Machine learning maps crops in-season using satellite data

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.

RANK_REASON The cluster contains an academic paper detailing a new research methodology and findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• California
• Harmonized Landsat-Sentinel
• Iowa
• USDA Cropland Data Layer

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