Machine learning approaches using satellite remote sensing to inform sustainable farming
Anjum, Rubaina
Anjum, Rubaina
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Abstract
Sustainable practices in agricultural management have become necessary to improve crop yields and minimize costs to farmers as well as to achieve wider ecosystem health and impacts on and adaptation to climate change. Precision agriculture (PA) has recently emerged as a method for monitoring and evaluating farming practices using a variety of high technology sensors and tools. Satellite remote sensing is widely used in PA and can provide data with high spatial and temporal resolution. In this paper, we apply machine learning approaches to satellite data and predict crop yield and soil organic matter for a farm in Pennsylvania
Description
Graduate
Date
2023-01-01