Chlorophyll Indexing via Smartphones for Early Nutrient Stress Detection in Root Crops

In this study, smartphone-based chlorophyll indexing research methods as a fast, inexpensive and non-destructive
procedure of identifying early-onset nutrient stress conditions in root crops including potatoes, carrots and beets are examined. In the study, imaging of smartphone camera is used to evaluate leaf greenness which has a correlation with nitrogen and general nutrient while being estimated using algorithms of chlorophyll estimation. The analysis and comparison of the data of 50 samples of root crops under the condition of controlled nutrients, with the SPAD meter and laboratory chlorophyll content were conducted. Findings demonstrated a high-level positive correlation (r = 0.91) between chlorophyll indices obtained using smartphones and the ecstatic levels of SPAD values with a mean estimation error of 92%. The constructed model was able to identify nutrient stress even up to 7 days before the visual inspection of tradition visual analysis is made. The statistical testing revealed that smartphone images had the capacity to forecast nitrogen deficiency with a mean absolute deviation of 0.06 and a root mean square deviation (RMSE) of 0.08. These results indicate that chlorophyll indexing through smartphones is a cost-effective and reliable method used to control nutrients precisely. The suggested approach will provide farmers with improved capacity to make decisions based on data to encourage effective and sustainable practices of crop production.