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Characteristic Analysis of Droughts and Waterlogging Events for Maize
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Data in Midwestern Jilin Province, China,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link
2001
BibRef
Liu, Y.L.[Yun-Ling],
Cen, C.J.[Chao-Jun],
Che, Y.P.[Ying-Pu],
Ke, R.[Rui],
Ma, Y.[Yan],
Ma, Y.T.[Yun-Tao],
Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link
2001
BibRef
Sakamoto, T.[Toshihiro],
Incorporating environmental variables into a MODIS-based crop yield
estimation method for United States corn and soybeans through the use
of a random forest regression algorithm,
PandRS(160), 2020, pp. 208-228.
Elsevier DOI
2001
MODIS, NLDAS-2, Phenology, Machine learning, WDRVI
BibRef
Zhang, L.[Lin],
Liu, Z.[Zhe],
Ren, T.[Tianwei],
Liu, D.[Diyou],
Ma, Z.[Zhe],
Tong, L.[Liang],
Zhang, C.[Chao],
Zhou, T.[Tianying],
Zhang, X.D.[Xiao-Dong],
Li, S.[Shaoming],
Identification of Seed Maize Fields With High Spatial Resolution and
Multiple Spectral Remote Sensing Using Random Forest Classifier,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link
2002
BibRef
Lewis-Beck, C.[Colin],
Walker, V.A.[Victoria A.],
Niemi, J.[Jarad],
Caragea, P.[Petruta],
Hornbuckle, B.K.[Brian K.],
Extracting Agronomic Information from SMOS Vegetation Optical Depth
in the US Corn Belt Using a Nonlinear Hierarchical Model,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link
2003
Soil Moisture and Ocean Salinity data. Analyze stage of growth.
BibRef
Jin, S.,
Su, Y.,
Gao, S.,
Wu, F.,
Ma, Q.,
Xu, K.,
Ma, Q.,
Hu, T.,
Liu, J.,
Pang, S.,
Guan, H.,
Zhang, J.,
Guo, Q.,
Separating the Structural Components of Maize for Field Phenotyping
Using Terrestrial LiDAR Data and Deep Convolutional Neural Networks,
GeoRS(58), No. 4, April 2020, pp. 2644-2658.
IEEE DOI
2004
Classification, deep learning, LiDAR, phenotype,
segmentation, structural components
BibRef
Dong, R.[Rui],
Miao, Y.X.[Yu-Xin],
Wang, X.[Xinbing],
Chen, Z.[Zhichao],
Yuan, F.[Fei],
Zhang, W.[Weina],
Li, H.[Haigang],
Estimating Plant Nitrogen Concentration of Maize using a Leaf
Fluorescence Sensor across Growth Stages,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link
2004
BibRef
Nigon, T.J.[Tyler J.],
Yang, C.[Ce],
Paiao, G.D.[Gabriel Dias],
Mulla, D.J.[David J.],
Knight, J.F.[Joseph F.],
Fernández, F.G.[Fabián G.],
Prediction of Early Season Nitrogen Uptake in Maize Using
High-Resolution Aerial Hyperspectral Imagery,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link
2004
BibRef
Thompson, L.J.[Laura J.],
Puntel, L.A.[Laila A.],
Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor
into a Practical Decision Support System for Precision Nitrogen
Management in Corn,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link
2006
BibRef
Siqueira, R.[Rafael],
Longchamps, L.[Louis],
Dahal, S.[Subash],
Khosla, R.[Raj],
Use of Fluorescence Sensing to Detect Nitrogen and Potassium
Variability in Maize,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link
2006
BibRef
Lee, H.[Hwang],
Wang, J.[Jinfei],
Leblon, B.[Brigitte],
Using Linear Regression, Random Forests, and Support Vector Machine
with Unmanned Aerial Vehicle Multispectral Images to Predict Canopy
Nitrogen Weight in Corn,
RS(12), No. 13, 2020, pp. xx-yy.
DOI Link
2007
BibRef
Ren, T.[Tianwei],
Liu, Z.[Zhe],
Zhang, L.[Lin],
Liu, D.[Diyou],
Xi, X.[Xiaojie],
Kang, Y.[Yanghui],
Zhao, Y.Y.[Yuan-Yuan],
Zhang, C.[Chao],
Li, S.[Shaoming],
Zhang, X.D.[Xiao-Dong],
Early Identification of Seed Maize and Common Maize Production Fields
Using Sentinel-2 Images,
RS(12), No. 13, 2020, pp. xx-yy.
