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Portz, C
Acosta, M
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Authors
Vilanova Jr., N.
Molin, J.P
Portz, C
Posada, L.V
Portz, G
Trevisan, R.G
Bhandari, S
Acosta, M
Cordova Gonzalez, C
Raheja, A
Sherafat, A
Topics
Sensor Application in Managing In-season CropVariability
Artificial Intelligence (AI) in Agriculture
Type
Oral
Year
2014
2024
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1. Cotton Field Relations Of Plant Height To Biomass Accumulation And N-Uptake On Conventional And Narrow Row Systems

Although studied for decades, cotton field management remains a challenge for growers, especially due to spatial variability of soil conditions and crop growth, which demands the use of variable rate application technology (VRT) for nitrogen and growth regulators to improve yields and quality and/or save inputs. Canopy optical reflectance sensors are being studied as an option to detect infield variability but may have some limitations due to the known effect of signal saturation when used... N. . Vilanova jr., J.P. Molin, C. Portz, L.V. Posada, G. Portz, R.G. Trevisan

2. Leveraging UAV-based Hyperspectral Data and Machine Learning Techniques for the Detection of Powderly Mildew in Vineyards

This paper presents the development and validation of machine learning models for the detection of powdery mildew in vineyards. The models are trained and validated using custom datasets obtained from unmanned aerial vehicles (UAVs) equipped with a hyperspectral sensor that can collect images in visible/near-infrared (VNIR) and shortwave infrared (SWIR) wavelengths. The dataset consists of the images of vineyards with marked regions for powdery mildew, meticulously annotated using LabelImg. ... S. Bhandari, M. Acosta, C. Cordova gonzalez, A. Raheja, A. Sherafat