Intercomparison of Machine Learning Algorithms for Remote Sensing-based In-season Crop Mapping
Researchers have developed a method for in-season crop mapping using machine learning algorithms and satellite imagery. This approach aims to provide timely crop information for food security, which is crucial given climate change impacts. The study compared ten different machine learning algorithms, finding that Support Vector Machines performed best, achieving an F1 score of 0.74 for almond mapping and 0.59 for corn mapping. AI
IMPACT Enables more timely agricultural monitoring and response to climate-related crop threats.