High-Resolution Images of Lychee and Jackfruit Plant Diseases for Classification and Detection
Description
This dataset consists of high-resolution images of lychee and jackfruit plants (Litchi chinensis and Artocarpus heterophyllus) collected from Biral, Dinajpur, Bangladesh, a region renowned for its lychee and jackfruit production. The dataset was captured between 7th and 12th November 2025 and includes a total of 8,000 images. The collection comprises 3,766 images of lychee leaves, which after augmentation, total 6,000 images, and 1,144 images of jackfruit leaves. The dataset is categorized into eight distinct disease types and plant conditions: Anthracnose Cloudy: 1,000 images Algal Spot Indirect: 1,000 images Dry Leaves: 1,000 images Entomosporium Spot: 1,000 images Leaf Mites Direct: 1,000 images Mayetiola PostRain: 1,000 images Mature Jackfruit Leaf: 1,000 images Young Jackfruit Leaf: 1,000 images The images were captured from various angles and under different lighting conditions using smartphones (Poco F5 and Google Pixel 7), ensuring a variety of perspectives and environmental conditions. The dataset is formatted in JPG with original image resolutions of 3024 × 4032 pixels, resized to 560 × 420 pixels for efficient processing. The dataset's primary goal is to support the development of machine learning and deep learning models for the classification, detection, and monitoring of disease stages in both lychee and jackfruit plants. By focusing on a variety of disease conditions and plant growth stages under natural conditions, this dataset is a valuable resource for advancing agricultural disease management, precision farming, and plant pathology. The inclusion of both lychee and jackfruit plant images makes it a versatile tool for improving crop health monitoring.