Predicting canopy chlorophyll concentration in citronella crop using machine learning algorithms and spectral vegetation indices derived from UAV multispectral imagery

Abstract

Unmanned Aerial Vehicle (UAV) remote sensing in precision agriculture (PA) is a promising technique for managing crop inputs. The high spatial-resolution data enables extensive examination of the crop canopy, including identifying low-chlorophyll patches for initiating prophylactic measures as early as possible. In this study DJI M-200 equipped with MicaSense Altum multispectral camera was used to acquire multispectral images of citronella fields under various nutrient treatments at different growth stages. The study assessed the relevance of Machine Learning (ML) on multispectral images to predict citronella chlorophyll concentrations. To predict the crop canopy chlorophyll concentration for citronella, we evaluated Random Forest (RF), Support Vector Machine (SVM) and Partial Least Square (PLS) machine learning models. Based on the Rsquare value of all three models, the SVM machine learning model could best predict the crop canopy chlorophyll concentration with coefficient of determination R2 (0.79), root mean square error (0.05) and mean absolute error (0.04) respectively. The correlation between predicted chlorophyll contents and selected Vegetation Indices (VIs) also demonstrated significant relationship, implying that these can serve as a valuable indicator of crop canopy chlorophyll, which in turn can be used for providing insights into crop health, vigor, and productivity. This study showed that by employing UAV multispectral images and machine learning models the spatial distributions of chlorophyll concentration in a citronella crop at different growth phases can be predicted accurately.