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.
Microbial quality of irrigation water is the public health issue that is the subject of regulatory actions mandated by the Food Safety Modification Act. Concentrations of the bacterium E. coli are currently used to derive the microbial water quality metrics. Direct E. coli monitoring requires substantial resources. We hypothesized that drone based imagery can reflect fine-scale differences in E. coli habitats and its survival in irrigation ponds. We tested this assumption using the DJI Matrice 600 Pro sUAS equipped with modified GoPro’s and a MicaSense camera. Digital numbers from imagery were averaged across the 46 sampling locations and compared to 10 water quality parameters using rule-based machine-learning algorithms for estimating E. coli concentrations at a Maryland irrigation pond. Cross-validation with Bootstrap obtained statistical distributions of RMSE and determination coefficient R2 of the decision rule based estimators. The average R2 was 0.79 which is comparable with R2 of estimates from the full set of water quality parameters. Overall, the results indicate the promise of proximal sensing with drone-based imagery to serve as an information source for evaluating microbial water quality.
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