Soybean Crop Analysis, Beans, Production, Detection, Health, Change

Chapter Contents (Back)
Classification. Soybeans. Beans.
See also Gross Primary Production, Net Primary Production, GPP, NPP. BibRef

Monteiro, S.T.[Sildomar Takahashi], Minekawa, Y.[Yohei], Kosugi, Y.[Yukio], Akazawa, T.[Tsuneya], Oda, K.[Kunio],
Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery,
PandRS(62), No. 1, May 2007, pp. 2-12.
Elsevier DOI 0709
Agriculture; Hyperspectral image; Modeling; Neural networks; Spatial prediction BibRef

Gusso, A., Ducati, J.R.,
Algorithm for Soybean Classification Using Medium Resolution Satellite Images,
RS(4), No. 10, October 2012, pp. 3127-3142.
DOI Link 1210
Soybean Crop Area Estimation And Mapping In Mato Grosso State, Brazil,
AnnalsPRS(I-7), No. 2012, pp. 215-219.
DOI Link 1209

Xin, Q.C.[Qin-Chuan], Gong, P.[Peng], Yu, C.Q.[Chao-Qing], Yu, L.[Le], Broich, M.[Mark], Suyker, A.E.[Andrew E.], Myneni, R.B.[Ranga B.],
A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US,
RS(5), No. 11, 2013, pp. 5926-5943.
DOI Link 1312

Zhao, F.[Feng], Huang, Y.B.[Yan-Bo], Guo, Y.Q.[Yi-Qing], Reddy, K.N.[Krishna N.], Lee, M.A.[Matthew A.], Fletcher, R.S.[Reginald S.], Thomson, S.J.[Steven J.],
Early Detection of Crop Injury from Glyphosate on Soybean and Cotton Using Plant Leaf Hyperspectral Data,
RS(6), No. 2, 2014, pp. 1538-1563.
DOI Link 1403

Wagle, P.[Pradeep], Xiao, X.M.[Xiang-Ming], Suyker, A.E.[Andrew E.],
Estimation and analysis of gross primary production of soybean under various management practices and drought conditions,
PandRS(99), No. 1, 2015, pp. 70-83.
Elsevier DOI 1502
Gross primary production BibRef

Zhong, L.H.[Li-Heng], Hu, L.[Lina], Yu, L.[Le], Gong, P.[Peng], Biging, G.S.[Gregory S.],
Automated mapping of soybean and corn using phenology,
PandRS(119), No. 1, 2016, pp. 151-164.
Elsevier DOI 1610
Automated classification BibRef

Peng, Y.[Yi], Nguy-Robertson, A.[Anthony], Arkebauer, T.[Timothy], Gitelson, A.A.[Anatoly A.],
Assessment of Canopy Chlorophyll Content Retrieval in Maize and Soybean: Implications of Hysteresis on the Development of Generic Algorithms,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704

Kira, O.[Oz], Nguy-Robertson, A.L.[Anthony L.], Arkebauer, T.J.[Timothy J.], Linker, R.[Raphael], Gitelson, A.A.[Anatoly A.],
Toward Generic Models for Green LAI Estimation in Maize and Soybean: Satellite Observations,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705

Ren, J.[Jie], Campbell, J.B.[James B.], Shao, Y.[Yang],
Estimation of SOS and EOS for Midwestern US Corn and Soybean Crops,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708

Bajwa, S.G.[Sreekala G.], Rupe, J.C.[John C.], Mason, J.[Johnny],
Soybean Disease Monitoring with Leaf Reflectance,
RS(9), No. 2, 2017, pp. xx-yy.
DOI Link 1703

Yuan, H.H.[Huan-Huan], Yang, G.J.[Gui-Jun], Li, C.C.[Chang-Chun], Wang, Y.J.[Yan-Jie], Liu, J.G.[Jian-Gang], Yu, H.Y.[Hai-Yang], Feng, H.K.[Hai-Kuan], Xu, B.[Bo], Zhao, X.Q.[Xiao-Qing], Yang, X.D.[Xiao-Dong],
Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705

