2025-11-19 イリノイ大学
＜関連情報＞
- https://aces.illinois.edu/news/seeing-beyond-visible-researchers-turn-everyday-cameras-crop-analysis-tools
- https://www.sciencedirect.com/science/article/pii/S0168169925012098
- https://www.sciencedirect.com/science/article/pii/S0168169925011688
Agro-HSR: ディープラーニングベースの画像再構成と品質予測のための、農業に特化した初の大規模ハイパースペクトルデータセット 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
Computers and Electronics in Agriculture Available online: 14 October 2025
DOI:https://doi.org/10.1016/j.compag.2025.111103
Highlights
- 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.
Abstract
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 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.
ウィンドウ適応型空間スペクトル注意変換器に基づくトウモロコシ生育状態監視のためのRGB画像からのマルチスペクトル画像再構成 Multispectral image reconstruction from RGB image for maize growth status monitoring based on window-adaptive spatial-spectral attention transformer
Di Song, Hong Sun, Esther Ngumbi, Mohammed Kamruzzaman
Computers and Electronics in Agriculture Available online 4 October 2025
DOI:https://doi.org/10.1016/j.compag.2025.111103
Highlights
- 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.
Abstract
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 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.
