Study on Fruit Visibility for Robotic Harvesting

Bulanon, D M; Burks, Thomas F.; Alchanatis, V.

doi:10.13031/2013.23428

Cited by 6 publications

(5 citation statements)

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“…There are two problems that can affect the success of fruit detection: lightning and occlusion. Bulanon [5], Tabb [6], and Kondo [16] found that lightning could be a significant problem by affecting filtering. The occlusion minimizes the visible fruit area and disrupts the affected fruit shape [25].…”

Section: Orange Count With K-means Algorithmmentioning

confidence: 99%

“…The mean diameters proportioned by the algorithm (72.15 ± 22.62 mm) did not present significant differences (p = 0.35) with the ones measured on fruits (72.68 ± 5.728 mm). Even though the use of 3D LiDAR scans is time-consuming, the harvest size estimation obtained in this research is very accurate.In the literature, diverse works of detection in different types of fruits or harvest can be found, such as almond [2], apple [3][4][5][6][7], cherryfruit [8], cucumber [9], mango [10,11], orange [12,13], pineapple [14,15], or tomato [16].Fruit detection requires segmentation, shape selection, and identification phases [17]. Segmentation consists of filtering through a colour threshold of the components of the scene that can be considered fruit [18].…”

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confidence: 99%

“…In the literature, diverse works of detection in different types of fruits or harvest can be found, such as almond [2], apple [3][4][5][6][7], cherryfruit [8], cucumber [9], mango [10,11], orange [12,13], pineapple [14,15], or tomato [16].…”

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In-Field Estimation of Orange Number and Size by 3D Laser Scanning

et al. 2019

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The estimation of fruit load of an orchard prior to harvest is useful for planning harvest logistics and trading decisions. The manual fruit counting and the determination of the harvesting capacity of the field results are expensive and time-consuming. The automatic counting of fruits and their geometry characterization with 3D LiDAR models can be an interesting alternative. Field research has been conducted in the province of Cordoba (Southern Spain) on 24 'Salustiana' variety orange trees-Citrus sinensis (L.) Osbeck-(12 were pruned and 12 unpruned). Harvest size and the number of each fruit were registered. Likewise, the unitary weight of the fruits and their diameter were determined (N = 160). The orange trees were also modelled with 3D LiDAR with colour capture for their subsequent segmentation and fruit detection by using a K-means algorithm. In the case of pruned trees, a significant regression was obtained between the real and modelled fruit number (R 2 = 0.63, p = 0.01). The opposite case occurred in the unpruned ones (p = 0.18) due to a leaf occlusion problem. The mean diameters proportioned by the algorithm (72.15 ± 22.62 mm) did not present significant differences (p = 0.35) with the ones measured on fruits (72.68 ± 5.728 mm). Even though the use of 3D LiDAR scans is time-consuming, the harvest size estimation obtained in this research is very accurate.In the literature, diverse works of detection in different types of fruits or harvest can be found, such as almond [2], apple [3][4][5][6][7], cherryfruit [8], cucumber [9], mango [10,11], orange [12,13], pineapple [14,15], or tomato [16].Fruit detection requires segmentation, shape selection, and identification phases [17]. Segmentation consists of filtering through a colour threshold of the components of the scene that can be considered fruit [18]. Different characteristics of perimeter, area, or compaction allow to select the shapes (blobs) and to identify the fruits one by one.In bidimensional models, circles can be detected by the Hough transform [19] or by adjusting circular contours [20]. The colour cameras allowed Harrell et al. [12] to implement a robotic orange harvesting system and Grasso and Recce [21] to perform RGB segmentation. Qureshi et al.[11] used clustering of K-nearest neighbour pixels. Qureshi et al. [22] presented a texture-based method for shape recognition using an over-segmentation of super-pixels from the local gradient calculation.Colour segmentation using charge coupled device cameras returns pixels with RGB graduations or colour composition by addition of the primary colours red, green, and blue, allowing rapid detection of ripe fruit [23]. However, its drawback is its false positives. In addition, shape detection has a high computational cost. Therefore, a suitable alternative is the use of a colour filter followed by shape detection to avoid these false positives.The colour and shape characteristics allow us to approach its count using 2D photos, filtering by colour or chromaticity, delimiting shapes by contour, and being...

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Section: Orange Count With K-means Algorithmmentioning

confidence: 99%

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confidence: 99%

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In-Field Estimation of Orange Number and Size by 3D Laser Scanning

et al. 2019

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“…Robot teams can be used to distribute the sensing load and provide multiple independent views of the crops. For example, fruit visibility for citrus trees has been reported to lie in the range of 40-70% depending on the tree and viewpoint (134) but rose to 91% when combining visible fruit from multiple-perspective images (135). A complementary approach is to utilize biology (breeding) and horticultural practices such as tree training or leaf thinning to simplify canopy structures and improve visibility.…”

Section: Challenges and Possible Directionsmentioning

confidence: 99%

Agricultural Robotics

Vougioukas

2019

Annu. Rev. Control Robot. Auton. Syst.

107

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A key goal of contemporary agriculture is to dramatically increase production of food, feed, fiber, and biofuel products in a sustainable fashion, facing the pressure of diminishing farm labor supply. Agricultural robots can accelerate plant breeding and advance data-driven precision farming with significantly reduced labor inputs by providing task-appropriate sensing and actuation at fine spatiotemporal resolutions. This article highlights the distinctive challenges imposed on ground robots by agricultural environments, which are characterized by wide variations in environmental conditions, diversity and complexity of plant canopy structures, and intraspecies biological variation of physical and chemical characteristics and responses to the environment. Existing approaches to address these challenges are presented, along with their limitations; possible future directions are also discussed. Two key observations are that biology (breeding) and horticultural practices can reduce variabilities at the source and that publicly available benchmark data sets are needed to increase perception robustness and performance despite variability.

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“…This will influence the segmentation process and the detection of the fruit with occlusion, especially with clustered fruits. The set of images used for this test is part of the fruit visibility study (Bulanon et al, 2007). A volume of the tree was bounded with a cube (0.5 m).…”

Section: C) Far-view Imagesmentioning

confidence: 99%

A Real-time Machine Vision Algorithm for Robotic Citrus Harvesting

Hannan¹,

Burks²,

Bulanon³

et al. 2007

2007 Minneapolis, Minnesota, June 17-20, 2007

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Study on Fruit Visibility for Robotic Harvesting

Cited by 6 publications

References 0 publications

In-Field Estimation of Orange Number and Size by 3D Laser Scanning

In-Field Estimation of Orange Number and Size by 3D Laser Scanning

Agricultural Robotics

A Real-time Machine Vision Algorithm for Robotic Citrus Harvesting

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