Imagine you’re a farmer, and you’re planning what to grow this season. You may want to know what crop would be most valuable to grow. If you’re a policymaker, you might want to know if there’s a shortage of a particular crop so you can decide whether or not to incentivize farmers to grow it through subsidies. To do this, you’d have to know what’s currently growing to make those decisions – crop mapping is the tool needed to efficiently record which crops are growing and where.
Yi-Chia Chang, a Ph.D. student at the University of Illinois Urbana-Champaign (U. of I.), is part of a team of researchers using ACCESS-allocated resources at the National Center for Supercomputing Applications (NCSA) to create more accurate global crop maps. Chang’s research, published in arXiv and accepted to IEEE IGARSS 2025, utilizes machine learning (ML) and remote sensing.
Crop mapping uses satellite imagery to create a map of all the crop types in a particular region. Crop maps are essential tools when it comes to monitoring crops and regional food supplies, and these maps help when farmers are planning which crops to plant in a growing season. The maps can also help with smart farming – using these crop maps applications can monitor growth, precipitation conditions, yield predictions and even disease.
All these tools are great for farmers, but they help at a larger scale as well, helping policymakers and organizations determine how much food and what types are being produced in a given area. Machine learning is an essential component when it comes to keeping these crop maps up-to-date. In the U.S. alone, there are millions of acres of farmland to analyze, label and map. There aren’t enough experts to analyze and keep up with data to create up-to-date, accurate crop maps, so training machines to scan satellite images and label crops is far more efficient and useful.
Our research will enable better-informed agricultural systems for policymakers and stakeholders to support global food security.
–Yi-Chia Chang, University of Illinois
Chang’s study, which was inspired by his team’s previous related research published in NeurIPS 2023 proceedings, looks at how popular Earth observation models work when applied to new regions, particularly in agriculture, where differences in farming practices and uneven data availability make it harder to transfer knowledge between areas. To do this, Chang chose four major cereal grains – maize, soybean, rice and wheat – and then tested three widely-used pre-trained models and compared their performance on data they had seen before (in-distribution) versus data from new regions (out-of-distribution).
“Our future work will focus on expanding crop-type datasets and developing agriculture-specific pre-trained models,” said Chang. “We will also establish benchmarks for agricultural applications of foundation models, such as crop-type mapping and crop-yield prediction, bridging the gap between GeoAI and food security solutions.”
Chang’s work required massive amounts of storage and compute power to complete. GPUs were necessary for the machine-learning aspect of the project to be completed in a timely manner, but a lot of space was also needed for all that satellite imagery.
“HPC resources significantly accelerate the machine learning workflows using GPUs, reducing model training time from hours on CPUs to minutes on GPUs,” said Chang. “Additionally, the large data-storage allocation enables us to efficiently manage the training datasets, pre-trained weights and model outputs in the cluster.”
Chang has experience using research computing and gave high praise to both the ACCESS program and the Delta team. “The ACCESS application process is straightforward, with clear instructions and fast approvals. This program is great for creating open science for everyone. Technical issues are resolved promptly.
“My experience using Delta has been smooth and user-friendly. The admin staff was responsive, approving token exchange for GPU hours and storage allocations within a few days. The technical staff efficiently helped with troubleshooting. I’d like to send a special thanks to Brett Bode for helping to allocate over 50 TB of storage for satellite imagery.”
To read more details about this research, you can find the original article here: Mapping the Earth’s Crops
Resource Provider Institution(s): National Center for Supercomputing Applications (NCSA)
Resources Used: Delta
Affiliations: University of Illinois Urbana-Champaign
Funding Agency: NSF
Grant or Allocation Number(s): CIS240072
The science story featured here was enabled by the U.S. National Science Foundation’s ACCESS program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.