A fruit-tracking system for robotic harvesting

Harrell, R. C.; Slaughter, David C.; Adsit, Phillip D.

doi:10.1007/bf01212369

Cited by 72 publications

(33 citation statements)

References 2 publications

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“…The type of feature is highly dependent on the working environment that the system is designed for [8]. For instance, the centroid and diameter of circles are used by Harrell et al [9], for a fruit tracking robot for harvesting. Rives and Borrelly [10] employ edge features to track pipes with an underwater robot.…”

Section: A Previous Workmentioning

confidence: 99%

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Vision-based 3-D trajectory tracking for unknown environments

Saeedi¹,

Lawrence

Lowe

2006

IEEE Trans. Robot.

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Section: A Previous Workmentioning

confidence: 99%

“…If the intensity variation is small, then is not a corner otherwise it is announced as a corner. That is if: (9) where represents the brightness variation threshold, a corner is detected. Figure 1 displays the output of the proposed method on one of the sample outdoor images.…”

Section: B Binary Corner Detection (Bcd)mentioning

confidence: 99%

Vision-based 3-D trajectory tracking for unknown environments

Saeedi¹,

Lawrence

Lowe

2006

IEEE Trans. Robot.

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“…Mancuso (1999) highlighted the importance of the leaf fractal geometry for fingerprinting plants. In addition, leaf colour information provides useful data for judging maturity of agricultural products (Gunasekaran et al, 1985), detecting diseases (Howaith et al, 1990), and fruit sorting (Harrell et al, 1989). Thus, the leaves really provide plenty of characteristics that can be used as a source of data for plant taxonomy (Yang and Lin, 2005).…”

Section: Introductionmentioning

confidence: 99%

<i>Camellia</i> (Theaceae) classification with support vector machines based on fractal parameters and Red, Green, and Blue intensity of leaves

Zhang

et al. 2017

Bangladesh J. Plant Taxon

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Leaf traits are commonly used in plant taxonomic applications. The aim of this study was to test the utility of fractal leaf parameters analysis (FA) and leaf red, green, and blue (RGB) intensity values based on support vector machines as a method for accurately discriminating Camellia (68 species from five sections, 11 from sect. Furfuracea, 13 from sect. Paracamellia, 15 from sect. Tuberculata, 24 from sect. Theopsis and 5 from sect. Camellia). The results showed that the best classification accuracy was up to 96.88% using the RBF SVM classifier (C = 16, g = 0.5). The linear kernel overall accuracy was 90.63%, and the correct classification rates of 40.63% and 93.75% were achieved for the sigmoid SVM classifier (C = 16, g = 0.5) and the polynomial SVM classifier (C = 16, g = 0.5, d = 2), respectively. A hierarchical dendrogram based on leaf FA and RGB intensity values was mostly on agreement with the generally accepted classification of the Camellia species. SVM combined with FA and RGB may be used for rapidly and accurately classifying Camellia species and identifying unknown genotypes.

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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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A fruit-tracking system for robotic harvesting

Cited by 72 publications

References 2 publications

Vision-based 3-D trajectory tracking for unknown environments

Vision-based 3-D trajectory tracking for unknown environments

<i>Camellia</i> (Theaceae) classification with support vector machines based on fractal parameters and Red, Green, and Blue intensity of leaves

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

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