Document Type

Article

Journal/Book Title/Conference

Agronomy

Author ORCID Identifier

Alexander Hernandez https://orcid.org/0000-0001-7690-002X

Matthew D. Robbins https://orcid.org/0000-0002-5467-4452

Kaden Patten https://orcid.org/0009-0002-6583-8528

Volume

14

Issue

11

Publisher

MDPI AG

Publication Date

11-1-2024

Journal Article Version

Version of Record

First Page

1

Last Page

21

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Abstract

Protocols to evaluate turfgrass quality rely on visual ratings that, depending on the rater’s expertise, can be subjective and susceptible to positive and negative drifts. We developed seasonal (spring, summer and fall) as well as inter-seasonal machine learning predictive models of turfgrass quality using multispectral and thermal imagery collected using unmanned aerial vehicles for two years as a proof-of-concept. We chose ordinal regression to develop the models instead of conventional classification to account for the ranked nature of the turfgrass quality assessments. We implemented a fuzzy correction of the resulting confusion matrices to ameliorate the probable drift of the field-based visual ratings. The best seasonal predictions were rendered by the fall (multi-class AUC: 0.774, original kappa 0.139, corrected kappa: 0.707) model. However, the best overall predictions were obtained when observation across seasons and years were used for model fitting (multi-class AUC: 0.872, original kappa 0.365, corrected kappa: 0.872), clearly highlighting the need to integrate inter-seasonal variability to enhance models’ accuracies. Vegetation indices such as the NDVI, GNDVI, RVI, CGI and the thermal band can render as much information as a full array of predictors. Our protocol for modeling turfgrass quality can be followed to develop a library of predictive models that can be used in different settings where turfgrass quality ratings are needed.

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