https://doi.org/10.4081/jae.2020.1030
In-field hyperspectral imaging: An overview on the ground-based applications in agriculture
Published: 29 September 2020
Abstract Views: 3351
PDF: 1989
HTML: 1352
HTML: 1352
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
The measurement of vegetation indexes that characterise the plants growth, assessing the fruit ripeness or detecting the presence of defects and diseases, is a key factor to gain high quality of fruit or vegetables. Such evaluation can be carried out using both destructive and non destructive techniques. Among non-destructive techniques, hyperspectral imaging (HSI), combining image analysis and visible/near-infrared spectroscopy, looks particularly useful. Many studies have been published concerning the use of hyperspectral cameras in the agronomic and food field, especially in controlled laboratory conditions. Conversely, few studies described the application of HSI technology directly in field, especially involving ground-based systems. Results suggest that this technique could be particularly useful, even if the role of environmental variables has to be considered (e.g., intensity and incidence of solar radiation, wind or the soil in the background). In this paper, recent in-field HSI applications based on ground systems are reviewed.
Al Makdessi, N., Jean, P.A., Ecarnot, M., Gorretta, N., Rabatel, G., Roumet, P., 2017. How plant structure impacts the biochemical leaf traits assessment from in-field hyperspectral images: A simulation study based on light propagation modeling in 3D virtual wheat scenes. F. Crop. Res. 205, 95–105. https://doi.org/10.1016/j.fcr.2017.02.001
DOI: https://doi.org/10.1016/j.fcr.2017.02.001
Chen, W., Li, H., Niu, Q., Hongnan, H., 2017. Hyperspectral image segmentation for maize stubble in no-till field, in: 2017 ASABE Annual International Meeting. American Society of Agricultural and Biological Engineers, p. 6. https://doi.org/10.13031/aim.201700963
DOI: https://doi.org/10.13031/aim.201700963
Deery, D., Jimenez-Berni, J., Jones, H., Sirault, X., Furbank, R., 2014. Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping. Agronomy 4, 349–379. https://doi.org/10.3390/agronomy4030349
DOI: https://doi.org/10.3390/agronomy4030349
ElMasry, G., Kamruzzaman, M., Sun, D.W., Allen, P., 2012. Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review. Crit. Rev. Food Sci. Nutr. 52, 999–1023. https://doi.org/10.1080/10408398.2010.543495
DOI: https://doi.org/10.1080/10408398.2010.543495
ElMasry, G., Sun, D.W., 2010. Principles of Hyperspectral Imaging Technology, in: Hyperspectral Imaging for Food Quality Analysis and Control. https://doi.org/10.1016/B978-0-12-374753-2.10001-2
DOI: https://doi.org/10.1016/B978-0-12-374753-2.10001-2
FAO, 2017. Conservation Agriculture - Revised version. AG Dept factsheets.
Garini, Y., Young, I.T., McNamara, G., 2006. Spectral imaging: Principles and applications. Cytom. Part A 69A, 735–747. https://doi.org/10.1002/cyto.a.20311
DOI: https://doi.org/10.1002/cyto.a.20311
Goetz, A., Vane, G., Solomon, J., Rock, B.., 1985. Imaging Spectrometry for Earth Remote Sensing. Science (80-. ). 228, 1147–1153. https://doi.org/10.1126/science.228.4704.1147
DOI: https://doi.org/10.1126/science.228.4704.1147
Gutiérrez, S., Fernández-Novales, J., Diago, M.P., Tardaguila, J., 2018. On-The-Go Hyperspectral Imaging Under Field Conditions and Machine Learning for the Classification of Grapevine Varieties. Front. Plant Sci. 9, 1–11. https://doi.org/10.3389/fpls.2018.01102
DOI: https://doi.org/10.3389/fpls.2018.01102
Herrmann, I., Shapira, U., Kinast, S., Karnieli, A., Bonfil, D.J., 2013. Ground-level hyperspectral imagery for detecting weeds in wheat fields. Precis. Agric. 14, 637–659. https://doi.org/10.1007/s11119-013-9321-x
DOI: https://doi.org/10.1007/s11119-013-9321-x
Huang, Y., Lee, M.A., Thomson, S.J., Reddy, K.N., 2016. Ground-based hyperspectral remote sensing for weed management in crop production. Int. J. Agric. Biol. Eng. 9, 98–109. https://doi.org/10.3965/j.ijabe.20160902.2137
Jay, S., Gorretta, N., Morel, J., Maupas, F., Bendoula, R., Rabatel, G., Dutartre, D., Comar, A., Baret, F., 2017. Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery. Remote Sens. Environ. 198, 173–186. https://doi.org/10.1016/j.rse.2017.06.008
DOI: https://doi.org/10.1016/j.rse.2017.06.008
Jensen, J.R., 2014. Remote sensing of the environment: an earth resource perspective, 2nd ed, Pearson Education Limited.
