Leaf nutrient levels are traditionally quantified by laboratory chemical analysis, which is time-consuming and requires intensive labor and investment. The objective of this study was to predict the moisture content (MC) and nutrient content of peanut leaves from color parameters (CIE Lab) using a chromameter. This method is faster and requires less labor and investment compared to laboratory chemical analysis. Fifty peanut leaf samples were collected from a commercial peanut field. The samples were analyzed for MC, N, P, K, Ca, Mg, Fe, Mn, Zn, and Cu concentrations after color parameters were acquired by chromameter. A positive and high correlation was found between MC and brightness (L*; r = 0.91) and MC and yellowness (b*; r = 0.95). The results show the possibility of predicting the MC of peanut leaves from color data (R2 = 0.88). A strong relationship was also observed between the measured and predicted levels of P and K based on the PLS2 regression model (R2 = 0.88 and R2 = 0.90, respectively). P and K concentrations of peanut leaves can be predicted from the color parameters to within approximately ±0.03% and ±0.15%, respectively. In contrast to MC, P, and K, concentrations of N, Mg, Mn, Zn, and Cu had only moderate correlation, and Fe concentration had the lowest correlation with color parameters (|r| ≤ 0.27).
All Science Journal Classification (ASJC) codes
- Food Science