Review Articles Open access

In-field hyperspectral imaging: An overview on the ground-based applications in agriculture

Authors

Published
29 September 2020
Section
Review Articles
DOI
10.4081/jae.2020.1030
Pages
129-139

Abstract

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.

Keywords

References

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. FAO, 2017. Conservation Agriculture - Revised version. AG Dept factsheets.
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. Jensen, J.R., 2014. Remote sensing of the environment: an earth resource perspective, 2nd ed, Pearson Education Limited.
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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

Metrics

Citations and online attention from Dimensions, Altmetric and PlumX.

PlumX Metrics

License

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.