Jiang, Y., Li, C., Robertson, J.S., Sun, S., Xu, R., Paterson, A.H., 2018. GPhenoVision: A ground mobile system with multi-modal imaging for field-based high throughput phenotyping of cotton, Scientific Reports. Springer US. https://doi.org/10.1038/s41598-018-19142-2
DOI: https://doi.org/10.1038/s41598-018-19142-2
Liu, D., Sun, D.-W., Zeng, X.-A., 2014. Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry. Food Bioprocess Technol. 7, 307–323. https://doi.org/10.1007/s11947-013-1193-6
DOI: https://doi.org/10.1007/s11947-013-1193-6
Liu, D., Zeng, X.A., Sun, D.W., 2015. Recent Developments and Applications of Hyperspectral Imaging for Quality Evaluation of Agricultural Products: A Review. Crit. Rev. Food Sci. Nutr. 55, 1744–1757. https://doi.org/10.1080/10408398.2013.777020
DOI: https://doi.org/10.1080/10408398.2013.777020
Lu, R., Park, B., 2015. Introduction, in: Hyperspectral Imaging Technology in Food and Agriculture. Springer, New York, NY, pp. 3–7. https://doi.org/10.1007/978-1-4939-2836-1_1
DOI: https://doi.org/10.1007/978-1-4939-2836-1_1
Ma, J., Sun, D.-W., Pu, H., Cheng, J.-H., Wei, Q., 2019. Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications. Annu. Rev. Food Sci. Technol. 10, 197–220. https://doi.org/10.1146/annurev-food-032818-121155
DOI: https://doi.org/10.1146/annurev-food-032818-121155
Malenovský, Z., Turnbull, J.D., Lucieer, A., Robinson, S.A., 2015. Antarctic moss stress assessment based on chlorophyll content and leaf density retrieved from imaging spectroscopy data. New Phytol. 208, 608–624. https://doi.org/10.1111/nph.13524
DOI: https://doi.org/10.1111/nph.13524
Mishra, P., Asaari, M.S.M., Herrero-Langreo, A., Lohumi, S., Diezma, B., Scheunders, P., 2017. Close range hyperspectral imaging of plants: A review. Biosyst. Eng. https://doi.org/10.1016/j.biosystemseng.2017.09.009
DOI: https://doi.org/10.1016/j.biosystemseng.2017.09.009
Onoyama, H., Ryu, C., Suguri, M., Iida, M., 2018. Estimation of rice protein content before harvest using ground-based hyperspectral imaging and region of interest analysis. Precis. Agric. 19, 721–734. https://doi.org/10.1007/s11119-017-9552-3
DOI: https://doi.org/10.1007/s11119-017-9552-3
Onoyama, H., Ryu, C., Suguri, M., Iida, M., 2015. Nitrogen prediction model of rice plant at panicle initiation stage using ground-based hyperspectral imaging: growing degree-days integrated model. Precis. Agric. 16, 558–570. https://doi.org/10.1007/s11119-015-9394-9
DOI: https://doi.org/10.1007/s11119-015-9394-9
Pantazi, X.E., Moshou, D., Bravo, C., 2016. Active learning system for weed species recognition based on hyperspectral sensing. Biosyst. Eng. 146, 193–202. https://doi.org/10.1016/j.biosystemseng.2016.01.014
DOI: https://doi.org/10.1016/j.biosystemseng.2016.01.014
Reddy, K.N., Huang, Y., Lee, M.A., Nandula, V.K., Fletcher, R.S., Thomson, S.J., Zhao, F., 2014. Glyphosate-resistant and glyphosate-susceptible Palmer amaranth (Amaranthus palmeri S. Wats.): Hyperspectral reflectance properties of plants and potential for classification. Pest Manag. Sci. 70, 1910–1917. https://doi.org/10.1002/ps.3755
DOI: https://doi.org/10.1002/ps.3755
Rinnan, Å., Berg, F. van den, Engelsen, S.B., 2009. Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends Anal. Chem. 28, 1201–1222. https://doi.org/10.1016/J.TRAC.2009.07.007
DOI: https://doi.org/10.1016/j.trac.2009.07.007
Rodriguez-Moreno, F., Zemek, F., Kren, J., Pikl, M., Lukas, V., Novak, J., 2016. Spectral monitoring of wheat canopy under uncontrolled conditions for decision making purposes. Comput. Electron. Agric. 125, 81–88. https://doi.org/10.1016/j.compag.2016.05.002
DOI: https://doi.org/10.1016/j.compag.2016.05.002
