Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot

To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-...

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Autores:: Triana-Martinez, Jenniffer Carolina
Álvarez-Meza, Andrés Marino
Gil-González, Julian
De Swaef, Tom
Fernandez-Gallego, Jose A

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2024

Institución:: Universidad de Ibagué

Repositorio:: Repositorio Universidad de Ibagué

Idioma:: eng

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repository_id_str
dc.title.eng.fl_str_mv	Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot
title	Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot
spellingShingle	Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot Cultivos - Estado hídrico Antennas Crops Forestry Linear transformations Metadata Precision agriculture Regression analysis
title_short	Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot
title_full	Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot
title_fullStr	Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot
title_full_unstemmed	Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot
title_sort	Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot
dc.creator.fl_str_mv	Triana-Martinez, Jenniffer Carolina Álvarez-Meza, Andrés Marino Gil-González, Julian De Swaef, Tom Fernandez-Gallego, Jose A
dc.contributor.author.none.fl_str_mv	Triana-Martinez, Jenniffer Carolina Álvarez-Meza, Andrés Marino Gil-González, Julian De Swaef, Tom Fernandez-Gallego, Jose A
dc.subject.armarc.none.fl_str_mv	Cultivos - Estado hídrico
topic	Cultivos - Estado hídrico Antennas Crops Forestry Linear transformations Metadata Precision agriculture Regression analysis
dc.subject.proposal.eng.fl_str_mv	Antennas Crops Forestry Linear transformations Metadata Precision agriculture Regression analysis
description	To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within specific subgroups, frequently emerge in agricultural data. Interpreting these complex patterns requires sophisticated analysis due to the presence of noise, high variability, and non-stationarity behavior in the collected samples. Here, we introduce Local Biplot, a methodological framework tailored for discerning meaningful data patterns in non-stationary contexts for precision agriculture. Local Biplot relies on the well-known uniform manifold approximation and projection method, such as UMAP, and local affine transformations to codify non-stationary and non-linear data patterns while maintaining interpretability. This lets us find important clusters for transformation and projection within a single global axis pair. Hence, our framework encompasses variable and observational contributions within individual clusters. At the same time, we provide a relevance analysis strategy to help explain why those clusters exist, facilitating the understanding of data dynamics while favoring interpretability. We demonstrated our method’s capabilities through experiments on both synthetic and real-world datasets, covering scenarios involving grass and rice crops. Moreover, we use random forest and linear regression models to predict water status variables from our Local Biplot-based feature ranking and clusters. Our findings revealed enhanced clustering and prediction capability while emphasizing the importance of input features in precision agriculture. As a result, Local Biplot is a useful tool to visualize, analyze, and compare the intricate underlying patterns and internal structures of complex agricultural datasets.
