# Quantifying Nutrient Content in the Leaves of Cowpea Using Remote Sensing
## 3 Results and Discussion
### Model Validation
The parameters used for analysing model performance for the variable zinc using PLSR in phenological stage V4 presented an R\({}^{2}\) of 0.97, RPD of 1.40 and RMSE of 31.60 g.kg\({}^{-1}\), classifying them as strong for R\({}^{2}\), satisfactory for RPD and insufficient for RMSE (Figure 10b). The zinc estimate tends to overestimate all zinc values. The parameters used for analysing model performance for the variable potassium using PLSR in phenological phase  
(Figure 12a). The potassium estimate tends to overestimate values up to 10.0 g.kg\({}^{-1}\) and underestimate values over 10.0 g.kg\({}^{-1}\). The parameters used for analysing model performance for the variable calcium using PLSR in phenological phase R9 presented an R\({}^{2}\) of 0.61, RPD of 1.00 and RMSE of 6.20 g.kg\({}^{-1}\), classifying them as moderate for R\({}^{2}\) and insufficient for RPD and RMSE (Figure 12b). The calcium estimate tends to overestimate values up to 40.0 g.kg\({}^{-1}\) and underestimate values over 40.0 g.kg\({}^{-1}\). The performance of the model using PLSR with data from all phenological phases utilised for the estimate of phosphorus presented an R\({}^{2}\) of 0.74, RPD of 1.66 and RMSE of 0.47 g.kg\({}^{-1}\), classified as moderate for R\({}^{2}\) and satisfactory for RPD and RMSE (Figure 13a). Figure 12: Relationships between the measured and estimated levels of K (**a**) and Ca (**b**) at phenological stage R9. Figure 13: Relationships between the measured and estimated P (**a**), K (**b**), Ca (**c**) and Zn (**d**) content using data from all three of the phenological stages (V4, R6 and R9). The performance of the model using PLSR with data from all phenological phases utilised for estimating potassium presented an R\({}^{2}\) of 0.77, RPD of 2.06 and RMSE of 3.38 g.kg\({}^{-1}\), classified as moderate for R\({}^{2}\) and insufficient for RPD and RMSE (Figure 13b). The performance of the model using PLSR with data from all phenological phases utilised for estimating calcium presented an R\({}^{2}\) of 0.66, RPD of 1.54 and RMSE of 4.67 g.kg\({}^{-1}\), classified as moderate for R\({}^{2}\), satisfactory for RPD and insufficient for RMSE (Figure 13c). The performance of the model using PLSR with data from all phenological phases utilised for estimating zinc presented an R\({}^{2}\) of 0.80, RPD of 1.66 and RMSE of 30.57 g.kg\({}^{-1}\), classified as moderate for R\({}^{2}\), satisfactory for RPD and insufficient for RMSE (Figure 13d). With the exception of the model for estimating zinc, the others presented a trendline similar to the 1:1 line. [PERSON] [79] also verified the inefficiency of partial least squares regression models used to estimate the zinc content due to the high RMSE value, similar to that found in the present study. However, better correlations can be obtained when the nutrient content is higher. The partial least squares regression model is a powerful means of multivariate analysis for predicting leaf nutrients [22; 27; 75; 76]. On the other hand, the PLSR models for the individual phenological stages are unreliable when estimating leaf nutrients. According to [PERSON] [40], the amount of data used to predict the models influences the results; this explains the better performance of the models tested using joint data from all three phenological stages. The best model selected using data from all phenological stages together was the one using PLSR to estimate phosphorus content, with an R2 of 0.74, RPD of 1.66 and RMSE of 0.47. The selection of the band improved the model performance in the three methodologies applied. The models constructed using PLSR became reliable following the selection of the band utilising data from all phenological stages as a set but still needs to be fitted to predict micronutrients, such as zinc.