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

Chapter Contents (Back)
Classification. Soybeans. 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
BibRef
And:
Soybean Crop Area Estimation And Mapping In Mato Grosso State, Brazil,
AnnalsPRS(I-7), No. 2012, pp. 215-219.
HTML Version. 1209
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef


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


Last update:Oct 1, 2019 at 15:23:24