publishDate	2024
dc.date.issued.none.fl_str_mv	2024-08
dc.date.accessioned.none.fl_str_mv	2025-10-28T20:33:23Z
dc.date.available.none.fl_str_mv	2025-10-28T20:33:23Z
dc.type.none.fl_str_mv	Artículo de revista
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dc.type.content.none.fl_str_mv	Text
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dc.type.version.none.fl_str_mv	info:eu-repo/semantics/publishedVersion
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dc.identifier.citation.none.fl_str_mv	Triana-Martinez, J.C.; Álvarez-Meza, A.M.; Gil-González, J.; De Swaef, T.; Fernandez-Gallego, J.A. CropWater Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot. Remote Sens. 2024, 16, 2854. https://doi.org/ 10.3390/rs16152854
dc.identifier.doi.none.fl_str_mv	https://doi.org/ 10.3390/rs16152854
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/20.500.12313/5840
dc.identifier.url.none.fl_str_mv	https://www.mdpi.com/2072-4292/16/15/2854
identifier_str_mv	Triana-Martinez, J.C.; Álvarez-Meza, A.M.; Gil-González, J.; De Swaef, T.; Fernandez-Gallego, J.A. CropWater Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot. Remote Sens. 2024, 16, 2854. https://doi.org/ 10.3390/rs16152854
url	https://doi.org/ 10.3390/rs16152854 https://hdl.handle.net/20.500.12313/5840 https://www.mdpi.com/2072-4292/16/15/2854
dc.language.iso.none.fl_str_mv	eng
language	eng
dc.relation.citationissue.none.fl_str_mv	16
dc.relation.citationstartpage.none.fl_str_mv	15
dc.relation.ispartofjournal.none.fl_str_mv	Remote Sensing
dc.relation.references.none.fl_str_mv	Arouna, A.; Dzomeku, I.K.; Shaibu, A.G.; Nurudeen, A.R. Water management for sustainable irrigation in rice (Oryza sativa L.) production: A review. Agronomy 2023, 13, 1522. Oumarou Abdoulaye, A.; Lu, H.; Zhu, Y.; Alhaj Hamoud, Y.; Sheteiwy, M. The global trend of the net irrigation water requirement of maize from 1960 to 2050. Climate 2019, 7, 124 Gui, Y.W.; Sheteiwy, M.S.; Zhu, S.G.; Batool, A.; Xiong, Y.C. Differentiate effects of non-hydraulic and hydraulic root signaling on yield and water use efficiency in diploid and tetraploid wheat under drought stress. Environ. Exp. Bot. 2021, 181, 104287. Al Hamedi, F.; Karthishwaran, K.; Alyafei, M.A.M. Hydroponic wheat production using fresh water and treated wastewater under the semi-arid region. Emir. J. Food Agric 2021, 33, 178 Jiang, H.; Hu, H.; Li, B.; Zhang, Z.; Wang, S.; Lin, T. Understanding the non-stationary relationships between corn yields and meteorology via a spatiotemporally varying coefficient model. Agric. For. Meteorol. 2021, 301, 108340. Archana, S.; Kumar, P.S. A Survey on Deep Learning Based Crop Yield Prediction. Nat. Environ. Pollut. Technol. 2023, 22 Crusiol, L.G.T.; Nanni, M.R.; Furlanetto, R.H.; Sibaldelli, R.N.R.; Sun, L.; Gonçalves, S.L.; Foloni, J.S.S.; Mertz-Henning, L.M.; Nepomuceno, A.L.; Neumaier, N.; et al. Assessing the sensitive spectral bands for soybean water status monitoring and soil moisture prediction using leaf-based hyperspectral reflectance. Agric. Water Manag. 