赋能农业洞察：RiceLeafBD——通过迁移学习技术进行水稻叶片病害诊断的新型数据集与最优模型选择
Empowering Agricultural Insights: RiceLeafBD -- A Novel Dataset and Optimal Model Selection for Rice Leaf Disease Diagnosis through Transfer Learning Technique
AI总结：
本文构建采集自孟加拉国田间的水稻叶片病害数据集 RiceLeafBD，并比较轻量级 CNN 与多种迁移学习模型，EfficientNet-V2 以 91.5% 准确率实现最优诊断性能。
摘要：
我们这个被郁郁葱葱的绿色植被环绕的农业国家，生活人口每天都在增长。由此导致耕地面积不断减少，住宅房屋和工业工厂也在侵占土地。粮食危机正成为我们未来面临的主要威胁，因为一方面人口不断增加，另一方面因病害侵袭，粮食作物产量却在下降。水稻是最重要的栽培作物之一，为全球一半以上人口提供食物。孟加拉国依赖水稻（Oryza sativa）作为其农业的重要作物，但由于常见病害导致水稻产量持续下降，该国面临重大问题。早期病害检测是水稻作物种植中的主要难题。本文中，我们提出了自己的数据集，该数据集采集自孟加拉国的田间，并应用深度学习和迁移学习模型对数据集进行评估。我们详细阐述了该数据集，并为利用该数据集服务社会的进一步研究工作提供方向。我们应用了一个轻量级 CNN 模型以及预训练的 InceptionNet-V2、EfficientNet-V2 和 MobileNet-V2 模型，其中本研究的 EfficientNet-V2 模型取得了 91.5% 的性能。所得结果击败了其他模型，甚至超过了被认为属于最先进水平的方法。本研究表明，利用这一无偏数据集，可以精确而有效地识别影响水稻叶片的病害。在分析不同模型的性能之后，所提出的数据集对于社会研究工作具有重要意义，可为减少水稻叶片病害提供解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
