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Paulo Eduardo Teodoro
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2020 – today
- 2024
- [j15]Dthenifer Cordeiro Santana
, Gustavo de Faria Theodoro
A New Approach to Identifying Sorghum Hybrids Using UAV Imagery Using Multispectral Signature and Machine Learning. Algorithms 17(1): 23 (2024) - 2023
- [j14]Fábio Henrique Rojo Baio
Maize Yield Prediction with Machine Learning, Spectral Variables and Irrigation Management. Remote. Sens. 15(1): 79 (2023) - [j13]Dthenifer Cordeiro Santana
Machine Learning in the Classification of Soybean Genotypes for Primary Macronutrients' Content Using UAV-Multispectral Sensor. Remote. Sens. 15(5): 1457 (2023) - [j12]Patrícia Monique Crivelari-Costa, Mendelson Lima
Changes in Carbon Dioxide Balance Associated with Land Use and Land Cover in Brazilian Legal Amazon Based on Remotely Sensed Imagery. Remote. Sens. 15(11): 2780 (2023) - [j11]Gabriella Silva de Gregori
Machine Learning in the Hyperspectral Classification of Glycaspis brimblecombei (Hemiptera Psyllidae) Attack Severity in Eucalyptus. Remote. Sens. 15(24): 5657 (2023) - 2022
- [j10]Carlos Antonio da Silva Junior
Fires Drive Long-Term Environmental Degradation in the Amazon Basin. Remote. Sens. 14(2): 338 (2022) - [j9]Alexandre Maniçoba da Rosa Ferraz Jardim, George do Nascimento Araújo Júnior, Marcos Vinícius da Silva
Using Remote Sensing to Quantify the Joint Effects of Climate and Land Use/Land Cover Changes on the Caatinga Biome of Northeast Brazilian. Remote. Sens. 14(8): 1911 (2022) - [j8]Thiago Frank
Is the Gridded Data Accurate? Evaluation of Precipitation and Historical Wet and Dry Periods from ERA5 Data for Canadian Prairies. Remote. Sens. 14(24): 6347 (2022) - [j7]Lanes Beatriz Acosta Jaques
Portable-Mechanical-Sampler System for Real-Time Monitoring and Predicting Soybean Quality in the Bulk Transport. IEEE Trans. Instrum. Meas. 71: 1-12 (2022) - 2021
- [j6]Fernando Saragosa Rossi
19-year remotely sensed data in the forecast of spectral models of the environment. Int. J. Digit. Earth 14(8): 1040-1066 (2021) - [j5]Paulo Eduardo Teodoro
Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data. Remote. Sens. 13(22): 4632 (2021) - 2020
- [j4]Carlos Antonio da Silva Junior
Mapping soybean planting area in midwest Brazil with remotely sensed images and phenology-based algorithm using the Google Earth Engine platform. Comput. Electron. Agric. 169: 105194 (2020) - [j3]Ana Paula Marques Ramos
A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices. Comput. Electron. Agric. 178: 105791 (2020) - [j2]Lucas Prado Osco
Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques. Remote. Sens. 12(19): 3237 (2020)
2010 – 2019
- 2019
- [j1]Carlos Antonio da Silva Junior
Object-based image analysis supported by data mining to discriminate large areas of soybean. Int. J. Digit. Earth 12(3): 270-292 (2019)
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