A LIDAR-Based Tree Canopy Characterization under Simulated Uneven Road Condition: Advance in Tree Orchard Canopy Profile Measurement

Yue Shen,Fida Hussain,Destaw Addis,Hui Liu

doi:10.1155/2017/8367979

Abstract

In real outdoor canopy profile detection, the accuracy of a LIDAR scanner to measure canopy structure is affected by a potentially uneven road condition. The level of error associated with attitude angles from undulations in the ground surface can be reduced by developing appropriate correction algorithm. This paper proposes an offline attitude angle offset correction algorithm based on a 3D affine coordinate transformation. The validity of the correction algorithm is verified by conducting an indoor experiment. The experiment was conducted on an especially designed canopy profile measurement platform. During the experiment, an artificial tree and a tree-shaped carved board were continuously scanned at constant laser scanner travel speed and detection distances under simulated bumpy road conditions. Acquired LIDAR laser scanner raw data was processed offline by exceptionally developed MATLAB program. The obtained results before and after correction method show that the single attitude angle offset correction method is able to correct the distorted data points in tree-shaped carved board profile measurement, with a relative error of 5%, while the compound attitude angle offset correction method is effective to reduce the error associated with compound attitude angle deviation from the ideal scanner pose, with relative error of 7%.

Highlights

Laser scanning sensors can provide more accurate detection of tree crop structures than infrared sensors and have potential to be incorporated in intelligent machines in precision agriculture [1,2,3]
This paper proposed an offline attitude angle offset correction method to adjust the distorted sensor dataset from undulations in the ground surface for precise tree crop detection and profile characterization
Since the occurrence of the distorted data points is governed by the attitude angle offset of the laser scanning sensor, the developed method was based on the coordinate system transformation and data point rotation under controlled conditions

Summary

Introduction

Laser scanning sensors can provide more accurate detection of tree crop structures than infrared sensors and have potential to be incorporated in intelligent machines in precision agriculture [1,2,3]. As Rosell and Sanz stated in [1], tree crop canopy characterization is a significant factor in numerous applications in agriculture. Some important agricultural tasks that can benefit from these plant-geometry characterization are the application of pesticides, irrigation, fertilization, and crop training. Development of fast, easy, and efficient methods to determine the fundamental parameters used to characterize a canopy structure is an important need. Wei and Salyani in [11] developed a laser scanning system to measure canopy height, width, and volume in citrus trees. In citrus trees, this device showed an accuracy of 96% in length measurements in three perpendicular directions. In [12], a 270° radial range laser scanning sensor was evaluated for its accuracy to measure target surface with complex shapes and sizes in X, Y, and Z Cartesian coordinates in different travel speeds

Methods

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Discussion

Conclusion