{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T18:33:46Z","timestamp":1783535626698,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,5]],"date-time":"2023-03-05T00:00:00Z","timestamp":1677974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico (CNPq)","award":["303767\/2020-0"],"award-info":[{"award-number":["303767\/2020-0"]}]},{"name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico (CNPq)","award":["309250\/2021-8"],"award-info":[{"award-number":["309250\/2021-8"]}]},{"name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico (CNPq)","award":["306022\/2021-4"],"award-info":[{"award-number":["306022\/2021-4"]}]},{"name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico (CNPq)","award":["88\/2021"],"award-info":[{"award-number":["88\/2021"]}]},{"name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico (CNPq)","award":["07\/2022"],"award-info":[{"award-number":["07\/2022"]}]},{"name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico (CNPq)","award":["30478"],"award-info":[{"award-number":["30478"]}]},{"name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico (CNPq)","award":["31333"],"award-info":[{"award-number":["31333"]}]},{"name":"Funda\u00e7\u00e3o de Apoio ao Desenvolvimento do Ensino, Ci\u00eancia, e Tecnologia do Estado de Mato Grosso do Sul (FUNDECT)","award":["303767\/2020-0"],"award-info":[{"award-number":["303767\/2020-0"]}]},{"name":"Funda\u00e7\u00e3o de Apoio ao Desenvolvimento do Ensino, Ci\u00eancia, e Tecnologia do Estado de Mato Grosso do Sul (FUNDECT)","award":["309250\/2021-8"],"award-info":[{"award-number":["309250\/2021-8"]}]},{"name":"Funda\u00e7\u00e3o de Apoio ao Desenvolvimento do Ensino, Ci\u00eancia, e Tecnologia do Estado de Mato Grosso do Sul (FUNDECT)","award":["306022\/2021-4"],"award-info":[{"award-number":["306022\/2021-4"]}]},{"name":"Funda\u00e7\u00e3o de Apoio ao Desenvolvimento do Ensino, Ci\u00eancia, e Tecnologia do Estado de Mato Grosso do Sul (FUNDECT)","award":["88\/2021"],"award-info":[{"award-number":["88\/2021"]}]},{"name":"Funda\u00e7\u00e3o de Apoio ao Desenvolvimento do Ensino, Ci\u00eancia, e Tecnologia do Estado de Mato Grosso do Sul (FUNDECT)","award":["07\/2022"],"award-info":[{"award-number":["07\/2022"]}]},{"name":"Funda\u00e7\u00e3o de Apoio ao Desenvolvimento do Ensino, Ci\u00eancia, e Tecnologia do Estado de Mato Grosso do Sul (FUNDECT)","award":["30478"],"award-info":[{"award-number":["30478"]}]},{"name":"Funda\u00e7\u00e3o de Apoio ao Desenvolvimento do Ensino, Ci\u00eancia, e Tecnologia do Estado de Mato Grosso do Sul (FUNDECT)","award":["31333"],"award-info":[{"award-number":["31333"]}]},{"name":"SIAFEM","award":["303767\/2020-0"],"award-info":[{"award-number":["303767\/2020-0"]}]},{"name":"SIAFEM","award":["309250\/2021-8"],"award-info":[{"award-number":["309250\/2021-8"]}]},{"name":"SIAFEM","award":["306022\/2021-4"],"award-info":[{"award-number":["306022\/2021-4"]}]},{"name":"SIAFEM","award":["88\/2021"],"award-info":[{"award-number":["88\/2021"]}]},{"name":"SIAFEM","award":["07\/2022"],"award-info":[{"award-number":["07\/2022"]}]},{"name":"SIAFEM","award":["30478"],"award-info":[{"award-number":["30478"]}]},{"name":"SIAFEM","award":["31333"],"award-info":[{"award-number":["31333"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Using spectral data to quantify nitrogen (N), phosphorus (P), and potassium (K) contents in soybean plants can help breeding programs develop fertilizer-efficient genotypes. Employing machine learning (ML) techniques to classify these genotypes according to their nutritional content makes the analyses performed in the programs even faster and more reliable. Thus, the objective of this study was to find the best ML algorithm(s) and input configurations in the classification of soybean genotypes for higher N, P, and K leaf contents. A total of 103 F2 soybean populations were evaluated in a randomized block design with two repetitions. At 60 days after emergence (DAE), spectral images were collected using a Sensefly eBee RTK fixed-wing remotely piloted aircraft (RPA) with autonomous take-off, flight plan, and landing control. The eBee was equipped with the Parrot Sequoia multispectral sensor. Reflectance values were obtained in the following spectral bands (SBs): red (660 nm), green (550 nm), NIR (735 nm), and red-edge (790 nm), which were used to calculate the vegetation index (VIs): normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), green normalized difference vegetation index (GNDVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI), modified chlorophyll absorption in reflectance index (MCARI), enhanced vegetation index (EVI), and simplified canopy chlorophyll content index (SCCCI). At the same time of the flight, leaves were collected in each experimental unit to obtain the leaf contents of N, P, and K. The data were submitted to a Pearson correlation analysis. Subsequently, a principal component analysis was performed together with the k-means algorithm to define two clusters: one whose genotypes have high leaf contents and another whose genotypes have low leaf contents. Boxplots were generated for each cluster according to the content of each nutrient within the groups formed, seeking to identify which set of genotypes has higher nutrient contents. Afterward, the data were submitted to machine learning analysis using the following algorithms: decision tree algorithms J48 and REPTree, random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR, used as control). The clusters were used as output variables of the classification models used. The spectral data were used as input variables for the models, and three different configurations were tested: using SB only, using VIs only, and using SBs+VIs. The J48 and SVM algorithms had the best performance in classifying soybean genotypes. The best input configuration for the algorithms was using the spectral bands as input.<\/jats:p>","DOI":"10.3390\/rs15051457","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T01:35:30Z","timestamp":1678066530000},"page":"1457","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Machine Learning in the Classification of Soybean Genotypes for Primary Macronutrients\u2019 Content Using UAV\u2013Multispectral Sensor"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7611-6040","authenticated-orcid":false,"given":"Dthenifer Cordeiro","family":"Santana","sequence":"first","affiliation":[{"name":"Department of Agronomy, State University of S\u00e3o Paulo (UNESP), Ilha Solteira 15385-000, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2303-3465","authenticated-orcid":false,"given":"Marcelo Carvalho Minhoto","family":"Teixeira Filho","sequence":"additional","affiliation":[{"name":"Department of Agronomy, State University of S\u00e3o Paulo (UNESP), Ilha Solteira 15385-000, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcelo Rinaldi","family":"da Silva","sequence":"additional","affiliation":[{"name":"Department of Agronomy, State University of S\u00e3o Paulo (UNESP), Ilha Solteira 15385-000, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paulo Henrique Menezes das","family":"Chagas","sequence":"additional","affiliation":[{"name":"Department of Agronomy, State University of S\u00e3o Paulo (UNESP), Ilha Solteira 15385-000, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o Lucas Gouveia","family":"de Oliveira","sequence":"additional","affiliation":[{"name":"Federal University of Mato Grosso do Sul (UFMS), Chapad\u00e3o do Sul 79560-000, MS, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9522-0342","authenticated-orcid":false,"given":"F\u00e1bio Henrique Rojo","family":"Baio","sequence":"additional","affiliation":[{"name":"Federal University of Mato Grosso do Sul (UFMS), Chapad\u00e3o do Sul 79560-000, MS, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6810-885X","authenticated-orcid":false,"given":"Cid Naudi Silva","family":"Campos","sequence":"additional","affiliation":[{"name":"Federal University of Mato Grosso do Sul (UFMS), Chapad\u00e3o do Sul 79560-000, MS, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8121-0119","authenticated-orcid":false,"given":"Larissa Pereira Ribeiro","family":"Teodoro","sequence":"additional","affiliation":[{"name":"Federal University of Mato Grosso do Sul (UFMS), Chapad\u00e3o do Sul 79560-000, MS, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7102-2077","authenticated-orcid":false,"given":"Carlos Antonio","family":"da Silva Junior","sequence":"additional","affiliation":[{"name":"Department of Geography, State University of Mato Grosso (UNEMAT), Sinop 