2026-10-15T21:43:35Z https://data.mendeley.com/oai

oai:data.mendeley.com/8t6k37ztxc.3 2026-10-09T12:30:08Z

Billah, Md Masum Agri-Vision4: A Comprehensive Multi-Crop Leaf Disease Dataset of Tomato, Papaya, Zucchini, and Bottle Gourd from Bangladesh Mendeley Data Agri-Vision4 is an expert-validated multi-crop leaf disease dataset developed for computer vision and precision agriculture research, comprising 5,180 original images and 28,000 augmented images across 28 disease and healthy classes from four major crops: Bottle Gourd, Papaya, Tomato, and Zucchini. The original dataset is divided into 3,615 training images (70%), 762 testing images (15%), and 803 validation images (15%). To address class imbalance, augmentation techniques were applied to expand each of the 28 classes to 1,000 images, resulting in a balanced augmented dataset of 28,000 images. Images were collected under real-world agricultural conditions in Bangladesh using a SONY α7 II camera and standardized to 512×512 pixels. The augmented dataset provides a balanced representation across all disease and healthy classes, supporting robust model training and evaluation. Overall, Agri-Vision4 provides a diverse and balanced resource for plant disease classification, agricultural image analysis, computer vision, and deep learning–based precision agriculture research. Computer Vision Machine Learning Image Classification Deep Learning Agriculture Rahman, Md.Anisur Sagor, Saifuddin Parvin, Sanzida Uddin, Mohammad Shorif Dataset doi:10.17632/8t6k37ztxc.3 oai:data.mendeley.com/8t6k37ztxc.3 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/8t6k37ztxc 2026-10-09T12:30:08Z