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Acharya, I
Molin, J.P
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Authors
Molin, J.P
Portz, G
Amaral, L.R
Amaral, L.R
Molin, J.P
Jasper, J
Portz, G
Kovacs, P
Maimaitijiang, M
Millett, B
Dorissant, L
Acharya, I
Janjua, U.U
Dilmurat, K
Topics
Sensor Application in Managing In-season Crop Variability
Big Data, Data Mining and Deep Learning
Type
Poster
Year
2012
2024
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Filter results3 paper(s) found.

1. Measuring Sugarcane Height in Complement to Biomass Sensor for Nitrogen Management

Although extensive studied, nitrogen management remains a challenger for sugarcane growers, especially the nutrient spatial variability management, which demands the use of variable rate application. Canopy reflectance sensors are being studied, but it seems to saturate the sensor signal... J.P. Molin, G. Portz, L.R. Amaral

2. Optimum Sugarcane Growth Stage for Canopy Reflectance Sensor to Predict Biomass and Nitrogen Uptake

The recent technology of plant canopy reflectance sensors can provide the status of biomass and nitrogen nutrition of sugarcane spatially and in real time, but it is necessary to know the right moment to use this technology aiming the best predictions of the crop parameters... L.R. Amaral, J.P. Molin, J. Jasper, G. Portz

3. Simultaneously Estimating Crop Biomass and Nutrient Parameters Using UAS Remote Sensing and Multitask Learning

Rapid and accurate estimation of crop growth status and nutrient levels such as aboveground biomass, nitrogen, phosphorus, and potassium concentrations and uptake is critical with respect to precision agriculture and field-based crop monitoring. Recent developments in Uncrewed Aircraft Systems (UAS) and sensor technologies have enabled the collection of high spatial, spectral, and temporal remote sensing data over large areas at a lower cost. Coupled deep learning-based modeling approaches with... P. Kovacs, M. Maimaitijiang, B. Millett, L. Dorissant, I. Acharya, U.U. Janjua, K. Dilmurat