Carlos Antonio da Silva Junior
Orcid: 0000-0002-7102-2077
According to our database1,
Carlos Antonio da Silva Junior
authored at least 15 papers
between 2019 and 2024.
Collaborative distances:
Collaborative distances:
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Bibliography
2024
A New Approach to Identifying Sorghum Hybrids Using UAV Imagery Using Multispectral Signature and Machine Learning.
Algorithms, 2024
2023
Machine Learning in the Hyperspectral Classification of Glycaspis brimblecombei (Hemiptera Psyllidae) Attack Severity in Eucalyptus.
Remote. Sens., December, 2023
Machine Learning in the Classification of Soybean Genotypes for Primary Macronutrients' Content Using UAV-Multispectral Sensor.
Remote. Sens., March, 2023
Maize Yield Prediction with Machine Learning, Spectral Variables and Irrigation Management.
Remote. Sens., January, 2023
Changes in Carbon Dioxide Balance Associated with Land Use and Land Cover in Brazilian Legal Amazon Based on Remotely Sensed Imagery.
Remote. Sens., 2023
2022
Is the Gridded Data Accurate? Evaluation of Precipitation and Historical Wet and Dry Periods from ERA5 Data for Canadian Prairies.
Remote. Sens., December, 2022
Remote. Sens., 2022
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., 2022
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022
2021
Predicting Days to Maturity, Plant Height, and Grain Yield in Soybean: A Machine and Deep Learning Approach Using Multispectral Data.
Remote. Sens., 2021
Int. J. Digit. Earth, 2021
2020
Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques.
Remote. Sens., 2020
A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices.
Comput. Electron. Agric., 2020
Mapping soybean planting area in midwest Brazil with remotely sensed images and phenology-based algorithm using the Google Earth Engine platform.
Comput. Electron. Agric., 2020
2019
Object-based image analysis supported by data mining to discriminate large areas of soybean.
Int. J. Digit. Earth, 2019