State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
Nucleic Acids Res. 2024 Jan 5;52(D1):D1556-D1568. doi: 10.1093/nar/gkad917.
Plant disease, a huge burden, can cause yield loss of up to 100% and thus reduce food security. Actually, smart diagnosing diseases with plant phenomics is crucial for recovering the most yield loss, which usually requires sufficient image information. Hence, phenomics is being pursued as an independent discipline to enable the development of high-throughput phenotyping for plant disease. However, we often face challenges in sharing large-scale image data due to incompatibilities in formats and descriptions provided by different communities, limiting multidisciplinary research exploration. To this end, we build a Plant Phenomics Analysis of Disease (PlantPAD) platform with large-scale information on disease. Our platform contains 421 314 images, 63 crops and 310 diseases. Compared to other databases, PlantPAD has extensive, well-annotated image data and in-depth disease information, and offers pre-trained deep-learning models for accurate plant disease diagnosis. PlantPAD supports various valuable applications across multiple disciplines, including intelligent disease diagnosis, disease education and efficient disease detection and control. Through three applications of PlantPAD, we show the easy-to-use and convenient functions. PlantPAD is mainly oriented towards biologists, computer scientists, plant pathologists, farm managers and pesticide scientists, which may easily explore multidisciplinary research to fight against plant diseases. PlantPAD is freely available at http://plantpad.samlab.cn.
植物病害是一个巨大的负担，它可能导致高达 100%的产量损失，从而降低粮食安全。实际上，利用植物表型组学智能诊断疾病对于挽回最大产量损失至关重要，这通常需要充足的图像信息。因此，表型组学被视为一门独立的学科，以实现高通量植物病害表型分析。然而，由于不同社区提供的格式和描述不兼容，我们在共享大规模图像数据方面经常面临挑战，限制了多学科研究的探索。为此，我们构建了一个具有大规模疾病信息的植物表型分析疾病（PlantPAD）平台。我们的平台包含 421314 张图像、63 种作物和 310 种疾病。与其他数据库相比，PlantPAD 具有广泛、标注良好的图像数据和深入的疾病信息，并提供用于准确植物病害诊断的预训练深度学习模型。PlantPAD 支持多个学科的各种有价值的应用，包括智能疾病诊断、疾病教育以及高效的疾病检测和控制。通过 PlantPAD 的三个应用案例，我们展示了其易于使用和方便的功能。PlantPAD 主要面向生物学家、计算机科学家、植物病理学家、农场经理和农药科学家，他们可以轻松探索多学科研究来对抗植物病害。PlantPAD 可在 http://plantpad.samlab.cn 免费获取。
