Sayeem Adnan Rahman, Omi Jannatul Ferdous, Hasan Mehedi, Mojumdar Mayen Uddin, Chakraborty Narayan Ranjan
Multidisciplinary Action Research Laboratory, Department of Computer Science and Engineering, Daffodil International University, Birulia, Dhaka 1216, Bangladesh.
Data Brief. 2025 Jan 10;58:111293. doi: 10.1016/j.dib.2025.111293. eCollection 2025 Feb.
Agriculture has always played a vital role in the economic development of Bangladesh. In Agriculture, leaf diseases have become an issue because they can lead to a major drop in both quality and quantity of crops. Therefore, leveraging technology to automatically detect diseases on leaves plays an important role in farming. Malabar Spinach (Basella alba) is a well-known, widely grown leafy vegetable, which is valued for its nutritional benefits. However, there is almost no dataset that can aid in identifying diseases affecting this important crop, which often leads to decreased quality as well as financial drawback. This lack of resources makes it difficult for farmers to recognize and manage common diseases. Our purpose is to solve this problem by creating a unique dataset of Bangladesh's Malabar Spinach leaves that will ease agricultural management and disease detection. Our dataset contains both healthy and diseased samples, categorised into four common ailments: Anthracnose, Bacterial Spot, Downy Mildew, and Pest Damage. We collected 3,006 original images in total. Images were collected from various locations in Bangladesh, including Mirpur, Savar, Sirajganj and Gazipur, with photographs taken under natural lighting conditions at different times of the day. This dataset will help the researchers for further research on Malabar Spinach disease detection implementing various efficient computational models and applying advanced machine learning techniques.
农业在孟加拉国的经济发展中一直发挥着至关重要的作用。在农业领域，叶片病害已成为一个问题，因为它们会导致作物的质量和产量大幅下降。因此，利用技术自动检测叶片病害在农业生产中起着重要作用。马拉巴菠菜（落葵）是一种知名的、广泛种植的叶菜类蔬菜，因其营养价值而受到重视。然而，几乎没有数据集能够帮助识别影响这种重要作物的病害，这往往会导致质量下降以及经济损失。资源的匮乏使得农民难以识别和管理常见病害。我们的目的是通过创建一个独特的孟加拉国马拉巴菠菜叶片数据集来解决这个问题，该数据集将便于农业管理和病害检测。我们的数据集包含健康和患病样本，分为四种常见病害：炭疽病、细菌性叶斑病、霜霉病和虫害。我们总共收集了3006张原始图像。图像从孟加拉国的各个地点收集，包括米尔布尔、萨瓦尔、锡拉杰甘杰和加济布尔，照片在一天中的不同时间在自然光照条件下拍摄。该数据集将帮助研究人员利用各种高效的计算模型并应用先进的机器学习技术，对马拉巴菠菜病害检测进行进一步研究。
