Estimation of Water Stress in Potato Plants Using Hyperspectral Imagery and Machine Learning Algorithms
This work presents quantitative detection of water stress and estimation of the water stress level: none, light, moderate, and severe on potato crops. We use hyperspectral imagery and state of the art machine learning algorithms: random decision forest, multilayer perceptron, convolutional neural...
| Autores principales: | , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Español |
| Publicado: |
MDPI
2025
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12324/41212 https://doi.org/10.3390/horticulturae7070176 |
| _version_ | 1855494801068130304 |
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| author | Duarte Carvajalino, Julio Martin Silva Arero, Elías Alexander Góez Vinasco, Gerardo Antonio Torres Delgado, Laura Marcela Ocampo Paez, Oscar Dubán Castaño Marín, Angela María |
| author_browse | Castaño Marín, Angela María Duarte Carvajalino, Julio Martin Góez Vinasco, Gerardo Antonio Ocampo Paez, Oscar Dubán Silva Arero, Elías Alexander Torres Delgado, Laura Marcela |
| author_facet | Duarte Carvajalino, Julio Martin Silva Arero, Elías Alexander Góez Vinasco, Gerardo Antonio Torres Delgado, Laura Marcela Ocampo Paez, Oscar Dubán Castaño Marín, Angela María |
| author_sort | Duarte Carvajalino, Julio Martin |
| collection | Repositorio AGROSAVIA |
| description | This work presents quantitative detection of water stress and estimation of the water stress
level: none, light, moderate, and severe on potato crops. We use hyperspectral imagery and state of
the art machine learning algorithms: random decision forest, multilayer perceptron, convolutional
neural networks, support vector machines, extreme gradient boost, and AdaBoost. The detection
and estimation of water stress in potato crops is carried out on two different phenological stages
of the plants: tubers differentiation and maximum tuberization. The machine learning algorithms
are trained with a small subset of each hyperspectral image corresponding to the plant canopy. The
results are improved using majority voting to classify all the canopy pixels in the hyperspectral
images. The results indicate that both detection of water stress and estimation of the level of water
stress can be obtained with good accuracy, improved further by majority voting. The importance of
each band of the hyperspectral images in the classification of the images is assessed by random forest
and extreme gradient boost, which are the machine learning algorithms that perform best overall on
both phenological stages and detection and estimation of water stress in potato crops. |
| format | Artículo |
| id | RepoAGROSAVIA41212 |
| institution | Corporación Colombiana de Investigación Agropecuaria |
| language | Español |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | RepoAGROSAVIA412122025-09-11T03:00:25Z Estimation of Water Stress in Potato Plants Using Hyperspectral Imagery and Machine Learning Algorithms Duarte Carvajalino, Julio Martin Silva Arero, Elías Alexander Góez Vinasco, Gerardo Antonio Torres Delgado, Laura Marcela Ocampo Paez, Oscar Dubán Castaño Marín, Angela María Producción y tratamiento de semillas - F03 Solanum tuberosum Estrés de sequia Marchitamiento Raíces y tubérculos http://aims.fao.org/aos/agrovoc/c_7221 http://aims.fao.org/aos/agrovoc/c_24993 http://aims.fao.org/aos/agrovoc/c_8390 This work presents quantitative detection of water stress and estimation of the water stress level: none, light, moderate, and severe on potato crops. We use hyperspectral imagery and state of the art machine learning algorithms: random decision forest, multilayer perceptron, convolutional neural networks, support vector machines, extreme gradient boost, and AdaBoost. The detection and estimation of water stress in potato crops is carried out on two different phenological stages of the plants: tubers differentiation and maximum tuberization. The machine learning algorithms are trained with a small subset of each hyperspectral image corresponding to the plant canopy. The results are improved using majority voting to classify all the canopy pixels in the hyperspectral images. The results indicate that both detection of water stress and estimation of the level of water stress can be obtained with good accuracy, improved further by majority voting. The importance of each band of the hyperspectral images in the classification of the images is assessed by random forest and extreme gradient boost, which are the machine learning algorithms that perform best overall on both phenological stages and detection and estimation of water stress in potato crops. Papa-Solanum tuberosum 2025-09-10T19:27:00Z 2025-09-10T19:27:00Z 2021 2021 article Artículo científico http://purl.org/coar/resource_type/c_2df8fbb1 info:eu-repo/semantics/article https://purl.org/redcol/resource_type/ART http://purl.org/coar/version/c_970fb48d4fbd8a85 http://hdl.handle.net/20.500.12324/41212 https://doi.org/10.3390/horticulturae7070176 reponame:Biblioteca Digital Agropecuaria de Colombia instname:Corporación colombiana de investigación agropecuaria AGROSAVIA spa Plan View 8724 190388 Horticulturae 7 176 1 17 Center, I.P. Potato Facts and Figures. Available online: https://cipotato.org/potato/potato-facts-and-figures/ (accessed on 25 June 2021). Trenberth, K.E.; Dai, A.; van der Schrier, G.; Jones, P.D.; Barichivich, J.; Briffa, K.R.; Sheffield, J. Global warming and changes in drought. Nat. Clim. Chang. 