Kong, Q.M.[Qing-Ming], Cui, G.[Guowen], Yeo, S.S.[Sang-Soo], Su, Z.B.[Zhong-Bin], Wang, J.J.[Jing-Jing], Hu, F.Z.[Feng-Zhu], Shen, W.Z.[Wei-Zheng],
DBN wavelet transform denoising method in soybean straw composition based on near-infrared rapid detection,
RealTimeIP(13), No. 3, September 2017, pp. 613-626.
Springer DOI 1710

Maimaitijiang, M.[Maitiniyazi], Ghulam, A.[Abduwasit], Sidike, P.[Paheding], Hartling, S.[Sean], Maimaitiyiming, M.[Matthew], Peterson, K.[Kyle], Shavers, E.[Ethan], Fishman, J.[Jack], Peterson, J.[Jim], Kadam, S.[Suhas], Burken, J.[Joel], Fritschi, F.[Felix],
Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine,
PandRS(134), No. Supplement C, 2017, pp. 43-58.
Elsevier DOI 1712
Remote sensing, Unmanned Aerial System (UAS), Phenotyping, Data Fusion, Extreme Learning Machine (ELM), Extreme Learning Machine based Regression (ELR) BibRef

Clemente, A.M.[Augusto Monso], de Carvalho Júnior, O.A.[Osmar Abílio], Guimarães, R.F.[Renato Fontes], McManus, C.[Concepta], Turazi, C.M.V.[Caroline Machado Vasconcelos], Hermuche, P.M.[Potira Meirelles],
Spatial-Temporal Patterns of Bean Crop in Brazil over the Period 1990-2013,
IJGI(6), No. 4, 2017, pp. xx-yy.
DOI Link 1705

Ovando, G.[Gustavo], Sayago, S.[Silvina], Bocco, M.[Mónica],
Evaluating accuracy of DSSAT model for soybean yield estimation using satellite weather data,
PandRS(138), 2018, pp. 208-217.
Elsevier DOI 1804
CERES, TRMM, Crop models, Argentina BibRef

Herrmann, I.[Ittai], Vosberg, S.K.[Steven K.], Ravindran, P.[Prabu], Singh, A.[Aditya], Chang, H.X.[Hao-Xun], Chilvers, M.I.[Martin I.], Conley, S.P.[Shawn P.], Townsend, P.A.[Philip A.],
Leaf and Canopy Level Detection of Fusarium Virguliforme (Sudden Death Syndrome) in Soybean,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804

Sagan, V.[Vasit], Maimaitiyiming, M.[Matthew], Fishman, J.[Jack],
Effects of Ambient Ozone on Soybean Biophysical Variables and Mineral Nutrient Accumulation,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805

Hu, Q.[Qiong], Ma, Y.X.[Ya-Xiong], Xu, B.D.[Bao-Dong], Song, Q.[Qian], Tang, H.J.[Hua-Jun], Wu, W.B.[Wen-Bin],
Estimating Sub-Pixel Soybean Fraction from Time-Series MODIS Data Using an Optimized Geographically Weighted Regression Model,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805

Kaur, S.[Sukhvir], Pandey, S.[Shreelekha], Goel, S.[Shivani],
Semi-automatic leaf disease detection and classification system for soybean culture,
IET-IPR(12), No. 6, June 2018, pp. 1038-1048.
DOI Link 1805

Chaves, M.E.D.[Michel Eustáquio Dantas], de Carvalho Alves, M.[Marcelo], de Oliveira, M.S.[Marcelo Silva], Sáfadi, T.[Thelma],
A Geostatistical Approach for Modeling Soybean Crop Area and Yield Based on Census and Remote Sensing Data,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806

Guan, H.[Haiou], Liu, M.[Meng], Ma, X.D.[Xiao-Dan], Yu, S.[Song],
Three-Dimensional Reconstruction of Soybean Canopies Using Multisource Imaging for Phenotyping Analysis,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809

Gao, F.[Feng], Anderson, M.[Martha], Daughtry, C.[Craig], Johnson, D.[David],
Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810

de la Casa, A., Ovando, G., Bressanini, L., Martínez, J., Díaz, G., Miranda, C.,
Soybean crop coverage estimation from NDVI images with different spatial resolution to evaluate yield variability in a plot,
PandRS(146), 2018, pp. 531-547.
Elsevier DOI 1812
Precision agriculture, Remote sensing, Biomass, Soil water, Yield gap, NDVI BibRef

