Advances in Structured Light Sensors Applications in Precision Agriculture and Livestock Farming

Rosell-Polo, Joan R.; Cheein, Fernando Auat; Gregorio, Eduard; Andújar, Dionisio; Puigdomènech, Lluís; Masip, Joan; Escolà, Alexandre

doi:10.1016/bs.agron.2015.05.002

Cited by 65 publications

(59 citation statements)

References 57 publications

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“…The recent introduction of terrestrial multi-return laser scanners may improve the performance of current sensors in tree crop characterization. Other authors have used time-of-flight cameras and structured light sensors, which enable the acquisition of depth images and 3D point clouds of plants, for several agricultural purposes (Chéné et al 2012;Nock et al 2013;Rosell-Polo et al 2015). However, these sensors are still not ready to be used in a feasible way in commercial orchards since they need to be either further developed, stationary, or to take multiple shots of the scene from different angles to obtain useful data.…”

Section: Introductionmentioning

confidence: 99%

Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds

et al. 2016

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LiDAR sensors are widely used in many areas and, in recent years, that includes agricultural tasks. In this work, a self-developed mobile terrestrial laser scanner based on a 2D light detection and ranging (LiDAR) sensor was used to scan an intensive olive orchard, and different algorithms were developed to estimate canopy volume. Canopy volume estimations derived from LiDAR sensor readings were compared to conventional estimations used in fruticulture/horticulture research and the results prove that they are equivalent with coefficients of correlation ranging from r=0.56 to r=0.82 depending on the algorithms used. Additionally, tools related to analysis of point cloud data from the LiDAR-based system are proposed to extract further geometrical and structural information from tree row crop canopies to be offered to farmers and technical advisors as digital raster maps. Having high spatial resolution information on canopy geometry (i.e. height, width and volume) and on canopy structure (i.e. light penetrability, leafiness and porosity) may result in better orchard management decisions. Easily obtainable, reliable information on canopy geometry and structure may favour the development of decision support systems either for irrigation, fertilization or canopy management, as well as for variable rate application of agricultural inputs in the framework of precision fruticulture/horticulture.

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Section: Introductionmentioning

confidence: 99%

Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds

et al. 2016

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“…The second research line was characterized by the development of high-resolution 3D modeling by MTLS including aspects of the data acquisition, data processing, and estimation of different tree parameters. 3D modeling has also been the focus of other technologies that were not covered in the present review, but are worth mentioning due to increasing interest by researchers: (i) stereophotogrammetry approaches based on the use of digital RGB cameras and/or multispectral cameras 84,85 , and (ii) active systems based on the use of Depth cameras (also called RGB-D cameras), such as Microsoft’s Kinect 86,87 . The main advantage of LiDAR sensors over passive sensors to collect depth information is the fact that its electromagnetic signal (the laser beam) can penetrate the vegetation canopy, and hence giving information of canopy density and of the inner structures of the trees1.…”

Section: Discussion Future Challenges and Perspectivesmentioning

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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“…The Microsoft Kinect sensor (hereafter Kinect) is probably the most popular and representative model of the recently developed low-cost color-depth (RGB-D) cameras, and for this reason, it is being widely used in a broad range of technological and scientific applications by R&D actors [10]- [13]. Designed to be used as a home video game complement, Microsoft's Kinect was thought to be used in indoor environments and particularly with low illuminance levels.…”

Section: Introductionmentioning

confidence: 99%

“…Authors Commercially available low-cost RGB-D sensors (hereinafter, we will refer only to the Kinect sensor) can be an interesting alternative to LiDAR-based MTLS since high accuracy may not be critical in certain applications [18]- [20]. Moreover, these sensors can provide additional information, such as color and infrared, for each point of the cloud [13], [21], [22]. Kinect measurements in static mode, i.e., without movement, are similar to those undertaken by a stationary video camera, but the Kinect also provides distance data, allowing 3D point clouds to be obtained.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, a second program, based on the theoretical basis explained in section II, transforms raw data files into UTM georeferenced 3D point clouds with x, y, z, RGB, and infrared (IR) information for each point of the scanned object. Due to the known limitation of Kinect sensors, outdoor illuminance levels must be kept low enough to allow high quality measurements[13], making nocturne artificially lighted measurements an alternative option.Table Isummarizes the main characteristics of the developed K2-MTLS system.…”

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Kinect v2 Sensor-Based Mobile Terrestrial Laser Scanner for Agricultural Outdoor Applications

Rosell-Polo

Gregorio

Gené

et al. 2017

IEEE/ASME Trans. Mechatron.

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Mobile terrestrial laser scanners (MTLS), based on light detection and ranging (LiDAR) sensors, are used worldwide in agricultural applications. MTLS are applied to characterize the geometry and the structure of plants and crops for technical and scientific purposes. Although MTLS exhibit outstanding performance, their high cost is still a drawback for most agricultural applications. This paper presents a low-cost alternative to MTLS based on the combination of a Kinect v2 depth sensor and a Real Time Kinematic (RTK) global navigation satellite system (GNSS) with extended color information capability. The theoretical foundations of this system are exposed along with some experimental results illustrating their performance and limitations. This work is focused on open-field agricultural applications, although most conclusions can also be extrapolated to similar outdoor uses. The developed Kinect-based MTLS (K2-MTLS) system allows to select different acquisition frequencies and fields of view (FOV), from one to 512 vertical slices. The authors conclude that the better performance is obtained when a FOV of a single slice is used, but at the price of a very low measuring speed. With that particular configuration, plants, crops, and objects are reproduced accurately. Future efforts will be directed to increase the scanning efficiency by improving both the hardware and software components and to make it feasible using both partial and full FOV.Index Terms-Depth cameras, Kinect v2, LiDAR, RGB-D cameras, mobile terrestrial laser scanner (MTLS), precision agriculture, precision fruticulture, precision horticulture.

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Advances in Structured Light Sensors Applications in Precision Agriculture and Livestock Farming

Cited by 65 publications

References 57 publications

Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds

Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds

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

Kinect v2 Sensor-Based Mobile Terrestrial Laser Scanner for Agricultural Outdoor Applications

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