PADDY DOCTOR: AN AUTOMATED VISUAL IMAGE SENSING MODEL FOR PADDY DISEASE CLASSIFICATION
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There is an increasing demand for automated systems capable of accurately diagnosing paddy diseases, which would help lower pesticide usage and prevent yield loss. Yet, the absence of publicly available datasets with annotated disease labels has posed a challenge to the development and benchmarking of advanced deep learning models. To address this issue, we created and open-sourced the Paddy Doctor dataset, facilitating the development of reliable and effective paddy disease diagnosis systems."Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarking" is a specialized dataset designed to aid in the development and evaluation of machine learning models for the automated classification of diseases in paddy (rice) plants. This dataset typically includes a diverse collection of high-quality images of paddy leaves affected by various diseases, along with healthy samples. Each image is labeled according to the type of disease or condition it represents.
精准诊断水稻病害的自动化系统需求日益增长，此类系统可助力降低农药使用量并规避产量损失。然而，当前缺乏带有标注病害标签的公开可用数据集，这给先进深度学习模型的开发与性能基准测试带来了挑战。为解决这一问题，我们构建并开源了稻医（Paddy Doctor）数据集，以推动可靠且高效的水稻病害诊断系统的开发。《稻医：面向自动化水稻病害分类与基准测试的视觉图像数据集》（Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarking）是一款专为辅助开发、评估自动化水稻植株病害分类机器学习模型而设计的专用数据集。该数据集涵盖品类丰富的高质量水稻叶片图像，包含多种病害侵染的样本及健康叶片样本。每张图像均按照其对应的病害类型或健康状态进行标注。