Maimaitijiang, M.[Maitiniyazi], Sagan, V.[Vasit], Sidike, P.[Paheding], Maimaitiyiming, M.[Matthew], Hartling, S.[Sean], Peterson, K.T.[Kyle T.], Maw, M.J.W.[Michael J.W.], Shakoor, N.[Nadia], Mockler, T.[Todd], Fritschi, F.B.[Felix B.],
Vegetation Index Weighted Canopy Volume Model (CVMVI) for soybean biomass estimation from Unmanned Aerial System-based RGB imagery,
PandRS(151), 2019, pp. 27-41.
Elsevier DOI 1904
Canopy volume model (CVM), Vegetation index weighted canopy volume model (CVM), Photogrammetric point clouds BibRef

Ma, X.D.[Xiao-Dan], Zhu, K.[Kexin], Guan, H.[Haiou], Feng, J.R.[Jia-Rui], Yu, S.[Song], Liu, G.[Gang],
High-Throughput Phenotyping Analysis of Potted Soybean Plants Using Colorized Depth Images Based on A Proximal Platform,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905

Dold, C.[Christian], Hatfield, J.L.[Jerry L.], Prueger, J.H.[John H.], Moorman, T.B.[Tom B.], Sauer, T.J.[Tom J.], Cosh, M.H.[Michael H.], Drewry, D.T.[Darren T.], Wacha, K.M.[Ken M.],
Upscaling Gross Primary Production in Corn-Soybean Rotation Systems in the Midwest,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908

Zhou, J.[Jing], Yungbluth, D.[Dennis], Vong, C.N.[Chin Nee], Scaboo, A.[Andrew], Zhou, J.F.[Jian-Feng],
Estimation of the Maturity Date of Soybean Breeding Lines Using UAV-Based Multispectral Imagery,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909

Zhang, X.Y.[Xiao-Yan], Zhao, J.M.[Jin-Ming], Yang, G.J.[Gui-Jun], Liu, J.G.[Jian-Gang], Cao, J.Q.[Ji-Qiu], Li, C.Y.[Chun-Yan], Zhao, X.Q.[Xiao-Qing], Gai, J.[Junyi],
Establishment of Plot-Yield Prediction Models in Soybean Breeding Programs Using UAV-Based Hyperspectral Remote Sensing,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912

Gosselin, N.[Nichole], Sagan, V.[Vasit], Maimaitiyiming, M.[Matthew], Fishman, J.[Jack], Belina, K.[Kelley], Podleski, A.[Ann], Maimaitijiang, M.[Maitiniyazi], Bashir, A.[Anbreen], Balakrishna, J.[Jayashree], Dixon, A.[Austin],
Using Visual Ozone Damage Scores and Spectroscopy to Quantify Soybean Responses to Background Ozone,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link 2001

Shawon, A.R.[Ashifur Rahman], Ko, J.[Jonghan], Ha, B.[Bokeun], Jeong, S.[Seungtaek], Kim, D.K.[Dong Kwan], Kim, H.Y.[Han-Yong],
Assessment of a Proximal Sensing-integrated Crop Model for Simulation of Soybean Growth and Yield,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002

Radocaj, D.[Dorijan], Jurišic, M.[Mladen], Gašparovic, M.[Mateo], Plašcak, I.[Ivan],
Optimal Soybean (Glycine max L.) Land Suitability Using GIS-Based Multicriteria Analysis and Sentinel-2 Multitemporal Images,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005

Borra-Serrano, I.[Irene], de Swaef, T.[Tom], Quataert, P.[Paul], Aper, J.[Jonas], Saleem, A.[Aamir], Saeys, W.[Wouter], Somers, B.[Ben], Roldán-Ruiz, I.[Isabel], Lootens, P.[Peter],
Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006

