Intercomparison of Machine Learning Algorithms for Remote Sensing-based In-season Crop Mapping
arXiv:2606.05731v1 Announce Type: new Abstract: In-season crop type mapping is critical for food security in the face of increasingly extreme climate-related threats to crops. Currently, the USDA Cropland Data Layer provides crop type labels at 30m resolution and is available the February after harvest, but no product exists that maps crop types before harvest with satisfactory accuracy that would allow emergency managers to respond to crop threats in near real time. Furthermore, the relative advantages of a wide range of algorithms have not been evaluated in a way that accounts for interannua
Climate change is increasing the frequency of extreme weather events, making early and accurate crop yield predictions crucial for food security and disaster response.
Improving in-season crop mapping with AI can provide near real-time insights for emergency management and agricultural policy, allowing for proactive responses to potential food shortages.
The ability to map crop types before harvest with satisfactory accuracy changes the timeline of critical agricultural intelligence from months after harvest to near real-time.
- · Agricultural technology companies
- · Emergency management agencies
- · Farmers and agricultural businesses
- · Food security organizations
- · Traditional crop survey methods
- · Regions dependent on delayed agricultural data
More precise and timely agricultural data becomes available to stakeholders.
Improved resource allocation and risk management strategies in agriculture and supply chains.
Enhanced global food security and reduced volatility in agricultural commodity markets due to better foresight.
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