Abstract

Canopy characterization has become important when trying to optimize any kind of agricultural operation in high-growing crops, such as olive. Many sensors and techniques have reported satisfactory results in these approaches and in this work a 2D laser scanner was explored for measuring canopy trees in real-time conditions. The sensor was tested in both laboratory and field conditions to check its accuracy, its cone width, and its ability to characterize olive canopies in situ. The sensor was mounted on a mast and tested in laboratory conditions to check: (i) its accuracy at different measurement distances; (ii) its measurement cone width with different reflectivity targets; and (iii) the influence of the target’s density on its accuracy. The field tests involved both isolated and hedgerow orchards, in which the measurements were taken manually and with the sensor. The canopy volume was estimated with a methodology consisting of revolving or extruding the canopy contour. The sensor showed high accuracy in the laboratory test, except for the measurements performed at 1.0 m distance, with 60 mm error (6%). Otherwise, error remained below 20 mm (1% relative error). The cone width depended on the target reflectivity. The accuracy decreased with the target density.

Highlights

  • Tree crops are attracting the attention of many researchers because of the difficulty of their management [1,2,3,4,5,6,7,8]

  • Knowing canopy characteristics is very important from a management point of view and, canopy characterization is of remarkable importance in most operations performed in these crops, such as harvesting, pruning, spraying, and every related operation performed on the tree crown

  • The low relative error rate generated by the sensor, which stayed below 6% for every sampling distance, was expected from the low absolute error values previously mentioned

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Summary

Introduction

Tree crops are attracting the attention of many researchers because of the difficulty of their management [1,2,3,4,5,6,7,8]. Destructive operations are required, for example for taking leaf samples to characterize the leaf density [7,9,23] These methodologies present the advantage of being easy to use, allowing farmers and technicians to implement them in commercial plantations without an important degree of training but, on the other hand, they are not extremely precise and, in addition, they sometimes require a lot of work, especially if many trees require characterization. The 2D LiDAR, on the other hand, lacks this second horizontal rotation, so if left static, it only characterizes a 2D point set In this case, the movement needs to be added, and in agricultural applications the sensor is attached to a vehicle that moves alongside the tree row, scanning it from its both sides [47]. Union with a total harvested areai.e., of the reflectivity ofan the material and theofgaps percentage, to measure the width of each sensor’s around

Mha and annual production nearly
Materials and
Electronic
Laboratory
Absolute and Relative Accuracy of the Sensor
Density Influence on Sensor’s Accuracy
Sensor’s Accuracy Assessment
Field Results
Conclusions
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