Agricultural plant hyperspectral imaging dataset
Автор: Gaidel Andrey Viktorovich, Podlipnov Vladimir Vladimirovich, Ivliev Nikolay Aleksandrovich, Paringer Rustam Alexandrovich, Ishkin Pavel Aleksandrovich, Mashkov Sergey Vladimirovich, Skidanov Roman Vasilyevich
Журнал: Компьютерная оптика @computer-optics
Рубрика: Обработка изображений, распознавание образов
Статья в выпуске: 3 т.47, 2023 года.
Бесплатный доступ
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.
Hyperspectral imaging, image dataset, image processing, image segmentation, smart agriculture
Короткий адрес: https://sciup.org/140300064
IDR: 140300064 | DOI: 10.18287/2412-6179-CO-1226
Список литературы Agricultural plant hyperspectral imaging dataset
- Yang X, Shu L, Chen J, Ferrag MA, Wu J, Nurellari E, Huang K. A survey on smart agriculture: Development modes, technologies, and security and privacy challenges. IEEE/CAA J Autom Sin 2021; 8(2): 273-302. DOI: 10.1109/JAS.2020.1003536.
- Rose DC, Chilvers J. Agriculture 4.0: Broadening responsible innovation in an era of smart farming. Front Sustain Food Syst 2018; 2: 87. DOI: 10.3389/fsufs.2018.00087.
- Bertoglio R, Corbo C, Renga FM, Matteucci M. The digital agricultural revolution: A bibliometric analysis literature review. IEEE Access 2021; 9: 134762-134782. DOI: 10.1109/ACCESS.2021.3115258.
- Patrício DI, Rieder R. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Comput Electron Agric 2018; 153: 69-81. DOI: 10.1016/j.compag.2018.08.001.
- Hasan RI, Yusuf SM, Alzubaidi L. Review of the state of the art of deep learning for plant diseases: A broad analysis and discussion. Plants 2020; 9(10): 1302. DOI: 10.3390/plants9101302.
- Manolakis D, Shaw G. Detection algorithms for hyperspectral imaging applications. IEEE Signal Process Mag 2002; 19(1): 29-43. DOI: 10.1109/79.974724.
- Mishra P, Asaari MSM, Herrero-Langreo A, Lohumi S, Diezma B, Scheunders P. Close range hyperspectral imaging of plants: A review. Biosyst Eng 2017; 164: 49-67. DOI: 10.1016/j.biosystemseng.2017.09.009.
- Singh D, Jain N, Jain P, Kayal P, Kumawat S, Batra N. PlantDoc: A dataset for visual plant disease detection. CoDS COMAD 2020: Proc 7th ACM IKDD CoDS and 25th COMAD 2020: 249-253. DOI: 10.1145/3371158.3371196.
- Sun Y, Liu Y, Wang G, Zhang H. Deep Learning for plant identification in natural environment. Comput Intell Neurosci 2017; 2017: 7361042. DOI: 10.1155/2017/7361042.
- Olsen A, Konovalov DA, Philippa B, Ridd P, Wood JC, Johns J, Banks W, Girgenti B, Kenny O, Whinney J, Calvert B, Azghadi MR, White RD. DeepWeeds: A multiclass weed species image dataset for deep learning. Sci Rep 2019; 9: 2058. DOI: 10.1038/s41598-018-38343-3.
- Behmann J, Acebron K, Emin D, Bennertz S, Matsubara S, Thomas S, Bohnenkamp D, Kuska MT, Jussila J, Salo H, Mahlein A-K, Rascher U. Specim IQ: Evaluation of a new, miniaturized handheld hyperspectral camera and its application for plant phenotyping and disease detection. Sensors 2018; 18: 441. DOI: 10.3390/s18020441.
- Imamoglu N, Oish Y, Zhang X, Ding G, Fang Y, Kouyama T, Nakamura R. Hyperspectral image dataset for benchmarking on salient object detection. 2018 Tenth Int Conf on Quality of Multimedia Experience (QoMEX) 2018: 1-3. DOI: 10.1109/QoMEX.2018.8463428.
- Zhang Q, Li Q, Yu G, Sun L, Zhou M, Chu J. A multidimensional choledoch database and benchmarks for cholangiocarcinoma diagnosis. IEEE Access 2019; 7: 149414-149421. DOI: 10.1109/ACCESS.2019.2947470.
- Nugent PW, Shaw JA, Jha P, Scherrer B, Donelick A, Kumar V. Discrimination of herbicide-resistant kochia with hyperspectral imaging. J Appl Remote Sens 2018; 12(1): 016037. DOI: 10.1117/1.JRS.12.016037.
