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

Chapter Contents (Back)
Classification. Soybeans. Beans. 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.
HTML Version. 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.[Xiangming], 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.[Haikuan], 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.[Guijun], 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

Kamal, M.[Mustafa], Schulthess, U.[Urs], Krupnik, T.J.[Timothy J.],
Identification of Mung Bean in a Smallholder Farming Setting of Coastal South Asia Using Manned Aircraft Photography and Sentinel-2 Images,
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

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

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Rice Crop Analysis, Production, Detection, Health, Change .

Last update:Jun 9, 2021 at 21:04:26