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.