Abstract
The branches of fruit trees provide support for the growth of leaves, buds, flowers, fruits, and other organs. The number and length of branches guarantee the normal growth, flowering, and fruiting of fruit trees and are thus important indicators of tree growth and yield. However, due to their low height and the high number of branches, the precise management of fruit trees lacks a theoretical basis and data support. In this paper, we introduce a method for extracting topological and structural information on fruit tree branches based on LiDAR (Light Detection and Ranging) point clouds and proved its feasibility for the study of fruit tree branches. The results show that based on Terrestrial Laser Scanning (TLS), the relative errors of branch length and number are 7.43% and 12% for first-order branches, and 16.75% and 9.67% for second-order branches. The accuracy of total branch information can reach 15.34% and 2.89%. We also evaluated the potential of backpack-LiDAR by comparing field measurements and quantitative structural models (QSMs) evaluations of 10 sample trees. This comparison shows that in addition to the first-order branch information, the information about other orders of branches is underestimated to varying degrees. The root means square error (RMSE) of the length and number of the first-order branches were 3.91 and 1.30 m, and the relative root means square error (NRMSE) was 14.62% and 11.96%, respectively. Our work represents the first automated classification of fruit tree branches, which can be used in support of precise fruit tree pruning, quantitative forecast of yield, evaluation of fruit tree growth, and the modern management of orchards.
Highlights
Fruit tree branches support the growth of leaves, buds, flowers, fruits, and other organs
The five basic input parameters of the TreeQSM model were sampled within a meaningful value range (Figure 3) to calculate the sensitivity of each parameter to branch information under different samples
Based on terrestrial laser scanning (TLS), we have evaluated the feasibility of the TreeQSM algorithm for grading and exRtermaoctteiSnegnst.h2e02i0n, 1fo2,rxmFaOtRioPnEEoRf aRpEVpIlEeWbranches, indicating that TLS can be used for extracting first1-3oordf 1e7r, seecxopnldor-oedrdtehre, ainnfdluteontacleborfadnicfhfeersenotf papoipnltectlroeueds. dInentshiitsiessebctaisoend, oonn tthheisebxatrsaisc,tiwone ofufratphperleetxrpeelobreradntchhe iniflnufoernmceatoiofnd.ifferent point cloud densities based on the extraction of apple tree branch information
Summary
Fruit tree branches support the growth of leaves, buds, flowers, fruits, and other organs. The appropriate number and length of branches are required to guarantee normal growth, flowering, and fruiting of fruit trees. Fruit tree branch information (branches topology, length, number, etc.) is an important indicator of tree growth and yield. Accurate extraction of fruit tree branch information is of great significance for orchard production. The application of LiDAR in forestry, especially backpack LiDAR and terrestrial laser scanning (TLS), which are high-precision three-dimensional laser point cloud active remote sensing platforms, provides an effective technical means for obtaining tree branch information. There have been many studies on the acquisition of tree parameters based on three-dimensional laser point clouds, mainly focusing on single tree structures, single tree/population canopy parameters, and leaf distribution [1,2,3,4,5], but there has been little quantitative research on the extraction of tree branch information
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