Alam B M Shahria, Ahammed Fahad, Kibria Golam, Noor Mohammad Tahmid, Shikdar Omar Faruq, Mahzabin Kazi Isat, Niloy Nishat Tasnim, Ali Md Nawab Yousuf
Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka 1212, Bangladesh.
Data Brief. 2025 Jun 10;61:111769. doi: 10.1016/j.dib.2025.111769. eCollection 2025 Aug.
Tea is an extremely popular beverage around the world due to its exquisite taste and flavor. Unfortunately, it is prone to different types of illness, which can reduce the amount of harvest along with its standard. Among these, leaf infections are a serious concern since they negatively affect the quality of tea leaves. As a consequence, tea producers often encounter a great deal of obstacles and financial losses. Keeping this in mind, a thorough dataset has been compiled, which contains 5278 images of diseased and healthy leaves. The purpose of this dataset is to improve our knowledge of how these conditions impact cultivating tea plants and tea production. These images are collected from a variety of locations and meteorological circumstances, which provide an extensive knowledge of the disease patterns unique to tea leaves. The pictures have been captured with the help of some high-quality devices from different angles and in high resolution to ensure the standard and increase the usability of the dataset. Rigorous steps were followed when preparing the dataset that would be of great help in building a precise artificial intelligence model. The dataset carefully determined and classified six tea leaf diseases: Tea algal leaf spot, Brown Blight, Gray Blight, Helopeltis, Red spider, and Green mirid bug. There is one more class in the dataset containing images of healthy leaves. These illnesses are known for their devastating impact on tea leaves. An automated disease classification system can be made utilizing deep learning techniques that will enable estate managers to take timely action to stop the spread of the disease, and this meticulously collected dataset will immensely help to train that model.
茶因其精致的口感和风味而在全球范围内广受欢迎。不幸的是，它容易患上不同类型的疾病，这会减少收成并降低其标准。其中，叶片感染是一个严重问题，因为它们会对茶叶质量产生负面影响。因此，茶叶生产商经常遇到诸多障碍并遭受经济损失。考虑到这一点，已编制了一个全面的数据集，其中包含5278张患病和健康叶片的图像。该数据集的目的是增进我们对这些病症如何影响茶树种植和茶叶生产的了解。这些图像是从各种地点和气象条件下收集的，提供了关于茶叶特有病害模式的广泛知识。这些图片借助一些高质量设备从不同角度以高分辨率拍摄，以确保数据集的标准并提高其可用性。在准备数据集时遵循了严格的步骤，这对构建精确的人工智能模型将有很大帮助。该数据集仔细确定并分类了六种茶叶病害：茶藻斑病、赤叶斑病、灰斑病、茶网蝽、红蜘蛛和绿盲蝽。数据集中还有一类包含健康叶片的图像。这些病害以对茶叶的毁灭性影响而闻名。利用深度学习技术可以建立一个自动病害分类系统，这将使茶园管理者能够及时采取行动阻止病害传播，而这个精心收集的数据集将极大地有助于训练该模型。