Römer, C., Wahabzada, M., Ballvora, A., Pinto, F., Rossini, M., Panigada, C., Behmann, J., Léon, J., Thurau, C., Bauckhage, C., Kersting, K., Rascher, U., Plümer, L., 2012. Early drought stress detection in cereals : simplex volume maximisation for hyperspectral image analysis. Funct. Plant Biol. 39, 878–890. https://doi.org/10.1071/FP12060
DOI: https://doi.org/10.1071/FP12060
Thomas, S., Kuska, M.T., Bohnenkamp, D., Brugger, A., Alisaac, E., Wahabzada, M., Behmann, J., Mahlein, A.K., 2018. Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective. J. Plant Dis. Prot. 125, 5–20. https://doi.org/10.1007/s41348-017-0124-6
DOI: https://doi.org/10.1007/s41348-017-0124-6
Underwood, J., Wendel, A., Schofield, B., McMurray, L., Kimber, R., 2017. Efficient in-field plant phenomics for row-crops with an autonomous ground vehicle. J. F. Robot. 34, 1061–1083. https://doi.org/10.1002/rob.21728
DOI: https://doi.org/10.1002/rob.21728
Vigneau, N., Ecarnot, M., Rabatel, G., Roumet, P., 2011. Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in Wheat. F. Crop. Res. 122, 25–31. https://doi.org/10.1016/j.fcr.2011.02.003
DOI: https://doi.org/10.1016/j.fcr.2011.02.003
Wang, B., Zhang, X., Dong, Y., Zhang, Jingwen, Zhang, Jingcheng, Zhou, X., 2018. Retrieval of Leaf Chlorophyll Content of Paddy Rice with Extracted Foliar Hyperspectral Imagery, in: 2018 7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE, pp. 1–5. https://doi.org/10.1109/Agro-Geoinformatics.2018.8476024
DOI: https://doi.org/10.1109/Agro-Geoinformatics.2018.8476024
Wendel, A., Underwood, J., Walsh, K., 2018. Maturity estimation of mangoes using hyperspectral imaging from a ground based mobile platform. Comput. Electron. Agric. 155, 298–313. https://doi.org/10.1016/j.compag.2018.10.021
DOI: https://doi.org/10.1016/j.compag.2018.10.021
Whetton, R.L., Waine, T.W., Mouazen, A.M., 2018. Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 2: On-line field measurement. Biosyst. Eng. 167, 144–158. https://doi.org/10.1016/j.biosystemseng.2018.01.004
DOI: https://doi.org/10.1016/j.biosystemseng.2018.01.004
Wu, Q., Wang, C., Fang, J.J., Ji, J.W., 2016. Field monitoring of wheat seedling stage with hyperspectral imaging. Int. J. Agric. Biol. Eng. 9, 143–148. https://doi.org/10.3965/j.ijabe.20160905.1707
Yoon, S.-C., Park, B., 2015. Hyperspectral Image Processing Methods. Springer, New York, NY, pp. 81–101. https://doi.org/10.1007/978-1-4939-2836-1_4
DOI: https://doi.org/10.1007/978-1-4939-2836-1_4
Zhao, J., Zhang, D., Huang, L., Zhang, Q., Liu, W., Yang, H., 2016. Vertical features of yellow rust infestation on winter wheat using hyperspectral imaging measurements. 2016 5th Int. Conf. Agro-Geoinformatics, Agro-Geoinformatics 2016 1–4. https://doi.org/10.1109/Agro-Geoinformatics.2016.7577620
DOI: https://doi.org/10.1109/Agro-Geoinformatics.2016.7577620
How to Cite
Benelli, A., Cevoli, C. and Fabbri, A. (2020) “In-field hyperspectral imaging: An overview on the ground-based applications in agriculture”, Journal of Agricultural Engineering, 51(3), pp. 129–139. doi: 10.4081/jae.2020.1030.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Adeshina Fadeyibi, Kehinde Raheef Adebayo, Taiye Mayowa Obafemi, Abiodun Samson Olubo, Rasheed Amao Busari, Mohammed Gana Yisa , Development and evaluation of biomass-based alternative charcoal , Journal of Agricultural Engineering: Vol. 51 No. 3 (2020)
- Adeel Rafiq, Wook Ho Na, Adnan Rasheed, Jong Won Lee, Hyeon Tae Kim, Hyun Woo Lee, Measurement of longwave radiative properties of energy-saving greenhouse screens , Journal of Agricultural Engineering: Vol. 52 No. 3 (2021)
- Jingtao Li, Hao Chen, Guisong Li, Yueqi Liu, Yanli Yang, Xia Liu, Chang Yi Wang, Detection method of potato leaf disease based on YOLOv5s , Journal of Agricultural Engineering: Vol. 55 No. 3 (2024)
You may also start an advanced similarity search for this article.
