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.
Highlights
The use of UAVs for agriculture and plant biosecurity is rapidly increasing [1–6] and the use of UAV remote sensing for precision agriculture (PA) has grown dramatically [7].The use of unmanned aerial vehicles (UAVs) for remote sensing (RS) has developed rapidly as a method of capturing high-resolution images from the near surface of the Earth [8–13].Several remote sensing applications have proven to be a valuable source of reflectance data for estimating various crop canopy variables relating to biophysical, physiological, or biochemical properties [14]
We looked at a total of 24 spectral indices, which is more than prior research of this type have done [42]. to previous studies of Ballester et al [74] and Zeng & Chen, [75], when a single vegetation indices (VIs) was used to create an association with chlorophyll using basic linear regressions, the R2 of the generated relationships had significant fluctuates in values
Chlorophyll is an important crop biophysical feature to measure crop health and create early predictions. This current study looked at the viability of using multispectral
Summary
The use of UAVs for agriculture and plant biosecurity is rapidly increasing [1–6] and the use of UAV remote sensing for precision agriculture (PA) has grown dramatically [7].The use of unmanned aerial vehicles (UAVs) for remote sensing (RS) has developed rapidly as a method of capturing high-resolution images from the near surface of the Earth [8–13].Several remote sensing applications have proven to be a valuable source of reflectance data for estimating various crop canopy variables relating to biophysical, physiological, or biochemical properties [14]. The use of UAVs for agriculture and plant biosecurity is rapidly increasing [1–6] and the use of UAV remote sensing for precision agriculture (PA) has grown dramatically [7]. The use of unmanned aerial vehicles (UAVs) for remote sensing (RS) has developed rapidly as a method of capturing high-resolution images from the near surface of the Earth [8–13]. Several remote sensing applications have proven to be a valuable source of reflectance data for estimating various crop canopy variables relating to biophysical, physiological, or biochemical properties [14]. Many criteria of crop monitoring have already been proved to be relevant to remote sensing data and methodologies [15]. Remote sensing of plant spectral responses has been demonstrated to be a promising method for capturing changes in vegetation attributes while providing a non-destructive approach [17]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