- Kazanskiy NL, Kharitonov SI, Karsakov SI, Khonina SN. Modeling action of a hyperspectrometer based on the Offner scheme within geometric optics. Computer Optics 2014; 38(2): 271-280. DOI: 10.18287/0134-2452-2014-38-2-271-280.
- Kazanskiy NL, Kharitonov SI, Doskolovich LL, Pavelyev AV. Modeling the performance of a spaceborne hyperspectrometer based on the Offner scheme. Computer Optics 2015; 39(1): 70-76. DOI: 10.18287/0134-2452-2015-39-1-70-76.
- Karpeev SV, Khonina SN, Kharitonov SI. Study of the diffraction grating on a convex surface as a dispersive element. Computer Optics 2015; 39(2): 211-217. DOI: 10.18287/0134-2452-2015-39-2-211-217.
- Kazanskiy NL. Modeling diffractive optics elements and devices. Proc SPIE 2018; 10774: 107740O. DOI: 10.1117/12.2319264.
- Kazanskiy NL, Morozov AA, Nikonorov AV, Petrov MV, Podlipnov VV, Skidanov RV, Fursov VA. Experimental study of optical characteristics of a satellite-based Offner hyperspectrometer. Proc SPIE 2018; 10774: 1077411. DOI: 10.1117/12.2318853.
- Rastorguev AA, Kharitonov SI, Kazanskiy NL. Numerical simulation of the performance of a spaceborne Offner imaging hyperspectrometer in the wave optics approximation. Computer Optics 2022; 46(1): 56-64. DOI: 10.18287/2412-6179-CO-1034.
- Podlipnov VV, Skidanov RV. Calibration of an imaging hyperspectrometer. Computer Optics 2017; 41(6): 869-874. DOI: 10.18287/2412-6179-2017-41-6-869-874.
- NikonorovA, Petrov M, Yakimov P, Blank V, Karpeev S, Skidanov R, Kazanskiy N. Evaluating imaging quality of the offner hyperspectrometer. 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS) 2016: 1-6. DOI: 10.1109/PRRS.2016.7867020.
- Karpeev SV, Khonina SN, Murdagulov AR, Petrov MV. Alignment and study of prototypes of the Offner hyperspectrometer. Vestnik of Samara University. Aerospace and Mechanical Engineering 2016; 15(1): 197-206. DOI: 10.18287/2412-7329-2016-15-1-197-206.
- Walt S, Colbert SC, Varoquaux G. The NumPy array: A structure for efficient numerical computation. Comput Sci Eng 2011; 13(2): 22-30. DOI: 10.1109/MCSE.2011.37.
- Paringer RA, Mukhin AV, Kupriyanov AV. Formation of an informative index for recognizing specified objects in hyperspectral data. Computer Optics 2021; 45(6): 873-878. DOI: 10.18287/2412-6179-CO-930.
- Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É. Scikit-learn: Machine learning in Python. J Mach Learn Res 2011; 12(85): 2825-2830.
- Tolles J, Meurer WJ. Logistic regression: Relating patient characteristics to outcomes. JAMA 2016; 316(5): 533-534. DOI: 10.1001/jama.2016.7653.
- Fletcher R. Practical methods of optimization. New York: John Wiley & Sons; 1987. ISBN: 978-0-471-91547-8.
- Baudat G, Anouar F. Generalized discriminant analysis using a kernel approach. Neural Comput 2000; 12(10): 2385-2404. DOI: 10.1162/089976600300014980.
- Breiman L. Random forests. Mach Learn 2001; 45: 5-32. DOI: 10.1023/A:1010933404324.
- Fix E, Hodges JL. Discriminatory analysis, nonparametric discrimination: Consistency properties. Technical Report 4, USAF School of Aviation Medicine, Randolph Field; 1951.
- Firsov NA, Podlipnov VV, Ivliev NA, Nikolaev PP, Mashkov SV, Ishkin PA, Skidanov RV, Nikonorov AV. Neural network-aided classification of hyperspectral vegetation images with a training sample generated using an adaptive vegetation index. Computer Optics 2021; 45(6): 887-896. DOI: 10.18287/2412-6179-CO-1038.
- Kazanskiy N, Ivliev N, Podlipnov V, Skidanov R. An airborne Offner imaging hyperspectrometer with radiallyfastened primary elements. Sensors 2020; 20(12): 3411. DOI: 10.3390/s20123411.
