Data in Brief (Aug 2024)
Ultra-high-resolution hyperspectral imagery datasets for precision agriculture applications
Abstract
Technology infusion in agriculture has been progressing steadily, touching upon various spheres of agriculture such as crop identification, soil classification, yield prediction, disease detection, and weed-crop discrimination. On-demand crop type detection, often realized as crop mapping, is a primary requirement in agriculture. Alongside the topographic LiDAR and thermal imaging, hyperspectral remote sensing is a versatile technique for mapping and predicting various parameters of interest in agriculture. The ongoing developments in the methods and algorithms of remote sensing data analyses for crop mapping require the availability of curated, high-resolution hyperspectral datasets, varied by crop type, nutrient supply (nitrogen level), and ground truth data. Aimed at enabling the development and validation of approaches for crop mapping at the plant level, we present a high-resolution ground-based hyperspectral imaging dataset acquired over fields of two vegetable crops (cabbage, eggplant). These crops were grown on experimental plots of the University of Agricultural Sciences, Bengaluru, India, maintaining three different nitrogen levels (high, medium, and low). The datasets contain hyperspectral imagery of the vegetable crops grown under two configurations: (i) imagery, which contains only a single crop type in a scene, and (ii) imagery, which contains both crops in a single scene. In both configurations, each crop has plots representing three different nitrogen levels. Ultra-high spatial resolution hyperspectral imaging data were acquired in 400 to 900 nm with an effective spectral resolution of 3 nm and spatial resolution of 3 mm using a ground-based push-broom hyperspectral imaging system (Headwall Photonics, USA). Ground truth data were also presented. The datasets are valuable for developing and validating various methods and algorithms for precision agriculture applications, such as machine learning methods for crop mapping at plants and estimating crop growth responses to different nitrogen levels.