78550-000, MT, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8236-542X","authenticated-orcid":false,"given":"Paulo Eduardo","family":"Teodoro","sequence":"additional","affiliation":[{"name":"Department of Agronomy, State University of S\u00e3o Paulo (UNESP), Ilha Solteira 15385-000, SP, Brazil"},{"name":"Federal University of Mato Grosso do Sul (UFMS), Chapad\u00e3o do Sul 79560-000, MS, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1986-6432","authenticated-orcid":false,"given":"Luciano Shozo","family":"Shiratsuchi","sequence":"additional","affiliation":[{"name":"LSU Agcenter, School of Plant, Environmental and Soil Sciences, Louisiana State University, 307 Sturgis Hall, Baton Rouge, LA 70726, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1111\/pce.12451","article-title":"Root Phenes That Reduce the Metabolic Costs of Soil Exploration: Opportunities for 21st Century Agriculture","volume":"38","author":"Lynch","year":"2015","journal-title":"Plant Cell Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106001","DOI":"10.1016\/j.compag.2021.106001","article-title":"Development of an Automated Plant Phenotyping System for Evaluation of Salt Tolerance in Soybean","volume":"182","author":"Zhou","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Der Yang, M., Tseng, H.H., Hsu, Y.C., Yang, C.Y., Lai, M.H., and Wu, D.H. (2021). A UAV Open Dataset of Rice Paddies for Deep Learning Practice. Remote Sens., 13.","DOI":"10.3390\/rs13071358"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Panday, U.S., Pratihast, A.K., Aryal, J., and Kayastha, R.B. (2020). A Review on Drone-Based Data Solutions for Cereal Crops. Drones, 4.","DOI":"10.3390\/drones4030041"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Guo, Y., Chen, S., Li, X., Cunha, M., Jayavelu, S., Cammarano, D., and Fu, Y. (2022). Machine Learning-Based Approaches for Predicting SPAD Values of Maize Using Multi-Spectral Images. Remote Sens, 14.","DOI":"10.3390\/rs14061337"},{"key":"ref_6","first-page":"1187","article-title":"The Use of Unmanned Aerial Vehicles (UAVs) for Remote Sensing and Mapping","volume":"37","author":"Everaerts","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"142","DOI":"10.3389\/fpls.2019.00142","article-title":"Hyperspectral Analysis of Leaf Pigments and Nutritional Elements in Tallgrass Prairie Vegetation","volume":"10","author":"Ling","year":"2019","journal-title":"Front Plant. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.neucom.2013.03.057","article-title":"Extreme Learning Machines for Soybean Classification in Remote Sensing Hyperspectral Images","volume":"128","author":"Moreno","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mahajan, G.R., Das, B., Murgaokar, D., Herrmann, I., Berger, K., Sahoo, R.N., Patel, K., Desai, A., Morajkar, S., and Kulkarni, R.M. (2021). Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models. Remote Sens., 13.","DOI":"10.3390\/rs13040641"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"O\u2019Connell, J.L., Byrd, K.B., and Kelly, M. (2014). Remotely-Sensed Indicators of N-Related Biomass Allocation in Schoenoplectus Acutus. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0090870"},{"key":"ref_11","first-page":"101907","article-title":"Improvement of Leaf Nitrogen Content Inference in Valencia-Orange Trees Applying Spectral Analysis Algorithms in UAV Mounted-Sensor Images","volume":"83","author":"Osco","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","unstructured":"Marschner, P. (2012). Marschner\u2019s Mineral Nutrition of Higher Plants (Third Edition), Academic Press."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s12596-019-00517-1","article-title":"Vis\u2013NIR-Based Optical Sensor System for Estimation of Primary Nutrients in Soil","volume":"48","author":"Mukherjee","year":"2019","journal-title":"J. Opt."