Emon Yousuf Rayhan, Ahad Md Taimur, Rabbany Golam
Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka 1216, Bangladesh.
Data Brief. 2024 Jul 6;55:110713. doi: 10.1016/j.dib.2024.110713. eCollection 2024 Aug.
In Bangladesh, sweet orange cultivation has been popular among fruit growers as the fruit is in demand. However, the disease of sweet oranges decreases fruit production. Research suggests that computer-aided disease diagnosis and machine learning (IML) models can improve fruit production by detecting and classifying diseases. In this line, a dataset of sweet oranges is required to diagnose the disease. Moreover, like many other fruits, sweet disease may vary from country to country. Therefore, in Bangladesh, a dataset is required. Lastly, since different ML algorithms require datasets in various formats, only a few existing datasets fulfil the necessity. To fulfil the limitations, a sweet orange dataset in Bangladesh is collected. The dataset was collected in August and comprises high-quality images documenting multiple disease conditions, including . These images provide an opportunity to apply machine learning and computer vision techniques to detect and classify diseases. This dataset aims to help researchers advance agri engineering through ML. Other sweet orange growing countries with having similar environments may find helpful information. Lastly, such experiments using our dataset will assist farmers in taking preventive measures and minimising economic losses.
在孟加拉国，甜橙种植在果农中很受欢迎，因为这种水果有市场需求。然而，甜橙病害会降低水果产量。研究表明，计算机辅助病害诊断和机器学习（ML）模型可以通过检测和分类病害来提高水果产量。为此，需要一个甜橙数据集来诊断病害。此外，与许多其他水果一样，甜橙病害可能因国家而异。因此，在孟加拉国，需要一个数据集。最后，由于不同的ML算法需要各种格式的数据集，现有的数据集只有少数能满足这一需求。为了克服这些限制，我们收集了孟加拉国的一个甜橙数据集。该数据集于8月收集，包含记录多种病害情况的高质量图像，包括……这些图像为应用机器学习和计算机视觉技术检测和分类病害提供了机会。这个数据集旨在帮助研究人员通过机器学习推动农业工程发展。其他环境相似的甜橙种植国家可能会从中找到有用信息。最后，使用我们数据集进行的此类实验将帮助农民采取预防措施并将经济损失降至最低。
