MH-SoyaHealthVision: An Indian UAV and Leaf Image Dataset for Integrated Crop Health Assessment

MH-SoyaHealthVision is a comprehensive dataset developed for integrated crop health assessment in soybean farming. It combines ground-level leaf images and UAV-captured images from soyabean fields of Maharashtra region, enabling a holistic approach towards disease and pest attack detection. The leaf image dataset includes high-resolution visuals of soybean leaves affected by diseases such as rust, mosaic virus, septoria brown spot, and frog-eye leaf spot, along with pest damage caused by caterpillars and semiloopers. Complementing this, the UAV dataset provides large-scale aerial perspectives of soybean fields, capturing patterns of rust, mosaic virus, and pest attack infestations.
The inclusion of UAV technology in this dataset is crucial for precision agriculture, as drones facilitate highly accurate, targeted spraying of pesticides. The combined approaches of ground-level and aerial imagery make MH-SoyaHealthVision a valuable resource for developing machine learning and deep learning models for disease detection and classification. This dataset aims to contribute towards improved crop health monitoring, enabling early intervention strategies and enhancing productivity in soybean farming.
The dataset comprises a total of 5,680 images, divided into two parts:
First part includes Soybean Leaf Image Dataset, categorized into six folders out of which one is"Healthy," next four representing different types of diseases, and last is of pest attack.
Second part includes Soybean UAV Image Dataset, categorized into four folders out of which one is "Healthy," two folders representing diseases, and remaining one for pest attack.