Real-time approaches for characterization of fully and partially scanned canopies in groves

Cheein, Fernando Auat; Guivant, José; Sanz, Ricardo; Escolà, Alexandre; Yandún, Francisco; Torres-Torriti, Miguel; Rosell-Polo, Joan R.

doi:10.1016/j.compag.2015.09.017

Cited by 39 publications

(27 citation statements)

References 25 publications

(31 reference statements)

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“…This issue was more severe when the model was created over the entire plant. The importance of segmenting the point cloud when applying the convex-hull algorithm to estimate volume was also evidenced by Auat Cheein et al [19].…”

Section: Modeling Of 3d Objects From the Point Cloudmentioning

confidence: 88%

“…In order to compute the canopy volume, two main approaches are possible: either a discretization-based method in which small regular geometries are created inside the point cloud structure (occupancy grid approach) [6,7]; or a surface reconstruction method using triangulation of the outer points of the point cloud to represent the surface of the object. Auat Cheein et al [19] applied both the segmented convex-hull and the occupancy grid approaches in a point cloud from four pear trees and over a virtual template object. Both approaches proved to be effective on characterizing the tree canopies.…”

Section: Introductionmentioning

confidence: 99%

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A Method to Obtain Orange Crop Geometry Information Using a Mobile Terrestrial Laser Scanner and 3D Modeling

et al. 2017

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LiDAR (Light Detection and Ranging) technology has been used to obtain geometrical attributes of tree crops in small field plots, sometimes using manual steps in data processing. The objective of this study was to develop a method for estimating canopy volume and height based on a mobile terrestrial laser scanner suited for large commercial orange groves. A 2D LiDAR sensor and a GNSS (Global Navigation Satellite System) receiver were mounted on a vehicle for data acquisition. A georeferenced point cloud representing the laser beam impacts on the crop was created and later classified into transversal sections along the row or into individual trees. The convex-hull and the alpha-shape reconstruction algorithms were used to reproduce the shape of the tree crowns. Maps of canopy volume and height were generated for a 25 ha orange grove. The different options of data processing resulted in different values of canopy volume. The alpha-shape algorithm was considered a good option to represent individual trees whereas the convex-hull was better when representing transversal sections of the row. Nevertheless, the canopy volume and height maps produced by those two methods were similar. The proposed system is useful for site-specific management in orange groves.

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Section: Modeling Of 3d Objects From the Point Cloudmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

A Method to Obtain Orange Crop Geometry Information Using a Mobile Terrestrial Laser Scanner and 3D Modeling

et al. 2017

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“…This study has demonstrated that it is necessary to scan with high point density without gaps due to the complex plant structure of cotton plants [38]. In addition, the dense point cloud could also be helpful for plant volume estimation using the partial scanned plants [32].…”

Section: Discussionmentioning

confidence: 99%

“…For some selected trees the volume estimation based on the 3D model was accurate-the correlation coefficient was up to 0.976 between LiDAR measurements and manual measurements-but the procedure of 3D model reconstruction was very time-consuming as several steps had to be carried out manually. Auat Cheein et al [32] compared four real-time canopy volume estimation approaches based on LiDAR data collected using the methodology [31] and 3D model reconstruction procedure. Results showed that the volume could be estimated using partially scanned canopies on either left or right sides.…”

Section: Introductionmentioning

confidence: 99%

In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR

Sun

Paterson

2017

Remote Sensing

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A LiDAR-based high-throughput phenotyping (HTP) system was developed for cotton plant phenotyping in the field. The HTP system consists of a 2D LiDAR and an RTK-GPS mounted on a high clearance tractor. The LiDAR scanned three rows of cotton plots simultaneously from the top and the RTK-GPS was used to provide the spatial coordinates of the point cloud during data collection. Configuration parameters of the system were optimized to ensure the best data quality. A height profile for each plot was extracted from the dense three dimensional point clouds; then the maximum height and height distribution of each plot were derived. In lab tests, single plants were scanned by LiDAR using 0.5 • angular resolution and results showed an R 2 value of 1.00 (RMSE = 3.46 mm) in comparison to manual measurements. In field tests using the same angular resolution; the LiDAR-based HTP system achieved average R 2 values of 0.98 (RMSE = 65 mm) for cotton plot height estimation; compared to manual measurements. This HTP system is particularly useful for large field application because it provides highly accurate measurements; and the efficiency is greatly improved compared to similar studies using the side view scan.

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“…Surface reconstructions based on enclosing objects (hull-based approach) were reported for orange 58 , olive 70 , pear, and apple trees 71 . Auat Cheein and Guivant 72 applied both the segmented convex hull and the occupancy grid approaches in a point cloud from four pear trees and over a virtual template object. Although a few drawbacks were pointed out, both approaches proved to be effective on characterizing the tree canopies.…”

Section: High-resolution 3d Modeling Of Tree Cropsmentioning

confidence: 99%

Application of light detection and ranging and ultrasonic sensors to high-throughput phenotyping and precision horticulture: current status and challenges

et al. 2018

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Ultrasonic and light detection and ranging (LiDAR) sensors have been some of the most deeply investigated sensing technologies within the scope of digital horticulture. They can accurately estimate geometrical and structural parameters of the tree canopies providing input information for high-throughput phenotyping and precision horticulture. A review was conducted in order to describe how these technologies evolved and identify the main investigated topics, applications, and key points for future investigations in horticulture science. Most research efforts have been focused on the development of data acquisition systems, data processing, and high-resolution 3D modeling to derive structural tree parameters such as canopy volume and leaf area. Reported applications of such sensors for precision horticulture were restricted to real-time variable-rate solutions where ultrasonic or LiDAR sensors were tested to adjust plant protection product or fertilizer dose rates according to the tree volume variability. More studies exploring other applications in site-specific management are encouraged; some that integrates canopy sensing data with other sources of information collected at the within-grove scale (e.g., digital elevation models, soil type maps, historical yield maps, etc.). Highly accurate 3D tree models derived from LiDAR scanning demonstrate their great potential for tree phenotyping. However, the technology has not been widely adopted by researchers to evaluate the performance of new plant varieties or the outcomes from different management practices. Commercial solutions for tree scanning of whole groves, orchards, and nurseries would promote such adoption and facilitate more applied research in plant phenotyping and precision horticulture.

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Real-time approaches for characterization of fully and partially scanned canopies in groves

Cited by 39 publications

References 25 publications

A Method to Obtain Orange Crop Geometry Information Using a Mobile Terrestrial Laser Scanner and 3D Modeling

A Method to Obtain Orange Crop Geometry Information Using a Mobile Terrestrial Laser Scanner and 3D Modeling

In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR

Application of light detection and ranging and ultrasonic sensors to high-throughput phenotyping and precision horticulture: current status and challenges

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