Agarwood Leaf Image Dataset for Pest and Disease Analysis in Real-World Environment

The dataset comprises a total of 5,472 images of agarwood leaves curated and collected 14 classes from Benutan, Bukit Silat, Batong, Brunei Darussalam. Among which, 4,401 images were collected on agarwood diseases in 9 classes, including Downy mildew, Anthracnose, Black spots, Powdery mildew, Translucent lesions, Brown spots, Mosaic Viruses, Sooty mold, and one of which is healthy class and 1,071 images of Spider and Webs, Scale Insects, Mealybugs, Flea Beetles, and Brown Clumps in 5 pest classes. Each image has been carefully captured indicate specific regions as either healthy or diseased, given that each image includes a complex natural background. For structured model development, the dataset is categorized into 14 folders combining pests and diseases. This dataset is particularly valuable for training and validating deep learning and machine learning algorithms aimed at identifying and detecting diseases and pests in agarwood leaves. It offers researchers and learners a robust resource for analyzing and improving the health management of agarwood plants through the development of advanced computational models, accessible at Zenodo, in case you are interested in pests, considering using Mendeley.