DOI Link
2007
BibRef
Liu, H.[Haojie],
Sun, H.[Hong],
Li, M.[Minzan],
Iida, M.[Michihisa],
Application of Color Featuring and Deep Learning in Maize Plant
Detection,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link
2007
BibRef
Sharma, A.,
Lang, R.H.,
Kurum, M.,
O'Neill, P.E.,
Cosh, M.H.,
L-Band Radar Experiment and Modeling of a Corn Canopy Over a Full
Growing Season,
GeoRS(58), No. 8, August 2020, pp. 5821-5835.
IEEE DOI
2007
Backscatter, Dielectric measurement, L-band, Radar measurements,
Spaceborne radar, Soil measurements, Coherent backscatter, L-band, radar
BibRef
Crema, A.[Alberto],
Boschetti, M.[Mirco],
Nutini, F.[Francesco],
Cillis, D.[Donato],
Casa, R.[Raffaele],
Influence of Soil Properties on Maize and Wheat Nitrogen Status
Assessment from Sentinel-2 Data,
RS(12), No. 14, 2020, pp. xx-yy.
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2007
BibRef
Barzin, R.[Razieh],
Pathak, R.[Rohit],
Lotfi, H.[Hossein],
Varco, J.[Jac],
Bora, G.C.[Ganesh C.],
Use of UAS Multispectral Imagery at Different Physiological Stages
for Yield Prediction and Input Resource Optimization in Corn,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link
2008
BibRef
Qiao, L.[Lang],
Gao, D.H.[De-Hua],
Zhang, J.[Junyi],
Li, M.[Minzan],
Sun, H.[Hong],
Ma, J.[Junyong],
Dynamic Influence Elimination and Chlorophyll Content Diagnosis of
Maize Using UAV Spectral Imagery,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link
2008
BibRef
Stroppiana, D.,
Pepe, M.,
Boschetti, M.,
Crema, A.,
Candiani, G.,
Giordan, D.,
Baldo, M.,
Allasia, P.,
Monopoli, L.,
Estimating Crop Density From Multi-spectral UAV Imagery in Maize Crop,
UAV-g19(619-624).
DOI Link
1912
BibRef
Sibanda, M.,
Mutanga, O.,
Dube, T.,
Odindi, J.,
Mafongoya, P.L.,
Usability, Strength and Practicality of The Upcoming Hyspiri In
Detecting Maize Gray Leafy Spot in Relation to Sentinel-2 Msi, Venµs
And Landsat 8 Oli Spectral Band-settings,
SMPR19(1015-1022).
DOI Link
1912
BibRef
Montes Condori, R.H.,
Romualdo, L.M.,
Martinez Bruno, O.,
de Cerqueira Luz, P.H.,
Comparison Between Traditional Texture Methods and Deep Learning
Descriptors for Detection of Nitrogen Deficiency in Maize Crops,
WVC17(7-12)
IEEE DOI
1804
convolution, crops, feedforward neural nets, image texture,
learning (artificial intelligence), nitrogen, CNN model,
transfer learning
BibRef
Campos, Y.[Yerania],
Rodner, E.[Erik],
Denzler, J.[Joachim],
Sossa, H.[Humberto],
Pajares, G.[Gonzalo],
Vegetation Segmentation in Cornfield Images Using Bag of Words,
ACIVS16(193-204).
Springer DOI
1611
BibRef
Dahms, T.[Thorsten],
Seissiger, S.[Sylvia],
Conrad, C.[Christopher],
Borg, E.[Erik],
Modelling Biophysical Parameters of Maize Using Landsat 8 Time Series,
ISPRS16(B2: 171-175).
DOI Link
1610
See also Important Variables of a RapidEye Time Series for Modelling Biophysical Parameters of Winter Wheat.
BibRef
Hütt, C.,
Tilly, N.,
Schiedung, H.,
Bareth, G.,
Potential Of Multitemporal Tandem-x Derived Crop Surface Models For
Maize Growth Monitoring,
ISPRS16(B7: 803-808).
DOI Link
1610
BibRef
Lu, H.,
Cao, Z.,
Xiao, Y.,
Fang, Z.,
Zhu, Y.,
Fine-grained maize cultivar identification using filter-specific
convolutional activations,
ICIP16(3718-3722)
IEEE DOI
1610
Agriculture
BibRef
Hütt, C.,
Schiedung, H.,
Tilly, N.,
Bareth, G.,
Fusion of high resolution remote sensing images and terrestrial laser
scanning for improved biomass estimation of maize,
Thematic14(101-108).
DOI Link
1404
BibRef
Tilly, N.,
Hoffmeister, D.,
Schiedung, H.,
Hütt, C.,
Brands, J.,
Bareth, G.,
Terrestrial laser scanning for plant height measurement and biomass
estimation of maize,
Thematic14(181-187).
DOI Link
1404
BibRef
Chapter on Remote Sensing, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
Sugar Cane Crop Analysis, Production, Detection, Health, Change .