A new method for pedicel/peduncle detection and size assessment of grapevine berries and other fruits by image analysis

The berry size of wine-grapes has often been considered to influence wine composition and quality, as it is related to the skin-to-pulp ratio of the berry and the concentration of skin-located compounds that play a key role in the wine quality. The size and weight of wine-grapes are usually measured by hand, making it a slow, tedious and inaccurate process. This paper focuses on two main objectives aimed at automating this process using image analysis: (1) to develop a fast and accurate method for detecting and removing the pedicel in images of berries, and (2) to accurately determine the size and weight of the berry. A method to detect the peduncle of fruits is presented based on a novel signature of the contour. This method has been developed specifically for grapevine berries, and was later extended and tested with an independent set of other fruits with different shapes and sizes such as peppers, pears, apples or mandarins. Using this approach, the system has been capable of correctly estimating the berry weight (R2 > 0.96) and size (R2 > 0.97) of wine-grapes and of assessing the size of other fruits like mandarins, apples, pears and red peppers (R2 > 0.93). The proven performance of the image analysis methodology developed may be easily implemented in automated inspection systems to accurately estimate the weight of a wide range of fruits including wine-grapes. In this case, the implementation of this system on sorting tables after de-stemming may provide the winemaker with very useful information about the potential quality of the wine.

[1]  Yankun Peng,et al.  Hyperspectral Scattering for Assessing Peach Fruit Firmness , 2004 .

[2]  Nuria Aleixos,et al.  Erratum to: Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables , 2011 .

[3]  Lalit Gupta,et al.  A decision-fusion strategy for fruit quality inspection using hyperspectral imaging , 2012 .

[4]  Takashi Komeda,et al.  Gentle handling of strawberries using a suction device , 2011 .

[5]  Q. Yang,et al.  Finding stalk and calyx of apples using structured lighting , 1993 .

[6]  D. L. Peterson,et al.  Performance of a System for Apple Surface Defect Identification in Near-infrared Images , 2005 .

[7]  Rangaraj M. Rangayyan,et al.  Feature Extraction from a Signature Based on the Turning Angle Function for the Classification of Breast Tumors , 2008, Journal of Digital Imaging.

[8]  Haim J. Wolfson On curve matching , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  José Blasco,et al.  In-line sorting of irregular potatoes by using automated computer-based machine vision system , 2012 .

[10]  Herbert Freeman,et al.  On the Encoding of Arbitrary Geometric Configurations , 1961, IRE Trans. Electron. Comput..

[11]  Frank Y. Shih,et al.  Image Processing and Pattern Recognition: Fundamentals and Techniques , 2010 .

[12]  Moon S. Kim,et al.  Orienting apples for imaging using their inertial properties and random apple loading , 2009 .

[13]  J. Blasco,et al.  Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment , 2012, Food and Bioprocess Technology.

[14]  David W. Penman,et al.  Determination of stem and calyx location on apples using automatic visual inspection , 2001 .

[15]  Mark A. Matthews,et al.  Berry size and vine water deficits as factors in winegrape composition: Anthocyanins and tannins , 2004 .

[16]  Rob R. Walker,et al.  Shiraz berry size in relation to seed number and implications for juice and wine composition , 2005 .

[17]  P Jancsók,et al.  Stem-end/Calyx Identification on Apples using Contour Analysis in Multispectral Images , 2007 .

[18]  Stefano Poni,et al.  Impact of Early Leaf Removal on Yield and Fruit and Wine Composition of Vitis vinifera L. Graciano and Carignan , 2010, American Journal of Enology and Viticulture.

[19]  Silvia Guidoni,et al.  Berry Size and Qualitative Characteristics of Vitis vinifera L. cv. Syrah , 2016 .

[20]  Edith Schonberg,et al.  Two-Dimensional, Model-Based, Boundary Matching Using Footprints , 1986 .

[21]  James A. Throop,et al.  Conveyor Design for Apple Orientation , 2003 .

[22]  María-Paz Diago,et al.  Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions , 2012, Sensors.

[23]  Kenta Shigematsu,et al.  Evaluation of a strawberry-harvesting robot in a field test , 2010 .

[24]  Mark A. Matthews,et al.  BERRY SIZE AND YIELD PARADIGMS ON GRAPES AND WINES QUALITY , 2007 .

[25]  Filiberto Pla,et al.  Location and Characterization of the Stem–Calyx Area on Oranges by Computer Vision , 1996 .

[26]  Yael Edan,et al.  IMAGE–PROCESSING ALGORITHMS FOR TOMATO CLASSIFICATION , 2002 .

[27]  Dean Rubine,et al.  Specifying gestures by example , 1991, SIGGRAPH.

[28]  I. Kunttu,et al.  Shape-based retrieval of industrial surface defects using angular radius Fourier descriptor , 2007 .

[29]  Yael Edan,et al.  Computer vision for fruit harvesting robots - state of the art and challenges ahead , 2012, Int. J. Comput. Vis. Robotics.

[30]  E. J. van Henten,et al.  An Autonomous Robot for De-leafing Cucumber Plants grown in a High-wire Cultivation System , 2006 .

[31]  A. M. Lefcourta,et al.  Orienting apples for imaging using their inertial properties and random apple loading , 2009 .

[32]  José Blasco,et al.  Citrus sorting by identification of the most common defects using multispectral computer vision , 2007 .

[33]  José Blasco,et al.  Machine Vision System for Automatic Quality Grading of Fruit , 2003 .

[34]  José Blasco,et al.  Original paper: Automatic sorting of satsuma ( Citrus unshiu ) segments using computer vision and morphological features , 2009 .