Parker, T.A.[Travis A.], Palkovic, A.[Antonia], Gepts, P.[Paul],
Determining the Genetic Control of Common Bean Early-Growth Rate Using Unmanned Aerial Vehicles,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006

Stepanov, A.[Alexey], Dubrovin, K.[Konstantin], Sorokin, A.[Aleksei], Aseeva, T.[Tatiana],
Predicting Soybean Yield at the Regional Scale Using Remote Sensing and Climatic Data,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006

Kross, A.[Angela], Znoj, E.[Evelyn], Callegari, D.[Daihany], Kaur, G.[Gurpreet], Sunohara, M.[Mark], Lapen, D.R.[David R.], McNairn, H.[Heather],
Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007

Dado, W.T.[Walter T.], Deines, J.M.[Jillian M.], Patel, R.[Rinkal], Liang, S.Z.[Sang-Zi], Lobell, D.B.[David B.],
High-Resolution Soybean Yield Mapping Across the US Midwest Using Subfield Harvester Data,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011

Trevisan, R.[Rodrigo], Pérez, O.[Osvaldo], Schmitz, N.[Nathan], Diers, B.[Brian], Martin, N.[Nicolas],
High-Throughput Phenotyping of Soybean Maturity Using Time Series UAV Imagery and Convolutional Neural Networks,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011

Bi, L.N.[Lu-Ning], Hu, G.P.[Gui-Ping], Raza, M.M.[Muhammad Mohsin], Kandel, Y.[Yuba], Leandro, L.[Leonor], Mueller, D.[Daren],
A Gated Recurrent Units (GRU)-Based Model for Early Detection of Soybean Sudden Death Syndrome through Time-Series Satellite Imagery,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011

Hassanzadeh, A.[Amirhossein], Murphy, S.P.[Sean P.], Pethybridge, S.J.[Sarah J.], van Aardt, J.[Jan],
Growth Stage Classification and Harvest Scheduling of Snap Bean Using Hyperspectral Sensing: A Greenhouse Study,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011

Hosseini, M.[Mehdi], Kerner, H.R.[Hannah R.], Sahajpal, R.[Ritvik], Puricelli, E.[Estefania], Lu, Y.H.[Yu-Hsiang], Lawal, A.F.[Afolarin Fahd], Humber, M.L.[Michael L.], Mitkish, M.[Mary], Meyer, S.[Seth], Becker-Reshef, I.[Inbal],
Evaluating the Impact of the 2020 Iowa Derecho on Corn and Soybean Fields Using Synthetic Aperture Radar,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012

Malek-Madani, G.[Gunnar], Walter-Shea, E.A.[Elizabeth A.], Nguy-Robertson, A.L.[Anthony L.], Suyker, A.[Andrew], Arkebauer, T.J.[Timothy J.],
Modeling Gross Primary Production of Midwestern US Maize and Soybean Croplands with Satellite and Gridded Weather Data,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012

Hassanijalilian, O.[Oveis], Igathinathane, C., Bajwa, S.[Sreekala], Nowatzki, J.[John],
Rating Iron Deficiency in Soybean Using Image Processing and Decision-Tree Based Models,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012

Crusiol, L.G.T.[Luis Guilherme Teixeira], Nanni, M.R.[Marcos Rafael], Furlanetto, R.H.[Renato Herrig], Sibaldelli, R.N.R.[Rubson Natal Ribeiro], Cezar, E.[Everson], Sun, L.[Liang], Foloni, J.S.S.[José Salvador Simonetto], Mertz-Henning, L.M.[Liliane Marcia], Nepomuceno, A.L.[Alexandre Lima], Neumaier, N.[Norman], Farias, J.R.B.[José Renato Bouças],
Classification of Soybean Genotypes Assessed Under Different Water Availability and at Different Phenological Stages Using Leaf-Based Hyperspectral Reflectance,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101

Crusiol, L.G.T.[Luís Guilherme Teixeira], Nanni, M.R.[Marcos Rafael], Furlanetto, R.H.[Renato Herrig], Sibaldelli, R.N.R.[Rubson Natal Ribeiro], Cezar, E.[Everson], Sun, L.[Liang], Foloni, J.S.S.[José Salvador Simonetto], Mertz-Henning, L.M.[Liliane Marcia], Nepomuceno, A.L.[Alexandre Lima], Neumaier, N.[Norman], Farias, J.R.B.[José Renato Bouças],
Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103

