Abstract:
The use of satellite-based Remote Sensing (RS) is a well-developed field of research. RS
techniques have been successfully utilized to evaluate the chlorophyll content for the monitoring of
sugarcane crops. This research provides a new framework for inferring the chlorophyll content in
sugarcane crops at the canopy level using unmanned aerial vehicles (UAVs) and spectral vegetation
indices processed with multiple machine learning algorithms. Studies were conducted in a sugarcane
field located in Sugarcane Research Institute (SRI, Uda Walawe, Sri Lanka), with various fertilizer
applications over the entire growing season from 2020 to 2021. An UAV with multispectral camera
was used to collect the aerial images to generate the vegetation indices. Ground measurements
of leaf chlorophyll were used as indications for fertilizer status in the sugarcane field. Different
machine learning (ML) algorithms were used ground-truthing data of chlorophyll content and
spectral vegetation indices to forecast sugarcane chlorophyll content. Several machine learning
algorithms such as MLR, RF, DT, SVR, XGB, KNN and ANN were applied in two ways: before feature
selection (BFS) by training the algorithms with all twenty-four (24) vegetation indices with five (05)
spectral bands and after feature selection (AFS) by training algorithms with fifteen (15) vegetation
indices. All the algorithms with both BFS and AFS methods were compared with an estimated
coefficient of determination (R2
) and root mean square error (RMSE). Spectral indices such as RVI
and DVI were shown to be the most reliable indices for estimating chlorophyll content in sugarcane
fields, with coefficients of determination (R2
) of 0.94 and 0.93, respectively. XGB model shows the
highest validation score (R2
) and lowest RMSE in both methods of BFS (0.96 and 0.14) and AFS (0.98
and 0.78), respectively. However, KNN and SVR algorithms show the lowest validation accuracy
than other models. According to the results, the AFS validation score is higher than BFS in MLR, SVR,
XGB and KNN. Even though, validation score of the ANN model is decreased in AFS. The findings
demonstrated that the use of multispectral UAV could be utilized to estimate chlorophyll content and
measure crop health status over a larger sugarcane field. This methodology will aid in real-time crop
nutrition management in sugarcane plantations by reducing the need for conventional measurement
of sugarcane chlorophyll content.
