Optimal Thresholding for the Automatic Recognition of Apple Fruits

Bulanon, D M; Kataoka, Takao; Zhang, S; Ota, Y.; Hiroma, Tatsuo

doi:10.13031/2013.3672

Cited by 18 publications

(13 citation statements)

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“…In order to vanish with the objects below certain size, Blob size filter [6] can be applied. The Threshold histogram visualized in the Blob Size interface is used to threshold the picture into object/no object values.…”

Section: Blob Sizementioning

confidence: 99%

Object Detection and Displacement Integrated with RoborealM

Sathish¹,

Bhaskari²,

Harini³

2015

May 13-14, 2015 Kuala Lumpur, Malaysia

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Section: Blob Sizementioning

confidence: 99%

Object Detection and Displacement Integrated with RoborealM

Sathish¹,

Bhaskari²,

Harini³

2015

May 13-14, 2015 Kuala Lumpur, Malaysia

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“…The sensors employed in most previous studies are standard BW or colour CCD cameras, typically positioned on the body of the robot or the main platform to provide a single view on the scene being analysed (Bulanon et al, 2001(Bulanon et al, , 2002Bulanon and Kataoka, 2010;Okamoto and Lee, 2010;Zhao et al, 2005). Less frequent are systems with a camera placed on the robot's end effector (Hayashi et al, 2002;Ling et al, 2004) while in still fewer cases the system employs both types of cameras to enjoy global view of the scene and a gripper-centred close-up view on specific targets (Edan et al, 2000;Feng et al, 2008;Van Henten et al, 2002;Yuan et al, 2010).…”

Section: Single Cameramentioning

confidence: 99%

Computer vision for fruit harvesting robots state of the art and challenges ahead

Kapach¹,

Barnea²,

Mairon³

et al. 2012

IJCVR

198

128

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Despite extensive research conducted in machine vision for harvesting robots, practical success in this field of agrobotics is still limited. This article presents a comprehensive review of classical and state-of-the-art machine vision solutions employed in such systems, with special emphasis on the visual cues and machine vision algorithms used. We discuss the advantages and limitations of each approach and we examine these capacities in light of the challenges ahead. We conclude with suggested directions from the general computer vision literature which could assist our research community meet these challenges and bring us closer to the goal of practical selective fruit harvesting robots.

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“…The most natural property to be used for such purpose is the color. In the past, color has been extensively used [2], [3], [4], [5], [6], [7], [8], [9]. Color on is own may not provide enough information as it may change depending upon how ripe the fruit is.…”

Section: Introductionmentioning

confidence: 99%

Detection and Counting of On-Tree Citrus Fruit for Crop Yield Estimation

Malik¹,

Ziauddin²,

Raza³

et al. 2016

ijacsa

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Abstract-In this paper, we present a technique to estimate citrus fruit yield from the tree images. Manually counting the fruit for yield estimation for marketing and other managerial tasks is time consuming and requires human resources, which do not always come cheap. Different approaches have been used for the said purpose, yet separation of fruit from its background poses challenges, and renders the exercise inaccurate. In this paper, we use k-means segmentation for recognition of fruit, which segments the image accurately thus enabling more accurate yield estimation. We created a dataset containing 83 tree images with 4001 citrus fruits from three different fields. We are able to detect the on-tree fruits with an accuracy of 91.3%. In addition, we find a strong correlation between the manual and the automated fruit count by getting coefficients of determination R 2 up to 0.99.

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Optimal Thresholding for the Automatic Recognition of Apple Fruits

Cited by 18 publications

References 0 publications

Object Detection and Displacement Integrated with RoborealM

Object Detection and Displacement Integrated with RoborealM

Computer vision for fruit harvesting robots state of the art and challenges ahead

Detection and Counting of On-Tree Citrus Fruit for Crop Yield Estimation

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Optimal Thresholding for the Automatic Recognition of Apple Fruits

Cited by 18 publications

References 0 publications

Object Detection and Displacement Integrated with RoborealM

Object Detection and Displacement Integrated with RoborealM

Computer vision for fruit harvesting robots  state of the art and challenges ahead

Detection and Counting of On-Tree Citrus Fruit for Crop Yield Estimation

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Product

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Computer vision for fruit harvesting robots state of the art and challenges ahead