2014, 4, 17–22. Caturegli, L.; Matteoli, S.; Gaetani, M.; Grossi, N.; Magni, S.; Minelli, A.; Corsini, G.; Remorini, D.; Volterrani, M. Effects of water stress on spectral reflectance of bermudagrass. Sci. Rep. 2020, 10, 1–12. Gago, J.; Douthe, C.; Coopman, R.E.; Gallego, P.P.; Ribas-Carbo, M.; Flexas, J.; Escalona, J.; Medrano, H. UAVs challenge to assess water stress for sustainable agriculture. Agric. Water Manag. 2015, 153, 9–19. Gerhards, M.; Rock, G.; Schlerf, M.; Udelhoven, T. Water stress detection in potato plants using leaf temperature, emissivity, and reflectance. Int. J. Appl. Earth Obs. Geoinf. 2016, 53, 27–39. Amatya, S.; Karkee, M.; Alva, A.K.; Larbi, P.; Adhikari, B. Hyperspectral imaging for detecting water stress in potatoes. In Proceedings of the American Society of Agricultural and Biological Engineers Annual International Meeting 2012, ASABE 2012, Dallas, TX, USA, 29 July–1 August 2012; Volume 7, pp. 6134–6148 Loggenberg, K.; Strever, A.; Greyling, B.; Poona, N. Modelling water stress in a Shiraz vineyard using hyperspectral imaging and machine learning. Remote Sens. 2018, 10, 202. Van Emmerik, T.; Steele-Dunne, S.C.; Judge, J.; Van De Giesen, N. Dielectric Response of Corn Leaves to Water Stress. IEEE Geosci. Remote Sens. Lett. 2017, 14, 8–12. Gómez, M.I.; Magnitskiy, S.; Rodríguez, L.E. Normalized difference vegetation index, and K+ in stem sap of potato plants (Group Andigenum) as affected by fertilization. Exp. Agric. 2019, 55, 945–955. Tschaplinski, T.J.; Abraham, P.E.; Jawdy, S.S.; Gunter, L.E.; Martin, M.Z.; Engle, N.L.; Yang, X.; Tuskan, G.A. The nature of the progression of drought stress drives differential metabolomic responses in Populus deltoides. Ann Anaconda. Anaconda Individual Edition. Available online: https://www.anaconda.com/products/individual (accessed on 25 June 2021). Ho, T.K. Random decision forests. In Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada, 14–16 August 1995; Volume 1, pp. 278–282. Ioffe, S.; Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), Lile, France, 6–11 July 2015; Volume 1, pp Nwankpa, C.E.; Ijomah,W.; Gachagan, A.; Marshall, S. Activation functions: Comparison of trends in practice and research for deep learning. arXiv 2018, arXiv:1811.03378. Cheng, T.; Guestrin, C. XGBoost: A Scalable Tree Boosting System. In Proceedings of the The Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016; pp. 785–794. Gao, Y.; Qiu, J.; Miao, Y.; Qiu, R.; Li, H.; Zhang, M. Prediction of Leaf Water Content in Maize Seedlings Based on Hyperspectral Information. IFAC-PapersOnLine 2019, 52, 263–269. Krishna, G.; Sahoo, R.N.; Singh, P.; Bajpai, V.; Patra, H.; Kumar, S.; Dandapani, R.; Gupta, V.K.; Viswanathan, C.; Ahmad, T.; et al. Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing. Agric. Water Manag. 2019, 213, 231–244. Sistema de información agroclimática para papa. SIAP Atribución-NoComercial-CompartirIgual 4.0 Internacional http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf application/pdf MDPI Bogotá (Colombia) Horticulturae; Vol. 7, Num 176 (2021): Revista Horticulturae; p. 1-17. |
| spellingShingle | Producción y tratamiento de semillas - F03 Solanum tuberosum Estrés de sequia Marchitamiento Raíces y tubérculos http://aims.fao.org/aos/agrovoc/c_7221 http://aims.fao.org/aos/agrovoc/c_24993 http://aims.fao.org/aos/agrovoc/c_8390 Duarte Carvajalino, Julio Martin Silva Arero, Elías Alexander Góez Vinasco, Gerardo Antonio Torres Delgado, Laura Marcela Ocampo Paez, Oscar Dubán Castaño Marín, Angela María Estimation of Water Stress in Potato Plants Using Hyperspectral Imagery and Machine Learning Algorithms |
| title | Estimation of Water Stress in Potato Plants Using Hyperspectral Imagery and Machine Learning Algorithms |
| title_full | Estimation of Water Stress in Potato Plants Using Hyperspectral Imagery and Machine Learning Algorithms |
| title_fullStr | Estimation of Water Stress in Potato Plants Using Hyperspectral Imagery and Machine Learning Algorithms |
| title_full_unstemmed | Estimation of Water Stress in Potato Plants Using Hyperspectral Imagery and Machine Learning Algorithms |
| title_short | Estimation of Water Stress in Potato Plants Using Hyperspectral Imagery and Machine Learning Algorithms |
| title_sort | estimation of water stress in potato plants using hyperspectral imagery and machine learning algorithms |
| topic | Producción y tratamiento de semillas - F03 Solanum tuberosum Estrés de sequia Marchitamiento Raíces y tubérculos http://aims.fao.org/aos/agrovoc/c_7221 http://aims.fao.org/aos/agrovoc/c_24993 http://aims.fao.org/aos/agrovoc/c_8390 |
| url | http://hdl.handle.net/20.500.12324/41212 https://doi.org/10.3390/horticulturae7070176 |
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