2023, 277, 108089. Efthimiou, N. Object-oriented soil erosion modelling: A non-stationary approach towards a realistic calculation of soil loss at parcel level. Catena 2023, 222, 106816 Ndlovu, H.S.; Odindi, J.; Sibanda, M.; Mutanga, O.; Clulow, A.; Chimonyo, V.G.; Mabhaudhi, T. A comparative estimation of maize leaf water content using machine learning techniques and unmanned aerial vehicle (UAV)-based proximal and remotely sensed data. Remote Sens. 2021, 13, 4091. Abdulraheem, M.I.; Zhang, W.; Li, S.; Moshayedi, A.J.; Farooque, A.A.; Hu, J. Advancement of remote sensing for soil measurements and applications: A comprehensive review. Sustainability 2023, 15, 15444. Gu, Z.; Qi, Z.; Burghate, R.; Yuan, S.; Jiao, X.; Xu, J. Irrigation scheduling approaches and applications: A review. J. Irrig. Drain. Eng. 2020, 146, 04020007 Xie, X.; Yang, Y.; Li, W.; Liao, N.; Pan, W.; Su, H. Estimation of Leaf Area Index in a Typical Northern Tropical Secondary Monsoon Rainforest by Different Indirect Methods. Remote Sens. 2023, 15, 1621. Buthelezi, S.; Mutanga, O.; Sibanda, M.; Odindi, J.; Clulow, A.D.; Chimonyo, V.G.; Mabhaudhi, T. Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season. Remote Sens. 2023, 15, 1597. Karunathilake, E.; Le, A.T.; Heo, S.; Chung, Y.S.; Mansoor, S. The path to smart farming: Innovations and opportunities in precision agriculture. Agriculture 2023, 13, 1593. Sobjak, R.; De Souza, E.G.; Bazzi, C.L.; Opazo, M.A.U.; Mercante, E.; Aikes Junior, J. Process improvement of selecting the best interpolator and its parameters to create thematic maps. Precis. Agric. 2023, 24, 1461–1496. Dal Prà, A.; Bozzi, R.; Parrini, S.; Immovilli, A.; Davolio, R.; Ruozzi, F.; Fabbri, M.C. Discriminant analysis as a tool to classify farm hay in dairy farms. PLoS ONE 2023, 18, e0294468. Arevalo-Ramirez, T.; Auat Cheein, F. Cluster Analysis for Agriculture. In Encyclopedia of Smart Agriculture Technologies; Zhang, Q., Ed.; Springer International Publishing: Cham, Switzerland, 2022; pp. 1–8. Prakash, S.; Reddy, S.S.; Chaudhary, S.; Vimal, S.; Kumar, A. Multivariate analysis in rice (Oryza sativa L.) germplasms for yield attributing traits. Plant Sci. Today 2024, 11, 64–75. Fu, Z.; Zhang, J.; Jiang, J.; Zhang, Z.; Cao, Q.; Tian, Y.; Zhu, Y.; Cao, W.; Liu, X. Using the time series nitrogen diagnosis curve for precise nitrogen management in wheat and rice. Field Crop. Res. 2024, 307, 109259. Derraz, R.; Muharam, F.M.; Nurulhuda, K.; Jaafar, N.A.; Yap, N.K. Ensemble and single algorithm models to handle multicollinearity of UAV vegetation indices for predicting rice biomass. Comput. Electron. Agric. 2023, 205, 107621. Satpathi, A.; Setiya, P.; Das, B.; Nain, A.S.; Jha, P.K.; Singh, S.; Singh, S. Comparative analysis of statistical and machine learning techniques for rice yield forecasting for Chhattisgarh, India. Sustainability 2023, 15, 2786 Gabriel, K.R. The biplot graphic display of matrices with application to principal component analysis. Biometrika 1971, 58, 453–467. Yan, W.; Tinker, N.A. Biplot analysis of multi-environment trial data: Principles and applications. Can. J. Plant Sci. 2006, 86, 623–645 Mohammadi, R.; Jafarzadeh, J.; Poursiahbidi, M.M.; Hatamzadeh, H.; Amri, A. Genotype-by-environment interaction and stability analysis for grain yield in durum wheat using GGE biplot and genotypic and environmental covariates. Agric. Res. 2023, 12, 364–374. Sharma, V.; Tripathi, A.K.; Mittal, H. Technological revolutions in smart farming: Current trends, challenges & future directions. Comput. Electron. Agric. 2022, 201, 107217. Radočaj, D.; Jurišić, M.; Gašparović, M. The role of remote sensing data and methods in a modern approach to fertilization in precision agriculture. Remote Sens. 2022, 14, 778. McInnes, L.; Healy, J.; Melville, J. