Accuracy and reliability of predictions of E. coli concentrations in water of irrigation ponds from drone-based imagery as affected by parameters of the random forest algorithm

B. J. Morgan; M. D. Stocker; Y. A. Pachepsky; M. S. Kim

doi:10.1117/12.2586220

12 April 2021 Accuracy and reliability of predictions of E. coli concentrations in water of irrigation ponds from drone-based imagery as affected by parameters of the random forest algorithm

B. J. Morgan, M. D. Stocker, Y. A. Pachepsky, M. S. Kim

Author Affiliations +

Proceedings Volume 11754, Sensing for Agriculture and Food Quality and Safety XIII; 117540F (2021) https://doi.org/10.1117/12.2586220
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Microbial water quality monitoring is an essential component of food safety. E. coli bacterium is the major indicator organism used in assessing microbial water quality but dense sampling of water to assess spatial variability is impractical. The objective of this work was to test the hypothesis that sUAS imaging can provide information about the differences in E. coli habitats across two Maryland irrigation ponds and guide water sampling. We used modified GoPro cameras and a MicaSense RedEdge camera in flights shortly before sampling. Ponds P1 (0.37ha) and P2 (0.48ha) were sampled from a boat in the same locations, biweekly, during the 2018 growing season. Average concentrations of E. coli were 0.60±0.04 and 1.04±0.04 (mean ± st. error, log CFU/100 mL) in P1 and P2, respectively. The random forest (RF) machine learning algorithm was applied to relate ground sampling data with co-located image sections. The sensitivity of results to parameters of the RF algorithm was assessed with multiple scenarios. The most influential parameters for both ponds were maximum tree depth and minimum leaf size. The maximum R2 values in predictions of E. coli concentrations were 0.941 (0.943) and 0.532(0.565) in training and validation datasets, respectively, for pond P1 (P2). The most influential inputs for both ponds were red, blue, and green obtained after demosaicing images in the visible range, while P1 included red and blue obtained after demosaicing infrared images. Overall, accurate estimation of E. coli concentrations from imagery data is possible and benefits from tuning algorithm control parameters.

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B. J. Morgan, M. D. Stocker, Y. A. Pachepsky, and M. S. Kim "Accuracy and reliability of predictions of E. coli concentrations in water of irrigation ponds from drone-based imagery as affected by parameters of the random forest algorithm", Proc. SPIE 11754, Sensing for Agriculture and Food Quality and Safety XIII, 117540F (12 April 2021); https://doi.org/10.1117/12.2586220

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