Use of Drone Imagery to Predict Leaf Nitrogen Content of Sugarcane Cultivated Under Organic Fertilizer Application

Abstract

This study investigated the potential of unmanned aerial vehicle (UAV) based multispectral imagery (MI) to predict the leaf nitrogen (N) content of sugarcane (Saccharum officinarum L.). MI of canopy cover of two sugarcane varieties (Co 775 and SL 96 128) applied with different doses of N (0 – 550 kg/ha) were captured at 4½ months after planting. These images were used to calculate 10 different vegetation indices (VIs). Five machine learning (ML) models were tested for their potential to predict leaf N status using the most appropriate VIs. The correlation analysis showed that DVI (Difference Vegetation Index) was the most powerful VI for the prediction of leaf N (r = 0.81), followed by the RVI (Ratio Vegetation Index) and NDVI (Normalized Difference Vegetation Index) (R2= 0.78 and 0.77, respectively). A threshold correlation (r > 0.6) was applied to select predictive variables for ML models and performance was evaluated using a validation data set of leaf N content. Individual variety testing revealed that PLSR (Partial Least Squares Regression) and SVR (Support Vector Regression) models as the best prediction models with the highest Coefficient of determination (R2>0.72) and the lowest Root Mean Square Error values (RMSE<0.11). When both variety data were pooled, RF (Random Forest) demonstrated the highest predictive performance on the validation dataset, with an R2 value of 0.66 with a RMSE value of 0.12. Generally, the prediction accuracy of models was less when data from both varieties were pooled. This study postulated the potential for the fusion of UAV MI and ML approaches to predict leaf N states and the importance of developing varietal-specific prediction models for the sugarcane vegetation.