Vision systems for the location of citrus fruit in a tree canopy

Moltó, Enrique; Pla, Filiberto; Juste, F.

doi:10.1016/0021-8634(92)80053-u

Cited by 27 publications

(12 citation statements)

References 4 publications

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“…Since a robotic system for citrus harvesting was developed in the mid-1980s (Harrell et al 1985), more efforts have been made to improve robotic citrus harvesting. Moltó et al (1992) utilised the differences in the reflectance spectrum of the fruit and the leaves to locate the fruits on the trees. In the same work, they studied the reflectance of the light on the fruit surface due to the spherical shape of the fruits when they are illuminated using a flash.…”

Section: Robotics For Citrus Harvestingmentioning

confidence: 99%

“…Table 1 summarises the different works carried out for the application of computer vision in the citrus inspection in the field ordered by different topics chronologically. Table 1 Reference Achievement Robotics for citrus harvesting Harrell et al (1985) One of the first applications for harvesting Moltó et al (1992) Utilised the differences in the reflectance spectrum and reflection patterns to locate the fruit in the trees Burks et al (2003) Reported that field conditions, plant population and spacing, and plant shape and size were the most important factors for mechanical harvesting in the horticultural aspect Flood et al (2006) Studied a maximum value for an end-effector grasping force for harvesting Subramanian et al (2006) Developed an autonomous guidance system for citrus grove navigation using machine vision and laser radar Hannan et al (2009) Developed a machine vision algorithm based on red chromaticity coefficient to identify oranges for robotic harvesting. Mehta and Burks (2014) Developed a vision-based fruit depth estimation and robotic harvesting system using in-depth visual servo control formulation.…”

Section: Inspection Of Fruit In the Field Using Mobile Platformsmentioning

confidence: 99%

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Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review

Cubero

Lee

Albert

et al. 2016

Food Bioprocess Technol

107

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Computer vision systems are becoming a scientific but also a commercial tool for food quality assessment. In the field, these systems can be used to predict yield, as well as for robotic harvesting or the early detection of potentially dangerous diseases. In postharvest handling, it is mostly used for the automated inspection of the external quality of the fruits and for sorting them into commercial categories at very high speed. More recently, the use of hyperspectral imaging is allowing not only the detection of defects in the skin of the fruits but also their association to certain diseases of particular importance. In the research works that use this technology, wavelengths that play a significant role in detecting some of these dangerous diseases are found, leading to the development of multispectral imaging systems that can be used in industry. This article reviews recent works that use colour and non-standard computer vision systems for the automated inspection of citrus. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of internal and external features of these fruits. Particular attention is paid to inspection for the early detection of some dangerous diseases like citrus canker, black spot, decay or citrus Huanglongbing.

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Section: Robotics For Citrus Harvestingmentioning

confidence: 99%

Section: Inspection Of Fruit In the Field Using Mobile Platformsmentioning

confidence: 99%

Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review

Cubero

Lee

Albert

et al. 2016

Food Bioprocess Technol

107

View full text Add to dashboard Cite

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“…The use of robots in agriculture has been researched extensively for at least two decades, and technical feasibility has been demonstrated for a variety of agricultural tasks such as automatic guidance of agricultural field and greenhouse operations (e.g., Keicher & Seufert, 2000;Pilarski et al, 1999Pilarski et al, , 2002Reid, 2004;Reid, Zhang, Noguchi, & Dickson, 2000;Torii, 2000;Wilson, 2000), fruit selective harvesting (citrus: Fujiura, Ura, Kawamura, & Namikawa, 1990; Hannan & Burks, 2004;Juste & Fornes, 1990;Molto, Pla, & Juste, 1992;Rabatel, Bourely, Sevila, & Juste, 1995;apples: Grand d'Esnon, 1985;Kassay, 1992;grapes: Kondo, 1995;Monta, Kondo, Shibano, & Mohri, 1994;Sittichareonchai & Sevila, 1989;cucumbers: Arima, Kondo, Shibano, Fujiura et al, 1994;melons: Iida, Furube, Namikawa, & Umeda, 1996;radicchio: Maio & Reina, 2006) and seedling production Kondo, Monta, & Ogawa, 1997;Kondo & Ting, 1998;Simonton, 1990). Studies focused on object detection in natural environments, gripper and manipulator design, and autonomous guidance (Edan, 1999).…”

Section: Introductionmentioning

confidence: 99%

Toward elevated agrobotics: Development of a scaled‐down prototype for visually guided date palm tree sprayer

Shapiro¹,

Korkidi²,

Demri³

et al. 2009

Journal of Field Robotics

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Agricultural operations at great heights are typically laborious, expensive, and dangerous for human workers. Spraying and pollinating date palm trees, for instance, is currently done manually by a team of several workers from a platform lifted 18 m or more above the ground. This method is extremely unsafe, and many accidents have occurred due to instability when the platform is in a lifted position. In this paper we present the concept of an autonomous field robot that will effectively and accurately spray and pollinate date clusters. A scaled-down prototype has been designed and consists of a visually controlled robotic arm that guides the jet of a mounted sprayer directly to the date clusters completely autonomously and from a short distance. Rather than requiring an expensive dedicated platform, this robotic apparatus can be towed by a standard tractor operated by a single driver, with no human worker operating in the heights. This system can minimize risk of injury, significantly save manpower, and deliver the spray with maximum accuracy, thereby reducing chemical disposal. The spraying guidance system is based on a proportional controller that uses feedback from an image processing system combined with a small dead band. The system was modeled mathematically, and the effect of each component on overall performance was evaluated by simulation. Results were used for tuning the experimental system controller parameters. Experiments were performed to evaluate the tracking performance of the visually guided tracking system on a single tree and a 10-m-long runway at a distance of 6 m. These dimensions were inspired by the conditions Shapiro et al.: Development of a Prototype for Visually Guided Date Palm Tree Sprayer • 573at a typical orchard. During the experiment the sprayer was towed along the runway 19 times, during which the speed of the wagon was varied between 0.7 km/h (0.2 m/s) and 12 km/h (3.3 m/s). Experimental results indicate that up to a wagon ve1ocity of 1.25 m/s the tracking error was reasonably low and stayed below 10 deg from the center of the target (typical date spraying speed is 1.1 m/s). However, at higher speeds the tracking quality reduced progressively and some drift (i.e., accumulated error) was noticed in the pan axis due to image processing speed. The simulations and experiments with a scaleddown prototype show feasibility of the presented method and demonstrate how this new approach facilitates more efficient high-altitude agricultural robotics. C 2009 Wiley Periodicals, Inc.

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“…For this reason several projects are being carried out worldwide (Juste and Sevila 1991;Harrell 1987;Molto et al 1992;Sarig 1993;Edan 1995;Kondo et al 1995).…”

Section: Introductionmentioning

confidence: 99%

Localization of spherical fruits for robotic harvesting

Plebe

Grasso

2001

Machine Vision and Applications

113

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The orange picking robot (OPR) is a project for developing a robot that is able to harvest oranges automatically. One of the key tasks in this robotic application is to identify the fruit and to measure its location in three dimensions. This should be performed using image processing techniques which must be sufficiently robust to cope with variations in lighting conditions and a changing environment. This paper describes the image processing system developed so far to guide automatic harvesting of oranges, which here has been integrated in the first complete full-scale prototype OPR.

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Vision systems for the location of citrus fruit in a tree canopy

Cited by 27 publications

References 4 publications

Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review

Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review

Toward elevated agrobotics: Development of a scaled‐down prototype for visually guided date palm tree sprayer

Localization of spherical fruits for robotic harvesting

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