构建首个孟加拉稻叶病数据集，用迁移学习实现91.5%诊断准确率。
Empowering Agricultural Insights: RiceLeafBD -- A Novel Dataset and Optimal Model Selection for Rice Leaf Disease Diagnosis through Transfer Learning Technique
- 基于孟加拉田间数据构建新稻叶病数据集，覆盖真实场景。
- EfficientNet-V2模型在该数据集上达到91.5%准确率，超越多数现有方法。
- 成果可为农业病害早期预警提供可靠技术支撑，适合乡村振兴研究者参考。
本研究针对孟加拉这一农业国家面临的粮食危机问题，提出一种新的稻叶病数据集RiceLeafBD，该数据集源自孟加拉实地采集。为应对稻叶病导致的产量下降挑战，研究采用轻量级CNN及预训练模型InceptionNet-V2、EfficientNet-V2、MobileNet-V2进行迁移学习。实验表明，EfficientNet-V2模型在该数据集上取得91.5%的识别准确率，性能优于其他模型并超越部分现有先进技术。研究验证了使用该无偏数据集进行稻叶病精准识别的可行性，为后续农业病害智能诊断研究提供了重要基础与方向。
原文摘要 · Abstract (English)
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.
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