Habibi, L.N.[Luthfan Nur], Watanabe, T.[Tomoya], Matsui, T.[Tsutomu], Tanaka, T.S.T.[Takashi S. T.],
Machine Learning Techniques to Predict Soybean Plant Density Using UAV and Satellite-Based Remote Sensing,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107

Yoosefzadeh-Najafabadi, M.[Mohsen], Tulpan, D.[Dan], Eskandari, M.[Milad],
Using Hybrid Artificial Intelligence and Evolutionary Optimization Algorithms for Estimating Soybean Yield and Fresh Biomass Using Hyperspectral Vegetation Indices,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107

Smith, S.D.[Stuart D.], Bowling, L.C.[Laura C.], Rainey, K.M.[Katy M.], Cherkauer, K.A.[Keith A.],
Quantifying Effects of Excess Water Stress at Early Soybean Growth Stages Using Unmanned Aerial Systems,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108

Hassanzadeh, A.[Amirhossein], Zhang, F.[Fei], van Aardt, J.[Jan], Murphy, S.P.[Sean P.], Pethybridge, S.J.[Sarah J.],
Broadacre Crop Yield Estimation Using Imaging Spectroscopy from Unmanned Aerial Systems (UAS): A Field-Based Case Study with Snap Bean,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109

Schmitz, P.K.[Peder K.], Kandel, H.J.[Hans J.],
Using Canopy Measurements to Predict Soybean Seed Yield,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109

Oseland, E.[Eric], Shannon, K.[Kent], Zhou, J.F.[Jian-Feng], Fritschi, F.[Felix], Bish, M.D.[Mandy D.], Bradley, K.W.[Kevin W.],
Evaluating the Spectral Response and Yield of Soybean Following Exposure to Sublethal Rates of 2,4-D and Dicamba at Vegetative and Reproductive Growth Stages,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109

Shen, Y.[Yu], Zhang, X.Y.[Xiao-Yang], Yang, Z.W.[Zheng-Wei],
Mapping corn and soybean phenometrics at field scales over the United States Corn Belt by fusing time series of Landsat 8 and Sentinel-2 data with VIIRS data,
PandRS(186), 2022, pp. 55-69.
Elsevier DOI 2203
Field-scale crop phenometrics, VIIRS time series, HLS time series, United States Corn Belt, NASS CP, PhenoCam observations BibRef

Vieira, C.C.[Caio Canella], Sarkar, S.[Shagor], Tian, F.[Fengkai], Zhou, J.[Jing], Jarquin, D.[Diego], Nguyen, H.T.[Henry T.], Zhou, J.F.[Jian-Feng], Chen, P.Y.[Peng-Yin],
Differentiate Soybean Response to Off-Target Dicamba Damage Based on UAV Imagery and Machine Learning,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205

Guarenghi, M.M.[Marjorie Mendes], Walter, A.[Arnaldo], Seabra, J.E.A.[Joaquim E. A.], Rocha, J.V.[Jansle Vieira], Vieira, N.[Nathália], Damame, D.[Desirée], Santos, J.L.[João Luís],
Areas Available for the Potential Sustainable Expansion of Soy in Brazil: A Geospatial Assessment Using the SAFmaps Database,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205

Lou, Z.H.[Zi-Hang], Peng, D.L.[Dai-Liang], Zhang, X.Y.[Xiao-Yang], Yu, L.[Le], Wang, F.M.[Fu-Min], Pan, Y.H.[Yu-Hao], Zheng, S.J.[Shi-Jun], Hu, J.K.[Jin-Kang], Yang, S.L.[Song-Lin], Chen, Y.[Yue], Liu, S.W.[Sheng-Wei],
Soybean EOS Spatiotemporal Characteristics and Their Climate Drivers in Global Major Regions,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205

