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    • Quantifying Temporal Distortions of Artificial UAV Crop Canopy Temperature Measurements
    • High-throughput phenotyping of stay-green in a sorghum breeding program using unmanned aerial vehicles and machine learning
    • Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms
    • Evaluation of Diverse Sorghum for Leaf Dhurrin Content and Post-Anthesis (Stay-Green) Drought Tolerance
    • Registration of 252 sequenced sorghum mutants as a community reverse genetic resource
    • Sorghum genetic, genomic, and breeding resources
    • Comparison of image georeferencing strategies for agricultural applications of small unoccupied aircraft systems
    • Estimating the Severity of Sugarcane Aphids Infestation on Sorghum with Machine Vision
    • Transcriptomic and evolutionary analysis of the mechanisms by which P. argentatum, a rubber producing perennial, responds to drought
    • Heritability and quantitative trait loci of composition and structural characteristics in sorghum grain
    • A statistical evaluation of replicated block designs and spatial variability in sorghum performance trials
    • Automated detection and measurement of individual sorghum panicles using density-based clustering of terrestrial lidar data
    • Measurement and calibration of plant-height from fixed-wing UAV images
    • Estimation of plant health in a sorghum field infected with anthracnose using a fixed-wing unmanned aerial system
    • Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery
    • Temporal Estimates of Crop Growth in Sorghum and Maize Breeding Enabled by Unmanned Aerial Systems
    • Identification of quantitative trait loci for popping traits and kernel characteristics in sorghum grain
    • Heritability of popping characteristics in sorghum grain
    • Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research
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Peanut Yield Prediction

Feb 19, 2024 · 1 min read
Go to Project Site

Machine learning to predict yield in peanut using growth curves. Multiple informative traits can be pulled from the growth curves.

Last updated on Feb 19, 2024
Drones Machine Learning Crop Yield
N. Ace Pugh, Ph.D.
Authors
N. Ace Pugh, Ph.D.
Research Plant Scientist
← Stay-green Prediction Sep 25, 2024

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