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105768","DOI":"10.1016\/j.compag.2020.105768","article-title":"Hyperspectral Spectroscopy and Imbalance Data Approaches for Classification of Oil Palm\u2019s Macronutrients Observed from Frond 9 and 17","volume":"178","author":"Amirruddin","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.catena.2016.09.007","article-title":"Hybrid Integration of Multilayer Perceptron Neural Networks and Machine Learning Ensembles for Landslide Susceptibility Assessment at Himalayan Area (India) Using GIS","volume":"149","author":"Pham","year":"2017","journal-title":"Catena"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Camps-Valls, G. (2009, January 1\u20134). Machine Learning in Remote Sensing Data Processing. Proceedings of the 2009 IEEE International Workshop on Machine Learning for Signal Processing, Grenoble, France.","DOI":"10.1109\/MLSP.2009.5306233"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11267","DOI":"10.1038\/s41598-020-68273-y","article-title":"Interactive Machine Learning for Soybean Seed and Seedling Quality Classification","volume":"10","author":"Capobiango","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Orusa, T., Cammareri, D., and Borgogno Mondino, E. (2023). A Scalable Earth Observation Service to Map Land Cover in Geomorphological Complex Areas beyond the Dynamic World: An Application in Aosta Valley (NW Italy). Appl. Sci., 13.","DOI":"10.3390\/app13010390"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.compag.2019.04.035","article-title":"Detection of Nutrition Deficiencies in Plants Using Proximal Images and Machine Learning: A Review","volume":"162","author":"Barbedo","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gava, R., Santana, D.C., Cotrim, M.F., Rossi, F.S., Teodoro, L.P.R., da Silva Junior, C.A., and Teodoro, P.E. (2022). Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models. Sustainability, 14.","DOI":"10.3390\/su14127125"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103810","DOI":"10.1016\/j.infrared.2021.103810","article-title":"Is It Possible to Detect Boron Deficiency in Eucalyptus Using Hyper and Multispectral Sensors?","volume":"116","author":"Teodoro","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_22","first-page":"309","article-title":"Monitoring Vegetation Systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A Soil-Adjusted Vegetation Index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A Modified Soil Adjusted Vegetation Index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/S0034-4257(96)00112-5","article-title":"A Comparison of Vegetation Indices over a Global Set of TM Images for EOS-MODIS","volume":"59","author":"Huete","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1007\/s11119-014-9383-4","article-title":"Canopy-Scale Wavelength and Vegetative Index Sensitivities to Cotton Growth Parameters and Nitrogen Status","volume":"16","author":"Raper","year":"2015","journal-title":"Precis. Agric."},{"key":"ref_29","unstructured":"Bataglia, O.C., Teixeira, J.P.F., Furlani, P.R., Furlani, A.M.C., and Gallo, J.R. (1978). M\u00e9todos de An\u00e1lise Qu\u00edmica de Plantas, IAC."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1590\/1984-70332017v17n2s29","article-title":"Rbio: A Tool for Biometric and Statistical Analysis Using the R Platform","volume":"17","author":"Bhering","year":"2017","journal-title":"Crop. Breed. Appl. Biotechnol."},{"key":"ref_31","first-page":"1","article-title":"R: A Language and Environment for Statistical Computing","volume":"201","author":"Team","year":"2013","journal-title":"Comput. Sci. Rev."},{"key":"ref_32","first-page":"49","article-title":"C4. 5: Programming for Machine Learning","volume":"38","author":"Quinlan","year":"1993","journal-title":"Morgan Kauffmann"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.forsciint.2017.08.005","article-title":"Novel Age Estimation Model Based on Development of Permanent Teeth Compared with Classical Approach and Other Modern Data Mining Methods","volume":"279","author":"Buk","year":"2017","journal-title":"Forensic. Sci. Int."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.eij.2011.04.003","article-title":"Suite of Decision Tree-Based Classification Algorithms on Cancer Gene Expression Data","volume":"12","author":"Badran","year":"2011","journal-title":"Egypt. Inform. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random Forest in Remote Sensing: A Review of Applications and Future Directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2279","DOI":"10.1016\/S0031-3203(01)00178-9","article-title":"Image Processing with Neural Networks\u2014A Review","volume":"35","author":"Handels","year":"2002","journal-title":"Pattern. Recognit."