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Nagel, P
Saxena, A
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Authors
Nagel, P
Fleming, K
Fleming, K
Schottle, N
Nagel, P
Koch, G
Saxena, A
Dash, M
Verma, A.P
Topics
Big Data, Data Mining and Deep Learning
Profitability and Success Stories in Precision Agriculture
Geospatial Data
Type
Oral
Poster
Year
2018
2022
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Filter results3 paper(s) found.

1. Changing the Cost of Farming: New Tools for Precision Farming

Accurate prescription maps are essential for effective variable rate fertilizer application.  Grid soil sampling has most frequently been used to develop these prescription maps.  Past research has indicated several technical and economic limitations associated with this approach.  There is a need to keep the number of samples to a minimum while still allowing a reasonable level of map quality.  As can be seen, precision agriculture management... P. Nagel, K. Fleming

2. You Can Not Manage What You Dont Measure

The problem of variability in soil nutrient analysis has been studied for years by a number of industry experts; unable to decipher and commercialize hyperspectral soil sensing. Many studies have taken years of testing to account for variability thathas a dramatic impacts on precision of recommendations. The main tradeoff we have identified is between accuracy and precision. Large quantities of raw data are required... K. Fleming, N. Schottle, P. Nagel, G. Koch

3. Cloud Correction of Sentinel-2 NDVI Using S2cloudless Package

Optical satellite-derived Normalized Difference Vegetation Index (NDVI) is by far the most commonly used vegetation index value for crop monitoring. However, it is quite sensitive to the cloud, and cloud shadows and significantly decreases its usability, especially in agricultural applications. Therefore, an accurate and reliable cloud correction method is mandatory for its effective application. To address this issue, we have developed an approach to correct the NDVI values of each and every... A. Saxena, M. Dash, A.P. Verma