This article explores how machine learning and advanced sensor fusion with unmanned aerial systems (UASs) can be used to predict dry pea maturity. UASs offer a unique way to measure and monitor crops due to their low cost and easy accessibility. The use of UASs also allows farmers to gather higher quality data than traditional methods, such as manual scouting, in order to make informed decisions on how to manage their crops. By incorporating machine learning models and sensor fusion with UASs, farmers can accurately predict grain maturity and save on costly losses due to poor harvesting practices.
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The main author of this article is Dr. Zaheda Banu, a professor at the Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Indore. Dr. Banu has an illustrious academic background, having served as a visiting professor at the University of Tokyo, Japan, and as a faculty fellow at the Autonomous Intelligent Systems Institute in Bremen, Germany. Her research focuses on the application of machine learning in agricultural and environmental domains, particularly the integration of UASs with machine learning and advanced sensor fusion. She is passionate about working towards sustainable and cost-effective solutions to improve global food security.
