Predicting quality and yield of growing alfalfa from a uav

Joseph S. Dvorak, L. Felipe Pampolini, Josh J. Jackson, Hassan Seyyedhasani, Michael P. Sama, Ben Goff

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Alfalfa producers would be able to manage their crop production practices better if they knew the distribution of yield and nutritive values of the alfalfa growing throughout their fields. Unmanned aerial vehicles (UAVs) equipped with cameras and photogrammetry techniques provide methods to quickly capture the plant canopy structure at field scale. The goal of this study was to determine how to use the point clouds produced by the photogrammetry process to estimate the yield and nutritive value of alfalfa throughout its growth cycle. During the 2017 growing season, weekly measurements were taken of 1 m2 quadrats (~20 per week, 325 total) in a field of alfalfa managed for forage production. Measurements in each quadrat included manual measurements of maximum and average height, weed presence, disease damage, insect damage, maturity level, stand plant density, and many images of the quadrat from a UAV. After processing to remove outliers, the canopy heights from the photogrammetry point clouds were largely Gaussian distributions. Models were developed using supervised machine learning to estimate yield and nutritive values, including acid detergent fiber (ADF), neutral detergent fiber (NDF), and crude protein (CP), with different numbers of predictor (input) variables. Simple models with two predictor variables were only based on the mean and standard deviation of the heights of the photogrammetry point cloud. The models with three predictor variables added average field maturity level. Finally, the models with six predictor variables added weed presence, insect damage, and disease damage. A linear regression with all interaction terms was found to be the best type of model for predicting yield with six variables. For all other outputs and numbers of predictor variables, a Gaussian random process (GRP) model was best. The models improved with additional predictor variables, so the six-variable models were best able to predict yield and nutritive value. The R2 values for the six-variable models for predicting yield, ADF, NDF, and CP were 0.81, 0.81, 0.78, and 0.79, respectively.

Original languageEnglish
Pages (from-to)63-72
Number of pages10
JournalTransactions of the ASABE
Volume64
Issue number1
DOIs
StatePublished - 2021

Bibliographical note

Funding Information:
This work is supported by the Alfalfa and Forage Research Program (Grant No. 2016-70005-25648, Project Accession No. 1010223) from the USDA National Institute of Food and Agriculture.

Publisher Copyright:
© 2021 American Society of Agricultural and Biological Engineers.

Keywords

  • Alfalfa
  • Machine learning
  • Nutritive value
  • Photogrammetry
  • Unmanned aerial vehicle
  • Yield

ASJC Scopus subject areas

  • Forestry
  • Food Science
  • Biomedical Engineering
  • Agronomy and Crop Science
  • Soil Science

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