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1007\/s10462-017-9611-1","article-title":"Selecting Training Sets for Support Vector Machines: A Review","volume":"52","author":"Nalepa","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"507","DOI":"10.2307\/2529204","article-title":"A Cluster Analysis Method for Grouping Means in the Analysis of Variance","volume":"30","author":"Scott","year":"1974","journal-title":"Biometrics"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Osco, L.P., Ramos, A.P.M., Faita Pinheiro, M.M., Moriya, \u00c9.A.S., Imai, N.N., Estrabis, N., Ianczyk, F., de Ara\u00fajo, F.F., Liesenberg, V., and Jorge, L.A.d.A. (2020). A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements. Remote Sens., 12.","DOI":"10.3390\/rs12060906"},{"key":"ref_40","unstructured":"Chaney, R.L. (2022). World Soybean Research Conference III: Proceedings, Ames, IA, 12\u201317 August 1984, CRC Press."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Khechba, K., Laamrani, A., Dhiba, D., Misbah, K., and Chehbouni, A. (2021). Monitoring and Analyzing Yield Gap in Africa through Soil Attribute Best Management Using Remote Sensing Approaches: A Review. Remote Sens.","DOI":"10.3390\/rs13224602"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Peng, X., Chen, D., Zhou, Z., Zhang, Z., Xu, C., Zha, Q., Wang, F., and Hu, X. (2022). Prediction of the Nitrogen, Phosphorus and Potassium Contents in Grape Leaves at Different Growth Stages Based on UAV Multispectral Remote Sensing. Remote Sens., 14.","DOI":"10.3390\/rs14112659"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"104154","DOI":"10.1016\/j.envexpbot.2020.104154","article-title":"Effects of Elevated [CO2] on Photosynthesis and Seed Yield Parameters in Two Soybean Genotypes with Contrasting Water Use Efficiency","volume":"178","author":"Soba","year":"2020","journal-title":"Environ. Exp. Bot"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1111\/ppl.13201","article-title":"Root System Architecture, Physiological and Transcriptional Traits of Soybean (Glycine Max L.) in Response to Water Deficit: A Review","volume":"172","author":"Xiong","year":"2021","journal-title":"Physiol. Plant"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1007\/s12355-017-0509-7","article-title":"Use of the Decision Tree Technique to Estimate Sugarcane Productivity Under Edaphoclimatic Conditions","volume":"19","author":"Ferreira","year":"2017","journal-title":"Sugar Tech."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1016\/j.rse.2012.04.011","article-title":"Object Based Image Analysis and Data Mining Applied to a Remotely Sensed Landsat Time-Series to Map Sugarcane over Large Areas","volume":"123","author":"Vieira","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1007\/s12524-013-0286-z","article-title":"A Multiple SVM System for Classification of Hyperspectral Remote Sensing Data","volume":"41","author":"Bigdeli","year":"2013","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"107298","DOI":"10.1016\/j.patcog.2020.107298","article-title":"Deep Support Vector Machine for Hyperspectral Image Classification","volume":"103","author":"Okwuashi","year":"2020","journal-title":"Pattern. Recognit."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support Vector Machines in Remote Sensing: A Review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s11119-020-09740-4","article-title":"Vegetation Indices and NIR-SWIR Spectral Bands as a Phenotyping Tool for Water Status Determination in Soybean","volume":"22","author":"Braga","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Bian, C., Shi, H., Wu, S., Zhang, K., Wei, M., Zhao, Y., Sun, Y., Zhuang, H., Zhang, X., and Chen, S. (2022). Prediction of Field-Scale Wheat Yield Using Machine Learning Method and Multi-Spectral UAV Data. Remote Sens., 14.","DOI":"10.3390\/rs14061474"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1457\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:48:16Z","timestamp":1760122096000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1457"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,5]]},"references-count":51,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051457"],"URL":"https:\/\/doi.org\/10.3390\/rs15051457","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,5]]}}}