Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data

Jing-Xian Xu,Shu-Yun Wei,Yuan-Chen Wang,Jun Ma,Yi-Fei Liu,Hai-Qiang Guo,Wei-Xiong Wu,Ya-Nan Tang,Wan-Ben Wu,Jin-Hua Shao

doi:10.3390/rs12172823

Abstract

Sugarcane is a multifunctional crop mainly used for sugar and renewable bioenergy production. Accurate and timely estimation of the sugarcane yield before harvest plays a particularly important role in the management of agroecosystems. The rapid development of remote sensing technologies, especially Light Detecting and Ranging (LiDAR), significantly enhances aboveground fresh weight (AFW) estimations. In our study, we evaluated the capability of LiDAR mounted on an Unmanned Aerial Vehicle (UAV) in estimating the sugarcane AFW in Fusui county, Chongzuo city of Guangxi province, China. We measured the height and the fresh weight of sugarcane plants in 105 sampling plots, and eight variables were extracted from the field-based measurements. Six regression algorithms were used to build the sugarcane AFW model: multiple linear regression (MLR), stepwise multiple regression (SMR), generalized linear model (GLM), generalized boosted model (GBM), kernel-based regularized least squares (KRLS), and random forest regression (RFR). The results demonstrate that RFR (R2 = 0.96, RMSE = 1.27 kg m−2) performs better than other models in terms of prediction accuracy. The final fitted sugarcane AFW distribution maps exhibited good agreement with the observed values (R2 = 0.97, RMSE = 1.33 kg m−2). Canopy cover, the distance to the road, and tillage methods all have an impact on sugarcane AFW. Our study provides guidance for calculating the optimum planting density, reducing the negative impact of human activities, and selecting suitable tillage methods in actual cultivation and production.

Highlights

Sugarcane is a crop that grows in the tropics and subtropics and serves numerous economic and ecological functions [1,2]
Most of the previous studies focused on the application of unmanned aerial vehicle (UAV)-light detection and ranging (LiDAR) data in the prediction of forest aboveground biomass (AGB), whereas our study demonstrated that it is feasible to use this technology for sugarcane
The effectiveness of UAV-LiDAR data is demonstrated by providing proper metrics according to the sugarcane structural parameters

Summary

Introduction

Sugarcane is a crop that grows in the tropics and subtropics and serves numerous economic and ecological functions [1,2]. Light detection and ranging (LiDAR) is applicable in yield estimation because of its ability to obtain related information such as height at a fine spatial resolution [11]. In addition to the abovementioned three methods, other approaches (including using synthetic aperture radar (SAR) data, building regression models based on remotely sensed indicators or mixed information together with bio-climatic predictor variables, and so on) is useful to quantify the expected yield [9,10]. LiDAR, referred to as laser scanning, is an active remote sensing technology that can directly obtain information on the vertical vegetation structure [12]. The estimation of sugarcane aboveground fresh weight (AFW) in agroecosystem is similar to that of vegetation AGB in other ecosystems. In-depth investigations on the feasibility of UAV-LiDAR technology in the estimation of sugarcane AFW are necessary

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Methods

Conclusion