Measuring plant growth characteristics using smartphone based image analysis technique in controlled environment agriculture

High plant number per unit area makes it challenging to monitor plant growth in controlled environment agriculture (CEA) systems. Our objective was to develop and validate image analysis technique that uses a smartphone connected to local desktop computer for non-destructive measurement of growth characteristics of several species commonly grown in CEA. Using mobile apps, an iPhone-6 was remotely connected to a local computer containing image-processing software (MATLAB) and script. Smartphone was used to capture images of plants belonging to several species including basil, leaf lettuce, tomato, and zinnia. The images were moved to a folder on cloud storage and remotely processed on a local computer to derive estimated leaf area (LA(estimated)) of plants. Regression analysis indicated a near perfect linear relation between measured leaf area (LA(estimated)) and LA(estimated) (r(2) = 0.98) and shoot dry weight (SDW) and LA(estimated) (r(2) = 0.94) when data were pooled from all species. No significant differences were observed when relative growth rate (RGR) was measured using either SDW or LA(estimated) values. Further, results indicated that real-time and non-invasive LA(estimated) measurements can be used to track plant growth differences over time. This method was able to identify plant growth differences more accurately than visual assessments on plants. Our findings indicate that LA(estimated) can be used for accurate and non-invasive measurement of growth characteristics of plants in academic research. The technique can also aid in maximizing productivity, minimizing resource wastage and harvesting crops timely in commercial production.