Agro-HSR: The first large-scale agricultural-focused hyperspectral dataset for deep learning-based image reconstruction and quality prediction
Ocean Monjur, Md. Toukir Ahmed, Girish Chowdhary, Mohammed Kamruzzaman
Abstract
• Argo-HSR: First public agriculture-focused and largest HSI reconstruction benchmark. • Dataset includes 1322 paired HSI-RGB images with DMC, Brix, and Firmness attributes. • Restormer achieved the best reconstruction performance (PSNR: 36.68, RMSE: 0.0149) • Reconstructed spectra enabled competitive prediction of agricultural quality attributes. • Promotes accessible HSI research by reducing cost and complexity for agriculture. Hyperspectral imaging (HSI) has recently emerged as a valuable tool for various agricultural applications. However, the widespread adoption of hyperspectral imaging is hindered due to the high cost and complexity of collecting and processing hyperspectral images. To address this gap, we introduce Agro-HSR, 1 1 Link to dataset: Agro-HSR. a large-scale RGB to hyperspectral image reconstruction dataset of sweet potatoes, specifically curated to promote easy access to hyperspectral images for the agricultural community. Agro-HSR comprises 1322 pairs of RGB and hyperspectral image cubes from 790 samples across three sweet potato varieties. For 141 of these samples, the agro-product quality attributes are included in the dataset. Each hyperspectral image cube covers 31 evenly spaced bands within the wavelength range of 400–1000 nm. Benchmarks for hyperspectral image reconstruction were conducted to demonstrate the importance and applicability of Agro-HSR. These benchmarks evaluated the ability to predict critical quality parameters in sweet potatoes, including Brix, dry matter, and firmness, from reconstructed hyperspectral images. Agro-HSR enhances the accessibility of hyperspectral images and promotes opportunities for cross-domain research in deep learning and agricultural science, addressing critical challenges in assessing the quality of agro-products.