Pejak, B.[Branislav], Lugonja, P.[Predrag], Antic, A.[Aleksandar], Panic, M.[Marko], Pandžic, M.[Miloš], Alexakis, E.[Emmanouil], Mavrepis, P.[Philip], Zhou, N.[Naweiluo], Marko, O.[Oskar], Crnojevic, V.[Vladimir],
Soya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205

Parreiras, T.C.[Taya Cristo], Bolfe, É.L.[Édson Luis], Chaves, M.E.D.[Michel Eustáquio Dantas], Sanches, I.D.[Ieda Del'Arco], Sano, E.E.[Edson Eyji], de Castro-Victoria, D.[Daniel], Bettiol, G.M.[Giovana Maranhão], Vicente, L.E.[Luiz Eduardo],
Hierarchical Classification of Soybean in the Brazilian Savanna Based on Harmonized Landsat Sentinel Data,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208

Zhang, Y.[Yi], Yang, Y.Z.[Yi-Zhe], Zhang, Q.[Qinwei], Duan, R.Q.[Run-Qing], Liu, J.Q.[Jun-Qi], Qin, Y.C.[Yu-Chu], Wang, X.Z.[Xian-Zhi],
Toward Multi-Stage Phenotyping of Soybean with Multimodal UAV Sensor Data: A Comparison of Machine Learning Approaches for Leaf Area Index Estimation,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301

Cordeiro-Santana, D.[Dthenifer], Teixeira-Filho, M.C.M.[Marcelo Carvalho Minhoto], da Silva, M.R.[Marcelo Rinaldi], Menezes+das Chagas, P.H.[Paulo Henrique], de Oliveira, J.L.G.[João Lucas Gouveia], Rojo-Baio, F.H.[Fábio Henrique], Silva-Campos, C.N.[Cid Naudi], Ribeiro-Teodoro, L.P.[Larissa Pereira], da Silva Junior, C.A.[Carlos Antonio], Teodoro, P.E.[Paulo Eduardo], Shiratsuchi, L.S.[Luciano Shozo],
Machine Learning in the Classification of Soybean Genotypes for Primary Macronutrients' Content Using UAV-Multispectral Sensor,
RS(15), No. 5, 2023, pp. xx-yy.
DOI Link 2303

Vásquez, R.A.R.[Roger A. Rojas], Heenkenda, M.K.[Muditha K.], Nelson, R.[Reg], Serrano, L.S.[Laura Segura],
Developing a New Vegetation Index Using Cyan, Orange, and Near Infrared Bands to Analyze Soybean Growth Dynamics,
RS(15), No. 11, 2023, pp. 2888.
DOI Link 2306

Konno, T.[Tomohiro], Homma, K.[Koki],
Prediction of Areal Soybean Lodging Using a Main Stem Elongation Model and a Soil-Adjusted Vegetation Index That Accounts for the Ratio of Vegetation Cover,
RS(15), No. 13, 2023, pp. 3446.
DOI Link 2307

Sarkar, S.[Supria], Sagan, V.[Vasit], Bhadra, S.[Sourav], Rhodes, K.[Kristen], Pokharel, M.[Meghnath], Fritschi, F.B.[Felix B.],
Soybean seed composition prediction from standing crops using PlanetScope satellite imagery and machine learning,
PandRS(204), 2023, pp. 257-274.
Elsevier DOI 2310
Artificial intelligence, Computer vision, Geographic information system, Precision agriculture, Remote sensing BibRef

Ren, P.T.[Peng-Ting], Li, H.[Heli], Han, S.Y.[Shao-Yu], Chen, R.Q.[Ri-Qiang], Yang, G.J.[Gui-Jun], Yang, H.[Hao], Feng, H.K.[Hai-Kuan], Zhao, C.J.[Chun-Jiang],
Estimation of Soybean Yield by Combining Maturity Group Information and Unmanned Aerial Vehicle Multi-Sensor Data Using Machine Learning,
RS(15), No. 17, 2023, pp. 4286.
DOI Link 2310

Huang, L.X.[Ling-Xiao], Liu, M.[Meng], Yao, N.[Na],
Evaluation of Ecosystem Water Use Efficiency Based on Coupled and Uncoupled Remote Sensing Products for Maize and Soybean,
RS(15), No. 20, 2023, pp. 4922.
DOI Link 2310

