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

编号 030026104

推送时间 20201019

研究领域 森林经理 

年份 2020 

类型 期刊 

语种 英语

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

来源期刊 REMOTE SENSING

期 第261期

发表时间 20200831

关键词 sugarcane;  aboveground fresh weight;  random forest regression;  UAV-LiDAR;  agricultural management; 

摘要 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 (R-2= 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 (R-2= 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.

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