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv 2018, arXiv:1802.03426. Murphy, K.P. Probabilistic Machine Learning: An Introduction; MIT Press: Cambridge, MA, USA, 2022 House, D.; Keyser, J.C. Foundations of Physically Based Modeling and Animation; AK Peters/CRC Press: Boca Raton, FL, USA, 2016. De Swaef, T.; Maes, W.H.; Aper, J.; Baert, J.; Cougnon, M.; Reheul, D.; Steppe, K.; Roldán-Ruiz, I.; Lootens, P. Applying RGB- and thermal-based vegetation indices from UAVs for high-throughput field phenotyping of drought tolerance in forage grasses. Remote Sens. 2021, 13, 147 Woebbecke, D.M.; Meyer, G.E.; Von Bargen, K.; Mortensen, D.A. Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 1995, 38, 259–269. Meyer, G.E.; Hindman, T.W.; Laksmi, K. Machine vision detection parameters for plant species identification. In Precision Agriculture and Biological Quality; SPIE: Paris, France, 1999; Volume 3543, pp. 327–335 Gitelson, A.A.; Kaufman, Y.J.; Stark, R.; Rundquist, D. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 2002, 80, 76–87 Meyer, G.E.; Neto, J.C.; Jones, D.D.; Hindman, T.W. Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. Comput. Electron. Agric. 2004, 42, 161–180. Genno, H.; Kobayashi, K. Apple growth evaluated automatically with high-definition field monitoring images. Comput. Electron. Agric. 2019, 164, 104895 Jiménez-Muñoz, J.C.; Sobrino, J.A.; Plaza, A.; Guanter, L.; Moreno, J.; Martínez, P. Comparison between fractional vegetation cover retrievals from vegetation indices and spectral mixture analysis: Case study of PROBA/CHRIS data over an agricultural area. Sensors 2009, 9, 768–793. Steele, M.R.; Gitelson, A.A.; Rundquist, D.C.; Merzlyak, M.N. Nondestructive estimation of anthocyanin content in grapevine leaves. Am. J. Enol. Vitic. 2009, 60, 87–92 Xiaoqin, W.; Miaomiao, W.; Shaoqiang, W.; Yundong, W. Extraction of vegetation information from visible unmanned aerial vehicle images. Trans. Chin. Soc. Agric. Eng. 2015, 31 Bendig, J.; Yu, K.; Aasen, H.; Bolten, A.; Bennertz, S.; Broscheit, J.; Gnyp, M.L.; Bareth, G. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. Geoinf. 2015, 39, 79–87 Kataoka, T.; Kaneko, T.; Okamoto, H.; Hata, S. Crop growth estimation system using machine vision. In Proceedings of the 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003), Kobe, Japan, 20–24 July 2003; IEEE: Piscataway, NJ, USA, 2003; Volume 2, pp. b1079–b1083 Hague, T.; Tillett, N.; Wheeler, H. Automated crop and weed monitoring in widely spaced cereals. Precis. Agric. 2006, 7, 21–32. Buchaillot, M.L.; Gracia-Romero, A.; Vergara-Diaz, O.; Zaman-Allah, M.A.; Tarekegne, A.; Cairns, J.E.; Prasanna, B.M.; Araus, J.L.; Kefauver, S.C. Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques. Sensors 2019, 19, 1815 Vories, E.; Tacker, P.; Hogan, R. Multiple inlet approach to reduce water requirements for rice production. Appl. Eng. Agric. 2005, 21, 611–616. Rejesus, R.M.; Palis, F.G.; Rodriguez, D.G.P.; Lampayan, R.M.; Bouman, B.A. Impact of the alternate wetting and drying (AWD) water-saving irrigation technique: Evidence from rice producers in the Philippines. Food Policy 2011, 36, 280–288. Kriegler, F.J. Preprocessing transformations and their effects on multspectral recognition. In Proceedings of the Sixth International Symposium on Remote Sesning of Environment, Ann Arbor, MI, USA, 13–16 October 1969; pp. 97–131 Shaver, T.; Khosla, R.; Westfall, D. Utilizing green normalized difference vegetation indices (GNDVI) for production level management zone delineation in irrigated corn. In Proceedings of the 18th World Congress of Soil Science, Philadelphia, PA, USA, 9–15 July 2006 Sharifi, A.; Felegari, S. Remotely sensed normalized difference red-edge index for rangeland biomass estimation. Aircr. Eng. Aerosp. Technol. 