Ali Md Sawkat, Rashid Mohammad Rifat Ahmmad, Hossain Tasnim, Kabir Md Ahsan, Kamrul Md, Aumy Sayam Hossain Bhuiyan, Mridha Mehedi Hasan, Sajeeb Imam Hossain, Islam Mohammad Manzurul, Jabid Taskeed
Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh.
Department of Electrical, Electronic and Communication Engineering, Military Institute of Science and Technology, Dhaka, Bangladesh.
Data Brief. 2024 Sep 28;57:110981. doi: 10.1016/j.dib.2024.110981. eCollection 2024 Dec.
In agricultural research, particularly concerning rice cultivation, the presence of weeds within rice fields is acknowledged as a significant contributor to both diminished crop quality and increased production costs. Rice fields, due to their inherently moist environment, offer ideal conditions for weed proliferation. Traditionally, the control of these weeds has been managed through labor-intensive manual methods. However, as the agricultural sector evolves, there is a notable pivot towards leveraging advanced technological solutions, including deep learning and machine learning. The efficacy of these technologies hinges on the availability of high-quality, relevant data. To address this, a comprehensive dataset comprising 3632 high-resolution RGB images has been developed. This dataset is designed to capture a diverse range of weed species, specifically 11 types that are frequently found in rice fields. The diversity of the dataset ensures that machine learning models trained using this data can effectively identify and differentiate between desired and undesired plant species. While the dataset predominantly includes images from Bangladesh, the weed species it documents are commonly found across various global rice-growing regions, enhancing the dataset's applicability in different agricultural settings.
在农业研究中，尤其是在水稻种植方面，稻田中杂草的存在被认为是导致作物品质下降和生产成本增加的重要因素。由于稻田本身湿度较大，为杂草的繁殖提供了理想条件。传统上，这些杂草的控制是通过劳动密集型的人工方法进行的。然而，随着农业领域的发展，出现了明显的转向，即利用包括深度学习和机器学习在内的先进技术解决方案。这些技术的有效性取决于高质量、相关数据的可用性。为了解决这个问题，已经开发了一个包含3632张高分辨率RGB图像的综合数据集。该数据集旨在捕捉各种杂草物种，特别是在稻田中经常发现的11种类型。数据集的多样性确保了使用这些数据训练的机器学习模型能够有效地识别和区分所需和不需要的植物物种。虽然该数据集主要包括来自孟加拉国的图像，但它记录的杂草物种在全球各个水稻种植地区都很常见，从而提高了该数据集在不同农业环境中的适用性。
