Gani Raiyan, Rashid Mohammad Rifat Ahmmad, Ahmed Jubaer, Isty Maherun Nessa, Islam Maheen, Hasan Mahamudul, Ferdaus Md Hasanul, Ali Md Sawkat
Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
Data Brief. 2024 Jun 6;55:110599. doi: 10.1016/j.dib.2024.110599. eCollection 2024 Aug.
Papaya, renowned for its nutritional benefits, represents a highly profitable crop. However, it is susceptible to various diseases that can significantly impede fruit productivity and quality. Among these, leaf diseases pose a substantial threat, severely impacting the growth of papaya plants. Consequently, papaya farmers frequently encounter numerous challenges and financial setbacks. To facilitate the easy and efficient identification of papaya leaf diseases, a comprehensive dataset has been assembled. This dataset, comprising approximately 1400 images of diseased, infected, and healthy leaves, aims to enhance the understanding of how these ailments affect papaya plants. The images, meticulously collected from diverse regions and under varying weather conditions, offer detailed insights into the disease patterns specific to papaya leaves. Stringent measures have been taken to ensure the dataset's quality and enhance its utility. The images, captured from multiple angles and boasting high resolution are designed to aid in the development of a highly accurate model. Additionally, RGB mode has been employed to meticulously capture each detail, ensuring a flawless representation of the leaves. The dataset meticulously identifies and categorizes five primary types of leaf diseases: Leaf Curl (inclusive of its initial stage), Papaya Mosaic, Ring Spot, Mites (specifically, those affected by Red Spider Mites), and Mealybug. These diseases are recognized for their detrimental effects on both the leaves and the overall fruit production of the papaya plant. By leveraging this curated dataset, it is possible to train a model for the real-time detection of leaf diseases, significantly aiding in the timely identification of such conditions.
木瓜因其营养价值而闻名，是一种利润丰厚的作物。然而，它易受各种疾病影响，这些疾病会严重阻碍果实产量和品质。其中，叶部病害构成重大威胁，严重影响木瓜植株的生长。因此，木瓜种植户经常面临诸多挑战和经济挫折。为便于轻松、高效地识别木瓜叶部病害，已收集了一个综合数据集。该数据集包含约1400张患病、受感染和健康叶片的图像，旨在增进对这些病害如何影响木瓜植株的了解。这些图像是从不同地区、在不同天气条件下精心收集的，能详细洞察木瓜叶特有的病害模式。已采取严格措施确保数据集的质量并提高其效用。从多个角度拍摄且具有高分辨率的图像旨在帮助开发高度准确的模型。此外，采用RGB模式精心捕捉每个细节，确保叶片的完美呈现。该数据集精心识别并分类了五种主要的叶部病害：卷叶病（包括初期阶段）、木瓜花叶病、环斑病、螨类（特别是受红蜘蛛影响的螨类）和粉蚧。这些病害因其对木瓜植株的叶片和整体果实产量的有害影响而为人所知。通过利用这个精心策划的数据集，可以训练一个用于实时检测叶部病害的模型，极大地有助于及时识别此类情况。
