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Agricultural plant hyperspectral imaging dataset
A.V. Gaidel 1,2,3, V.V. Podlipnov 1,2,3, N.A. Ivliev 1,2,3, R.A. Paringer 1,2,3, P.A. Ishkin 4, S.V. Mashkov 4, R.V. Skidanov 1,2

1 IPSI RAS – Branch of the FRSC "Crystallography and Photonics" RAS,
443001, Samara, Russia, Molodogvardeiskaya St. 151;
2 Samara National Research University,
443086, Samara, Russia, Moskovskoye shosse 34;
3 Federal Research Center «Computer Science and Control» or Russian Academy of Sciences,
119333, Moscow, Russia, Vavilova 44;
4 Samara State Agrarian University, 446442, Usty-Kinelyskiy, Russia, Uchebnaya 2

 PDF, 3668 kB

DOI: 10.18287/2412-6179-CO-1226

Страницы: 442-450.

Язык статьи: English.

Аннотация:
Detailed automated analysis of crop images is critical to the development of smart agriculture and can significantly improve the quantity and quality of agricultural products. A hyperspectral camera potentially allows to extract more information about the observed object than a conventional one, so its use can help in solving problems that are difficult to solve with conventional methods. Often, predictive models that solve such problems require a large dataset for training. However, sufficiently large datasets of hyperspectral images of agricultural plants are not currently publicly available. Therefore, we present a new dataset of hyperspectral images of plants in this paper. This dataset can be accessed via URL https://pypi.org/project/HSI-Dataset-API/. It contains 385 hyperspectral images with a spatial resolution of 512 by 512 pixels and spectral resolution of 237 spectral bands. The images were captured in the summer of 2021 in Samara and Novocherkassk (Russia) using Offner based Imaging Hyperspectrometer of our own production. The article demonstrates the work of some basic approaches to the analysis of hyperspectral images using the dataset and states problems for further solving.

Ключевые слова:
hyperspectral imaging, image dataset, image processing, image segmentation, smart agriculture.

Благодарности
This work was supported by the Ministry of Science and Higher Education of the Russian Federation under Grant 00600/2020/51896 agreement number 075-15-2022-319.

Цитирование:
Gaidel, A.V. Agricultural plant hyperspectral imaging dataset / A.V. Gaidel, V.V. Podlipnov, N.A. Ivliev, R.A. Paringer, P.A. Ishkin, S.V. Mashkov, R.V. Skidanov // Computer Optics. - 2023. - Vol. 47(3). - P. 442-450. - DOI: 10.18287/2412-6179-CO-1226.

Citation:
Gaidel AV, Podlipnov VV, Ivliev NA, Paringer RA, Ishkin PA, Mashkov SV, Skidanov RV. Agricultural plant hyperspectral imaging dataset. Computer Optics 2023; 47(3): 442-450. DOI: 10.18287/2412-6179-CO-1226.

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