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Crop performance, aerial, and satellite data from multistate maize yield trials

dataset
posted on 2025-12-23, 23:35 authored by Nikee Shrestha, Anirudha Powadi, Jensina Davis, Timilehin Ayanlade, Huyu Liu, Michael TrossMichael Tross, Jordan Bares, Lina Lopez-CoronaLina Lopez-Corona, Jonathan D. Turkus, Lisa Coffey, Talukder Zaki Jubery, Yufeng Ge, Soumik Sarker, James SchnableJames Schnable, Baskar Ganapathysubramanian, Patrick Schnable
<p>Accurate genotype-specific early yield estimates at fields and plots offer potential benefits to farmers in optimizing their agronomic practices, breeders in screening hundreds and thousands of varieties, and policymakers in decisions contributing to the overall improvement of agriculture and food production systems. Effective, generalizable approaches to track plant growth and predict yield at the individual plot level require large matched datasets of remote sensing and ground truth data collected across multiple environments. Low-altitude drone flights are increasingly being used to collect data from field evaluations of new crop varieties, while satellite imagery is being explored to track yield and management practices at the regional and field scales. Despite their lower spatial resolution, satellite platforms exhibit multiple logistical and technical advantages in scalability and accessibility, and could facilitate plot-level predictions, especially with steadily improving spatial resolution. However, genotype-specific, plot-level, high-resolution satellite images from multiple environments integrated with the ground truth measurements are not yet publicly available. Here we generated, described, and evaluated a set of more than 20,000 plot-level images of over 80 hybrid maize (<em>Zea mays</em>) varieties grown in six locations across the US corn belt under various management practices collected from (near simultaneous) satellite and drone flights integrated with ground truth measurements of crop yield. Of the six baseline models examined, models employing data collected from satellite images often matched or exceeded the performance of models employing data collected from drones for both within-environment and cross-environment yield prediction. Large, multimodal, multi-environment, genetically diverse training datasets such as those generated in this study, along with more complex models could help unlock the power of satellite imagery as an important new addition to the tool of farmers, plant geneticists, crop breeders, and policymakers.</p>

Funding

USDA-NIFA: 2020-67021-31528

USDA-NIFA: 2020-68013-30934

USDA-NIFA: 2021-67021-35329

NSF: 2021-67021-35329

History

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Data contact name

Shrestha, Nikee

Data contact email

nshrestha5@huskers.unl.edu

Publisher

Dryad

Theme

  • Not specified

ISO Topic Category

  • biota

National Agricultural Library Thesaurus terms

altitude; corn; yield forecasting; remote sensing; satellites; data collection; plant growth; hybrids; food production; Corn Belt region; crop yield

Pending citation

  • Yes

Public Access Level

  • Public

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