Gradient Boosting Estimation of the Leaf Area Index of Apple Orchards in UAV Remote Sensing

Zhijie Liu,Pengzong Zeng,Ce Feng,Wang Wang,Pengju Guo,Fuzeng Yang,Heng Liu,Pan Fan,Xiangyang Liu

doi:10.3390/rs13163263

Abstract

The leaf area index (LAI) is a key parameter for describing the canopy structure of apple trees. This index is also employed in evaluating the amount of pesticide sprayed per unit volume of apple trees. Hence, numerous manual and automatic methods have been explored for LAI estimation. In this work, the leaf area indices for different types of apple trees are obtained in terms of multispectral remote-sensing data collected with an unmanned aerial vehicle (UAV), along with simultaneous measurements of apple orchards. The proposed approach was tested on apple trees of the “Fuji”, “Golden Delicious”, and “Ruixue” types, which were planted in the Apple Experimental Station of the Northwest Agriculture and Forestry University in Baishui County, Shaanxi Province, China. Five vegetation indices of strong correlation with the apple leaf area index were selected and used to train models of support vector regression (SVR) and gradient-boosting decision trees (GBDT) for predicting the leaf area index of apple trees. The best model was selected based on the metrics of the coefficient of determination (R2) and the root-mean-square error (RMSE). The experimental results showed that the gradient-boosting decision tree model achieved the best performance with an R2 of 0.846, an RMSE of 0.356, and a spatial efficiency (SPAEF) of 0.57. This demonstrates the feasibility of our approach for fast and accurate remote-sensing-based estimation of the leaf area index of apple trees.

Highlights

The leaf area index (LAI) was introduced by the British ecologist D
The random forest (RF) model had root-mean-square error (RMSE) and R2 values of 0.14 and 0.79, respectively, and the results showed that this model can be effectively applied to predict the LAI spatial distribution [34]
The unmanned aerial vehicle (UAV)-based remote sensing is an important tool for quickly obtaining information on vegetation in large-scale areas of apple orchards

Summary

Introduction

The leaf area index (LAI) was introduced by the British ecologist D. 1940s and is defined as the total one-sided area of green leaves per unit land area [1,2,3,4,5]. This means that the LAI is a dimensionless quantity, characterized by a unit of (meter squared per meter squared), and it ranges from 0 (for a bare ground) to 5 (for dense orchards), but it might increase occasionally to over 10 (for highly dense forests such as the equatorial ones). The leaf area index of fruit trees plays a key role in controlling the amount of pesticide applied per unit volume of these trees. LAI-based pesticide management is important for improving spraying efficiency, achieving rational pesticide delivery, and reducing pest control costs [6,7,8,9]

Methods

Results

Discussion

Conclusion