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From yield history to productivity zone identification with hidden Markov random fields

  • Alex LaytonEmail author
  • James V. Krogmeier
  • Aaron Ault
  • Dennis R. Buckmaster
Article
  • 18 Downloads

Abstract

Modern precision agriculture equipment enables site-specific management by allowing different treatments for different parts of a field. This ability to subdivide the field calls for identifying management zones. A compromise between treating a field uniformly and treating every plant individually is needed, as the former does not maximize yields and the latter is often impractical. This work presents an algorithm for inferring the yield productivity zones (YPZ) for a field based on yield data from multiple years. The algorithm uses a hidden Markov random field model (HMRF) to find regions of the field which likely correspond to the same underlying yield distribution (i.e., productivity zones). These regions are modeled to be the same every year, but their distributions (i.e., yield characteristics) are allowed to vary with time to account for year-to-year variability (from e.g., weather effects, differing crops or crop varieties). The zone assignments and distributions are estimated using stochastic expectation maximization (SEM) and the maximizer of the posterior marginals (MPM). The underlying assumption of the model and algorithm is that the yields corresponding to a given YPZ will behave similarly and therefore derive from the same probability distribution. YPZs are useful inputs for determining management zones. An advantage of this method is that it is able to run with only the yield data which are automatically collected during harvest. Also, this method requires no crop specific calibration or configuration or normalization of the data by year.

Keywords

Hidden Markov models Image segmentation Expectation–maximization algorithms Monte Carlo methods Yield Productivity zone Management zone 

Notes

Acknowledgements

The authors would like to thank Ault Farms for supplying the yield data used. This work was supported by USDA/NIFA Project title: Improving Agricultural Management with Autogenic Mobile Technology, AWS Cloud Credits for Research, and FFAR Project title: An Open Source Framework and Community for Sharing Data and Algorithms.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electrical and Computer EngineeringPurdue UniversityWest LafayetteUSA
  2. 2.School of Agricultural and Biological EngineeringPurdue UniversityWest LafayetteUSA

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