Machine learning approaches using satellite remote sensing to inform sustainable farming
Conference Year
January 2023
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
Primary Faculty Mentor Name
Asim Zia
Status
Graduate
Student College
College of Agriculture and Life Sciences
Primary Research Category
Social Science
Machine learning approaches using satellite remote sensing to inform sustainable farming
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