Machine-Vision Systems Selection for Agricultural Vehicles: A Guide

Pajares, Gonzalo; García-Santillán, Iván; Campos, Yerania; Montalvo, M.; Guerrero, J. M.; Emmi, Luis; Romeo, J.; Guijarro, María; Santos, P. González de

doi:10.3390/jimaging2040034

Cited by 46 publications

(28 citation statements)

References 59 publications

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“…The biospeckle activity was measured using two different light sources. It is a well-known fact that near infrared is a useful band for plant identification and phenotyping due to chlorophyll activity and absorption causing high reflectance in the NIR band [ 50 ]. Based on this the 830 nm LDM 830 laser (MONOCROM, Barcelona, Spain) with 30 mW of output power was chosen as the first light source.…”

Section: Methodsmentioning

confidence: 99%

Postharvest Monitoring of Tomato Ripening Using the Dynamic Laser Speckle

Pieczywek

Nowacka

Dadan

et al. 2018

Sensors

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The dynamic laser speckle (biospeckle) method was tested as a potential tool for the assessment and monitoring of the maturity stage of tomatoes. Two tomato cultivars—Admiro and Starbuck—were tested. The process of climacteric maturation of tomatoes was monitored during a shelf life storage experiment. The biospeckle phenomena were captured using 640 nm and 830 nm laser light wavelength, and analysed using two activity descriptors based on biospeckle pattern decorrelation—C4 and ε. The well-established optical parameters of tomatoes skin were used as a reference method (luminosity, a*/b*, chroma). Both methods were tested with respect to their prediction capabilities of the maturity and destructive indicators of tomatoes—firmness, chlorophyll and carotenoids content. The statistical significance of the tested relationships were investigated by means of linear regression models. The climacteric maturation of tomato fruit was associated with an increase in biospckle activity. Compared to the 830 nm laser wavelength the biospeckle activity measured at 640 nm enabled more accurate predictions of firmness, chlorophyll and carotenoids content. At 640 nm laser wavelength both activity descriptors (C4 and ε) provided similar results, while at 830 nm the ε showed slightly better performance. The linear regression models showed that biospeckle activity descriptors had a higher correlation with chlorophyll and carotenoids content than the a*/b* ratio and luminosity. The results for chroma were comparable with the results for both biospeckle activity indicators. The biospeckle method showed very good results in terms of maturation monitoring and the prediction of the maturity indices of tomatoes, proving the possibility of practical implementation of this method for the determination of the maturity stage of tomatoes.

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Section: Methodsmentioning

confidence: 99%

Postharvest Monitoring of Tomato Ripening Using the Dynamic Laser Speckle

Pieczywek

Nowacka

Dadan

et al. 2018

Sensors

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“…Stereo cameras need textures in the images to find matching pixels in the two camera images, which brings a lack of distance measurements since matching is not achieved. However, if a perfect stereo camera is considered, the depth accuracy ∆Z could be described with [41]:…”

Section: Calibration and System Testmentioning

confidence: 99%

“…with p as the pixel size and f as the focal length of the camera [41]. For comparing the two sensor principles, two different cases were considered.…”

Section: Calibration and System Testmentioning

confidence: 99%

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3D Imaging with a Sonar Sensor and an Automated 3-Axes Frame for Selective Spraying in Controlled Conditions

et al. 2017

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Autonomous selective spraying could be a way for agriculture to reduce production costs, save resources, protect the environment and help to fulfill specific pesticide regulations. The objective of this paper was to investigate the use of a low-cost sonar sensor for autonomous selective spraying of single plants. For this, a belt driven autonomous robot was used with an attached 3-axes frame with three degrees of freedom. In the tool center point (TCP) of the 3-axes frame, a sonar sensor and a spray valve were attached to create a point cloud representation of the surface, detect plants in the area and perform selective spraying. The autonomous robot was tested on replicates of artificial crop plants. The location of each plant was identified out of the acquired point cloud with the help of Euclidian clustering. The gained plant positions were spatially transformed from the coordinates of the sonar sensor to the valve location to determine the exact irrigation points. The results showed that the robot was able to automatically detect the position of each plant with an accuracy of 2.7 cm and could spray on these selected points. This selective spraying reduced the used liquid by 72%, when comparing it to a conventional spraying method in the same conditions.

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“…Since the camera was set parallel to the ground, the relationship between the two-dimensional image and the three-dimensional scene [21,22] can be expressed using a simple perspective transformation expressed by the following equation:…”

Section: Performance Evaluation Testsmentioning

confidence: 99%

Comparison of Small Unmanned Aerial Vehicles Performance Using Image Processing

et al. 2017

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Precision agriculture is a farm management technology that involves sensing and then responding to the observed variability in the field. Remote sensing is one of the tools of precision agriculture. The emergence of small unmanned aerial vehicles (sUAV) have paved the way to accessible remote sensing tools for farmers. This paper describes the development of an image processing approach to compare two popular off-the-shelf sUAVs: 3DR Iris+ and DJI Phantom 2. Both units are equipped with a camera gimbal attached with a GoPro camera. The comparison of the two sUAV involves a hovering test and a rectilinear motion test. In the hovering test, the sUAV was allowed to hover over a known object and images were taken every quarter of a second for two minutes. For the image processing evaluation, the position of the object in the images was measured and this was used to assess the stability of the sUAV while hovering. In the rectilinear test, the sUAV was allowed to follow a straight path and images of a lined track were acquired. The lines on the images were then measured on how accurate the sUAV followed the path. The hovering test results show that the 3DR Iris+ had a maximum position deviation of 0.64 m (0.126 m root mean square RMS displacement) while the DJI Phantom 2 had a maximum deviation of 0.79 m (0.150 m RMS displacement). In the rectilinear motion test, the maximum displacement for the 3DR Iris+ and the DJI phantom 2 were 0.85 m (0.134 m RMS displacement) and 0.73 m (0.372 m RMS displacement). These results demonstrated that the two sUAVs performed well in both the hovering test and the rectilinear motion test and thus demonstrated that both sUAVs can be used for civilian applications such as agricultural monitoring. The results also showed that the developed image processing approach can be used to evaluate performance of a sUAV and has the potential to be used as another feedback control parameter for autonomous navigation.

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Machine-Vision Systems Selection for Agricultural Vehicles: A Guide

Cited by 46 publications

References 59 publications

Postharvest Monitoring of Tomato Ripening Using the Dynamic Laser Speckle

Postharvest Monitoring of Tomato Ripening Using the Dynamic Laser Speckle

3D Imaging with a Sonar Sensor and an Automated 3-Axes Frame for Selective Spraying in Controlled Conditions

Comparison of Small Unmanned Aerial Vehicles Performance Using Image Processing

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