2023, 95, 1128–1136. Huete, A.R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 1988, 25, 295–309. Steven, M.D. The sensitivity of the OSAVI vegetation index to observational parameters. Remote Sens. Environ. 1998, 63, 49–60 Aparicio, N.; Villegas, D.; Casadesus, J.; Araus, J.L.; Royo, C. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agron. J. 2000, 92, 83–91 Casadesús, J.; Kaya, Y.; Bort, J.; Nachit, M.; Araus, J.; Amor, S.; Ferrazzano, G.; Maalouf, F.; Maccaferri, M.; Martos, V.; et al. Using vegetation indices derived from conventional digital cameras as selection criteria for wheat breeding in water-limited environments. Ann. Appl. Biol. 2007, 150, 227–236. Géron, A. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow; O’Reilly Media, Inc.: Sebastopol, CA, USA, 2022. Triana-Martinez, J.C. Python-gcpds.localbiplot. 2024. Available online: https://github.com/UN-GCPDS/python-gcpds.localbiplot (accessed on 21 March 2024). Wu, L.; Yuan, L.; Zhao, G.; Lin, H.; Li, S.Z. Deep clustering and visualization for end-to-end high-dimensional data analysis. IEEE Trans. Neural Netw. Learn. Syst. 2022, 34, 8543–8554 Wang, H.; Chang, W.; Yao, Y.; Yao, Z.; Zhao, Y.; Li, S.; Liu, Z.; Zhang, X. Cropformer: A new generalized deep learning classification approach for multi-scenario crop classification. Front. Plant Sci. 2023, 14, 1130659.
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spelling	Triana-Martinez, Jenniffer Carolina407c69b2-46f4-42c4-a569-244690587cdb-1Álvarez-Meza, Andrés Marinoe7baa84c-f992-4731-8f15-4cca0d36381c-1Gil-González, Juliand4dc3d5d-c43a-46c7-813c-26d2fb86299b-1De Swaef, Tom789f0db7-00fb-4402-afb1-100c73bec72b-1Fernandez-Gallego, Jose A5c39dc3b-5876-4b8c-bceb-1e5bce9dc255-12025-10-28T20:33:23Z2025-10-28T20:33:23Z2024-08To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within specific subgroups, frequently emerge in agricultural data. Interpreting these complex patterns requires sophisticated analysis due to the presence of noise, high variability, and non-stationarity behavior in the collected samples. Here, we introduce Local Biplot, a methodological framework tailored for discerning meaningful data patterns in non-stationary contexts for precision agriculture. Local Biplot relies on the well-known uniform manifold approximation and projection method, such as UMAP, and local affine transformations to codify non-stationary and non-linear data patterns while maintaining interpretability. This lets us find important clusters for transformation and projection within a single global axis pair. Hence, our framework encompasses variable and observational contributions within individual clusters. At the same time, we provide a relevance analysis strategy to help explain why those clusters exist, facilitating the understanding of data dynamics while favoring interpretability. We demonstrated our method’s capabilities through experiments on both synthetic and real-world datasets, covering scenarios involving grass and rice crops. Moreover, we use random forest and linear regression models to predict water status variables from our Local Biplot-based feature ranking and clusters. Our findings revealed enhanced clustering and prediction capability while emphasizing the importance of input features in precision agriculture. As