A fruit recognition method for automatic harvesting

Yang, Lynn; Dickinson, John R.; Wu, Qi; Lang, Stefan

doi:10.1109/mmvip.2007.4430734

Cited by 26 publications

(14 citation statements)

References 5 publications

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“…Numerous research efforts have been reported in the literature on the development of a machine vision system for image acquisition, fruit detection, and fruit localization for robotic harvesting of fruits (Parish and Goksel, 1977;Harrell et al, 1985;Yang et al, 2007;Baeten et al, 2008;Scarfe et al, 2009;Yamamoto et al, 2014). However, proper synthesization of the literature to provide a clear guidance on the state-of-the-art and potential future direction has been lacking.…”

Section: Introductionmentioning

confidence: 99%

Sensors and systems for fruit detection and localization: A review

Gongal

Amatya

Karkee

et al. 2015

Computers and Electronics in Agriculture

447

244

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Section: Introductionmentioning

confidence: 99%

Sensors and systems for fruit detection and localization: A review

Gongal

Amatya

Karkee

et al. 2015

Computers and Electronics in Agriculture

447

244

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“…Pre-Processing. In this work fruit recognition [2,3] method is proposed where the three features (i.e., color, shape and texture) have been analyzed to recognize a fruit using CBVIR method.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fruit Recognition Technique using Multiple Features and Artificial Neural Network

Naskar¹,

Bhattacharya²

2015

IJCA

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Feature based Fruit recognition technique that has been proposed several times in past revealed in maximum research works either the color and shape or shape and texture or texture and color features. Different fruit images may have same color or identical shape or similar type texture but all three features are rarely identical at the same time for two different types of fruit. So the classification of fruit considering the three features at the same time increases the efficiency and accuracy of the algorithm. The proposed method represents fruit recognition expanding multiple feature based analysis that includes texture, color and shape. To recognize the texture of a fruit the Log Gabor filter has been used, mean hue has been calculated for color and shape has been analyzed by counting perimeter and area pixel. In the study of fruit recognition texture analysis using Log Gabor has been rarely used. The Projected fruit recognition technique is used to extract the above mention three features and Artificial Neural Network is used for classification. The system has achieved more than 90 % accuracy using successful implementation of the proposed algorithm. General TermsMachine Learning, Pattern Recognition.

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“…For example, they use the Point Grey BumbleBee2 binocular camera. 11,12 It is a commercial camera which makes it possible to acquire a depth map of viewed objects similarly to Microsoft Kinect.…”

Section: Two-frame Stereo Vision Systemmentioning

confidence: 99%

“…Calculations were made on the basis of Eq. (11). Disparity maps were calculated with the use of all these measures for window sizes 3 × 3 and 5 × 5.…”

Section: Experiments With Different Matching Measuresmentioning

confidence: 99%

Improving depth maps of plants by using a set of five cameras

Kaczmarek

2015

J. Electron. Imaging

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Abstract. Obtaining high-quality depth maps and disparity maps with the use of a stereo camera is a challenging task for some kinds of objects. The quality of these maps can be improved by taking advantage of a larger number of cameras. The research on the usage of a set of five cameras to obtain disparity maps is presented. The set consists of a central camera and four side cameras. An algorithm for making disparity maps called multiple similar areas (MSA) is introduced. The algorithm was specially designed for the set of five cameras. Experiments were performed with the MSA algorithm and the stereo matching algorithm based on the sum of sum of squared differences (sum of SSD, SSSD) measure. Moreover, the following measures were included in the experiments: sum of absolute differences (SAD), zero-mean SAD (ZSAD), zero-mean SSD (ZSSD), locally scaled SAD (LSAD), locally scaled SSD (LSSD), normalized cross correlation (NCC), and zero-mean NCC (ZNCC). Algorithms presented were applied to images of plants. Making depth maps of plants is difficult because parts of leaves are similar to each other. The potential usability of the described algorithms is especially high in agricultural applications such as robotic fruit harvesting.

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A fruit recognition method for automatic harvesting

Cited by 26 publications

References 5 publications

Sensors and systems for fruit detection and localization: A review

Sensors and systems for fruit detection and localization: A review

A Novel Fruit Recognition Technique using Multiple Features and Artificial Neural Network

Improving depth maps of plants by using a set of five cameras

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