Application of the Kinect sensor for three dimensional characterization of vine canopy

Marinello, Francesco; Pezzuolo, Andrea; Meggio, Franco; Martínez‐Casasnovas, José A.; Yezekyan, Tatevik; Sartori, Luigi

doi:10.1017/s2040470017001042

Cited by 10 publications

(8 citation statements)

References 12 publications

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“…Four body dimensions of Holstein cows (withers height, hip height, body length, and hip width) have been determined for predicting live weight by a fuzzy rule-based color imaging system [ 39 ], which have been deployed four different directions of Cannon cameras after calibration [ 40 ]. For continuous 3D body reconstruction of calves, young and adult cows, an automated measuring system with Kinect cameras has been validated for live weight estimation of each cow, and five body dimensions of cows (hip and withers height, hips distance, head size, chest girth) have been measured with different estimation coefficients [ 41 ]. Certain methods for live weight measuring for cattle have been achieved and referred to body dimensions, but no clear specific patterns and methods for the standing body dimensions of live cattle could be observed.…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Body Measurement for Qinchuan Cattle with LiDAR Sensor

Huang

Zhu

et al. 2018

Sensors

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The body dimension measurement of large animals plays a significant role in quality improvement and genetic breeding, and the non-contact measurements by computer vision-based remote sensing could represent great progress in the case of dangerous stress responses and time-costing manual measurements. This paper presents a novel approach for three-dimensional digital modeling of live adult Qinchuan cattle for body size measurement. On the basis of capturing the original point data series of live cattle by a Light Detection and Ranging (LiDAR) sensor, the conditional, statistical outliers and voxel grid filtering methods are fused to cancel the background and outliers. After the segmentation of K-means clustering extraction and the RANdom SAmple Consensus (RANSAC) algorithm, the Fast Point Feature Histogram (FPFH) is put forward to get the cattle data automatically. The cattle surface is reconstructed to get the 3D cattle model using fast Iterative Closest Point (ICP) matching with Bi-directional Random K-D Trees and a Greedy Projection Triangulation (GPT) reconstruction method by which the feature points of cattle silhouettes could be clicked and calculated. Finally, the five body parameters (withers height, chest depth, back height, body length, and waist height) are measured in the field and verified within an accuracy of 2 mm and an error close to 2%. The experimental results show that this approach could be considered as a new feasible method towards the non-contact body measurement for large physique livestock.

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Section: Introductionmentioning

confidence: 99%

Non-Contact Body Measurement for Qinchuan Cattle with LiDAR Sensor

Huang

Zhu

et al. 2018

Sensors

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“…Preliminary tests to estimate the grape mass using a Kinect sensor are presented in Marinello et al (2017a). An application of the Kinect sensor for three-dimensional characterization of vine canopy is also presented in Marinello et al (2017b), using the sensor statically positioned in the vineyard. Although recent developments of Kinect have led to higher robustness to artificial illumination and sunlight, some filters need to be applied to overcome the increased noise effects and to reach exploitable results (Rosell-Polo et al, 2015), therefore the application of this sensor remains mostly limited to indoor contexts and for close range monitoring (Lachat et al, 2015;Zen-naro et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

In-field high throughput grapevine phenotyping with a consumer-grade depth camera

Milella

Marani

Petitti

et al. 2019

Computers and Electronics in Agriculture

121

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Plant phenotyping, that is, the quantitative assessment of plant traits including growth, morphology, physiology, and yield, is a critical aspect towards efficient and effective crop management. Currently, plant phenotyping is a manually intensive and time consuming process, which involves human operators making measurements in the field, based on visual estimates or using hand-held devices. In this work, methods for automated grapevine phenotyping are developed, aiming to canopy volume estimation and bunch detection and counting. It is demonstrated that both measurements can be effectively performed in the field using a consumer-grade depth camera mounted onboard an agricultural vehicle. First, a dense 3D map of the grapevine row, augmented with its color appearance, is generated, based on infrared stereo reconstruction. Then, different computational geometry methods are applied and evaluated for plant per plant volume estimation. The proposed methods are validated through field tests performed in a commercial vineyard in Switzerland. It is shown that different automatic methods lead to different canopy volume estimates meaning that new standard methods and proce-

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“…The collected data were correlated with the results arising from manual measurements on volume and leaf area, carried out on the same vine portions collected through the new proposed optical approach. Manual measurements of the volume and leaf area were done applying the same procedure described in [4]. For the volume, widths at three different heights were measured considering as a reference surface the virtually flat plane passing through the trellising supporting the plants.…”

Section: A %mentioning

confidence: 99%

Characterization of vine canopy through two dimensional imaging

Marinello¹,

Toniolo²,

Yezekian³

et al. 2017

Engineering for Rural Development

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Abstract. In the last decade, reconstruction of plant dimensions and volume has gained a noticeable importance, in particular for the possibility of collecting data correlated to biomass, leaves area, etc. This is specifically of interest in the case of vineyards, where knowledge of variability can be not only a useful mean to evaluate the health condition of the vines and of the grapes, but also an important input to allow variable management practices. Different sensing technologies are available for shape reconstruction, as for instance ultrasonic sensors, laser scanners or depth cameras, however, their practical application is still limited by low quality information, high costs, or high speed data processing demand. For the present work two-dimensional imaging is proposed as a viable solution for shape reconstruction of canopies. The method takes advantage of low cost 2D commercial cameras, which can be installed on board the tractor allowing on the go collection of images of plants from the bottom. The paper describes the instrumentation set up and integration with a specific thresholding algorithm allowing segmentation of canopy profile. Tests were carried out on 20 different dates on a glera vineyard in an experimental farm in the North of Italy. The results were correlated with the number of leaves, leaf area index and canopy volume. High correlation was identified in the case of volume with coefficients of determination R² > 0.7 in most of the cases.Keywords: canopy, profile, imaging, segmentation. IntroductionThe opportunity of analyzing the variable profile of vineyard canopies is of the highest relevance for the possibility of precisely quantifying local biomass. Such interest arises from the correlation, which is typically found between the canopy size and plant needs in terms of water and nutrients as well as with the yield and quality [1]. As a consequence, knowledge of plant dimensions allows not only evaluation of the plant health conditions, but also exploitation of variable rate and management practices, including crop protection and canopy management [1][2][3].Many researches and approaches have been proposed in the last decade, which allow quantification of the plant dimensions [1; 3; 4], also taking advantage of the availability of miniaturized low cost sensors and of an increased speed in data processing [5; 6]. Available technologies can be referred mainly to ultrasonics, laser sensors and other optical methods [7].Ultrasonic technology is a low cost solution, which relies on the sound waves echoing off of a given target [8; 9] to provide its external contour. Its performance is influenced by ultrasound propagation, which often exhibits parasitic signals or losses especially in the cases of thin targets or high reflection angles, as is in the case of small branches or in the case of badly oriented leaves. Additionally, ultrasonic sensors are very much sensitive to humidity, dust and high temperatures, however, their practical implementation has provided interesting results especially cou...

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Application of the Kinect sensor for three dimensional characterization of vine canopy

Cited by 10 publications

References 12 publications

Non-Contact Body Measurement for Qinchuan Cattle with LiDAR Sensor

Non-Contact Body Measurement for Qinchuan Cattle with LiDAR Sensor

In-field high throughput grapevine phenotyping with a consumer-grade depth camera

Characterization of vine canopy through two dimensional imaging

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