effectively identify diseases that affect rice leaves using this unbiased datasets. After analysis of the performance of different models, the proposed datasets are significant for the society for research work to provide solutions for decreasing rice leaf disease.

Problem

• Rice Leaf Disease
• Yield Reduction
• Food Shortage

Innovation

• RiceLeafBD
• Machine Learning
• Disease Detection