a result, Local Biplot is a useful tool to visualize, analyze, and compare the intricate underlying patterns and internal structures of complex agricultural datasets.application/pdfTriana-Martinez, J.C.; Álvarez-Meza, A.M.; Gil-González, J.; De Swaef, T.; Fernandez-Gallego, J.A. CropWater Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot. Remote Sens. 2024, 16, 2854. https://doi.org/ 10.3390/rs16152854https://doi.org/ 10.3390/rs16152854https://hdl.handle.net/20.500.12313/5840https://www.mdpi.com/2072-4292/16/15/2854engMultidisciplinary Digital Publishing Institute (MDPI)Suiza1615Remote SensingArouna, A.; Dzomeku, I.K.; Shaibu, A.G.; Nurudeen, A.R. Water management for sustainable irrigation in rice (Oryza sativa L.) production: A review. Agronomy 2023, 13, 1522.Oumarou Abdoulaye, A.; Lu, H.; Zhu, Y.; Alhaj Hamoud, Y.; Sheteiwy, M. The global trend of the net irrigation water requirement of maize from 1960 to 2050. Climate 2019, 7, 124Gui, Y.W.; Sheteiwy, M.S.; Zhu, S.G.; Batool, A.; Xiong, Y.C. Differentiate effects of non-hydraulic and hydraulic root signaling on yield and water use efficiency in diploid and tetraploid wheat under drought stress. Environ. Exp. Bot. 2021, 181, 104287.Al Hamedi, F.; Karthishwaran, K.; Alyafei, M.A.M. Hydroponic wheat production using fresh water and treated wastewater under the semi-arid region. Emir. J. Food Agric 2021, 33, 178Jiang, H.; Hu, H.; Li, B.; Zhang, Z.; Wang, S.; Lin, T. Understanding the non-stationary relationships between corn yields and meteorology via a spatiotemporally varying coefficient model. Agric. For. Meteorol. 2021, 301, 108340.Archana, S.; Kumar, P.S. A Survey on Deep Learning Based Crop Yield Prediction. Nat. Environ. Pollut. Technol. 2023, 22Crusiol, L.G.T.; Nanni, M.R.; Furlanetto, R.H.; Sibaldelli, R.N.R.; Sun, L.; Gonçalves, S.L.; Foloni, J.S.S.; Mertz-Henning, L.M.; Nepomuceno, A.L.; Neumaier, N.; et al. Assessing the sensitive spectral bands for soybean water status monitoring and soil moisture prediction using leaf-based hyperspectral reflectance. Agric. Water Manag. 2023, 277, 108089.Efthimiou, N. Object-oriented soil erosion modelling: A non-stationary approach towards a realistic calculation of soil loss at parcel level. Catena 2023, 222, 106816Ndlovu, H.S.; Odindi, J.; Sibanda, M.; Mutanga, O.; Clulow, A.; Chimonyo, V.G.; Mabhaudhi, T. A comparative estimation of maize leaf water content using machine learning techniques and unmanned aerial vehicle (UAV)-based proximal and remotely sensed data. Remote Sens. 2021, 13, 4091.Abdulraheem, M.I.; Zhang, W.; Li, S.; Moshayedi, A.J.; Farooque, A.A.; Hu, J. Advancement of remote sensing for soil measurements and applications: A comprehensive review. Sustainability 2023, 15, 15444.Gu, Z.; Qi, Z.; Burghate, R.; Yuan, S.; Jiao, X.; Xu, J. Irrigation scheduling approaches and applications: A review. J. Irrig. Drain. Eng. 2020, 146, 04020007Xie, X.; Yang, Y.; Li, W.; Liao, N.; Pan, W.; Su, H. Estimation of Leaf Area Index in a Typical Northern Tropical Secondary Monsoon Rainforest by Different Indirect Methods. Remote Sens. 2023, 15, 1621.Buthelezi, S.; Mutanga, O.; Sibanda, M.; Odindi, J.; Clulow, A.D.; Chimonyo, V.G.; Mabhaudhi, T. Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season. Remote Sens. 2023, 15, 1597.Karunathilake, E.; Le, A.T.; Heo, S.; Chung, Y.S.; Mansoor, S. The path to smart farming: Innovations and opportunities in precision agriculture. Agriculture 2023, 13, 1593.Sobjak, R.; De Souza, E.G.; Bazzi, C.L.; Opazo, M.A.U.; Mercante, E.; Aikes Junior, J. Process improvement of selecting the best interpolator and its parameters to create thematic maps. Precis. Agric. 