Zhang, C.[Chishan], Diao, C.Y.[Chun-Yuan],
A Phenology-guided Bayesian-CNN (PB-CNN) framework for soybean yield estimation and uncertainty analysis,
PandRS(205), 2023, pp. 50-73.
Elsevier DOI 2311
Crop yield, Uncertainty, Phenology, Agriculture, Deep learning BibRef

Pan, D.[Di], Li, C.C.[Chang-Chun], Yang, G.J.[Gui-Jun], Ren, P.[Pengting], Ma, Y.Y.[Yuan-Yuan], Chen, W.N.[Wei-Nan], Feng, H.K.[Hai-Kuan], Chen, R.[Riqiang], Chen, X.[Xin], Li, H.[Heli],
Identification of the Initial Anthesis of Soybean Varieties Based on UAV Multispectral Time-Series Images,
RS(15), No. 22, 2023, pp. 5413.
DOI Link 2311

Fathi, M.[Mahdiyeh], Shah-Hosseini, R.[Reza], Moghimi, A.[Armin],
3D-ResNet-BiLSTM Model: A Deep Learning Model for County-Level Soybean Yield Prediction with Time-Series Sentinel-1, Sentinel-2 Imagery, and Daymet Data,
RS(15), No. 23, 2023, pp. 5551.
DOI Link 2312

Niu, Z.Z.[Zhong-Zhong], Young, J.[Julie], Johnson, W.G.[William G.], Young, B.[Bryan], Wei, X.[Xing], Jin, J.[Jian],
Early Detection of Dicamba and 2,4-D Herbicide Drifting Injuries on Soybean with a New Spatial-Spectral Algorithm Based on LeafSpec, an Accurate Touch-Based Hyperspectral Leaf Scanner,
RS(15), No. 24, 2023, pp. 5771.
DOI Link 2401

Skobalski, J.[Juan], Sagan, V.[Vasit], Alifu, H.[Haireti], Al Akkad, O.[Omar], Lopes, F.A.[Felipe A.], Grignola, F.[Fernando],
Bridging the gap between crop breeding and GeoAI: Soybean yield prediction from multispectral UAV images with transfer learning,
PandRS(210), 2024, pp. 260-281.
Elsevier DOI 2404
Remote Sensing, Plant Breeding, Soybean Yield Prediction, Multispectral Imagery, Machine Learning, Transfer Learning BibRef

Ubben, N.[Niklas], Pukrop, M.[Maren], Jarmer, T.[Thomas],
Spatial Resolution as a Factor for Efficient UAV-Based Weed Mapping: A Soybean Field Case Study,
RS(16), No. 10, 2024, pp. 1778.
DOI Link 2405

Liang, H.[Heng], Zhou, Y.G.[Yong-Gang], Lu, Y.W.[Yu-Wei], Pei, S.K.[Shuang-Kang], Xu, D.[Dong], Lu, Z.[Zhen], Yao, W.B.[Wen-Bo], Liu, Q.[Qian], Yu, L.[Lejun], Li, H.Y.[Hai-Yan],
Evaluation of Soybean Drought Tolerance Using Multimodal Data from an Unmanned Aerial Vehicle and Machine Learning,
RS(16), No. 11, 2024, pp. 2043.
DOI Link 2406

Furlanetto, R.H.[Renato Herrig], Crusiol, L.G.T.[Luís Guilherme Teixeira], Nanni, M.R.[Marcos Rafael], de Oliveira Junior, A.[Adilson], Sibaldelli, R.N.R.[Rubson Natal Ribeiro],
Hyperspectral Data for Early Identification and Classification of Potassium Deficiency in Soybean Plants (Glycine max (L.) Merrill),
RS(16), No. 11, 2024, pp. 1900.
DOI Link 2406

Herrero-Huerta, M., Rainey, K.M.,
High Throughput Phenotyping of Physiological Growth Dynamics From Uas-based 3d Modeling in Soybean,
DOI Link 1912

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Rice Crop Analysis, Production, Detection, Health, Change .

Last update:Jul 18, 2024 at 20:50:34