Assessing grass water use efficiency through smartphone imaging and ImageJ analysis

Abstract

Overwatering and underwatering grass are widespread issues with environmental and financial consequences. Current approaches to assessing grass water use efficiency (WUE) are inaccessible to the public. We developed an accessible method to assess grass WUE: combining smartphone imaging with open access color unmixing analysis. Images were converted from the RGB color space to the CIELAB color space using ImageJ—an open access, user-friendly software—to correct for uneven lighting without compromising image detail. We obtained parameters a* (unmixes green-to-red vector) and b* (unmixes blue-to-yellow vector). We hypothesized that WUE can be accurately determined from grass color and growth, which can be analyzed using CIELAB color unmixing. We tested how nine watering levels (100–900 mL every 4–5 days) affected Stenotaphrum secundatum (St. Augustine grass) over one month in Orange County, Southern California. Color was quantified using a*:b* ratios, growth was tracked using grass area, and pigment composition was analyzed using plot profiles. We analyzed whole samples in the uncontrolled real-world environment, individual leaves in a controlled homemade imaging box, and extracted pigments before and after paper chromatography. Results were clustered using Gaussian finite mixture models, implemented by R package ‘mclust’. Overall trends for grass coverage, a*:b* ratios, and pigment composition were consistent with real-world observations. Cluster analyses were consistent across image types, identified an ideal watering range (600–700 mL), and differentiated between underwatered and overwatered grass. Our hypothesis was supported. Our method can be applied in automated irrigation systems or apps, providing grass WUE assessment for regular consumer use.