2023, 24, 1461–1496.Dal Prà, A.; Bozzi, R.; Parrini, S.; Immovilli, A.; Davolio, R.; Ruozzi, F.; Fabbri, M.C. Discriminant analysis as a tool to classify farm hay in dairy farms. PLoS ONE 2023, 18, e0294468.Arevalo-Ramirez, T.; Auat Cheein, F. Cluster Analysis for Agriculture. In Encyclopedia of Smart Agriculture Technologies; Zhang, Q., Ed.; Springer International Publishing: Cham, Switzerland, 2022; pp. 1–8.Prakash, S.; Reddy, S.S.; Chaudhary, S.; Vimal, S.; Kumar, A. Multivariate analysis in rice (Oryza sativa L.) germplasms for yield attributing traits. Plant Sci. Today 2024, 11, 64–75.Fu, Z.; Zhang, J.; Jiang, J.; Zhang, Z.; Cao, Q.; Tian, Y.; Zhu, Y.; Cao, W.; Liu, X. Using the time series nitrogen diagnosis curve for precise nitrogen management in wheat and rice. Field Crop. Res. 2024, 307, 109259.Derraz, R.; Muharam, F.M.; Nurulhuda, K.; Jaafar, N.A.; Yap, N.K. Ensemble and single algorithm models to handle multicollinearity of UAV vegetation indices for predicting rice biomass. Comput. Electron. Agric. 2023, 205, 107621.Satpathi, A.; Setiya, P.; Das, B.; Nain, A.S.; Jha, P.K.; Singh, S.; Singh, S. Comparative analysis of statistical and machine learning techniques for rice yield forecasting for Chhattisgarh, India. Sustainability 2023, 15, 2786Gabriel, K.R. The biplot graphic display of matrices with application to principal component analysis. Biometrika 1971, 58, 453–467.Yan, W.; Tinker, N.A. Biplot analysis of multi-environment trial data: Principles and applications. Can. J. Plant Sci. 2006, 86, 623–645Mohammadi, R.; Jafarzadeh, J.; Poursiahbidi, M.M.; Hatamzadeh, H.; Amri, A. 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Plant Sci. 2023, 14, 1130659.© 2024 by the authors.info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Atribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/Cultivos - Estado hídricoAntennasCropsForestryLinear transformationsMetadataPrecision agricultureRegression analysisCrop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local BiplotArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-8134https://repositorio.unibague.edu.co/bitstreams/f7479b0e-9b63-47fc-a289-1764c743cc2b/download2fa3e590786b9c0f3ceba1b9656b7ac3MD51ORIGINALArtículo.pdfArtículo.pdfapplication/pdf99210https://repositorio.unibague.edu.co/bitstreams/a272f987-aec8-4dc5-b9d8-954065510d74/download7e8ce3ac6af7ba2ddcfabcd16bde86c6MD52TEXTArtículo.pdf.txtArtículo.pdf.txtExtracted texttext/plain3396https://repositorio.unibague.edu.co/bitstreams/eb3c8961-8431-40a9-b992-9e9714aa4602/download25f4f3d03e0ccd7f940753543dd4a204MD53THUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg31493https://repositorio.unibague.edu.co/bitstreams/7e134b86-f3fe-4ff2-ad93-eaa3882f8518/download9b7388541dde7112a145a47766e14c83MD5420.500.12313/5840oai:repositorio.unibague.edu.co:20.500.12313/58402025-10-29 03:01:05.456https://creativecommons.org/licenses/by/4.0/© 2024 by the authors.https://repositorio.unibague.edu.coRepositorio Institucional Universidad de Ibaguébdigital@metabiblioteca.comQ3JlYXRpdmUgQ29tbW9ucyBBdHRyaWJ1dGlvbi1Ob25Db21tZXJjaWFsLU5vRGVyaXZhdGl2ZXMgNC4wIEludGVybmF0aW9uYWwgTGljZW5zZQ0KaHR0cHM6Ly9jcmVhdGl2ZWNvbW1vbnMub3JnL2xpY2Vuc2VzL2J5LW5jLW5kLzQuMC8=

Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot

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