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

被引:44
作者
Cubero, Sergio [1 ,2 ]
Diago, Maria Paz [2 ,3 ]
Blasco, Jose [1 ]
Tardaguila, Javier [2 ]
Millan, Borja [2 ]
Aleixos, Nuria [4 ]
机构
[1] Inst Valenciano Invest Agr, Ctr Agroingn, Valencia 46113, Spain
[2] Univ La Rioja, CSIC, Inst Ciencias Vid & Vino, Logrono, Spain
[3] Univ Cattolica Sacro Cuore, I-29122 Piacenza, Italy
[4] Univ Politecn Valencia, Inst Interuniv Invest Bioingn & Technol Orientada, Valencia 46022, Spain
关键词
VISION; IDENTIFICATION; YIELD; CLASSIFICATION; INSPECTION; SYSTEM; ROBOT; AREA;
D O I
10.1016/j.biosystemseng.2013.06.007
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
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 (R-2 > 0.96) and size (R-2 > 0.97) of wine-grapes and of assessing the size of other fruits like mandarins, apples, pears and red peppers (R-2 > 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. (C) 2013 IAgrE. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:62 / 72
页数:11
相关论文
共 36 条
[1]  
[Anonymous], 2004, Chemical Analysis of Grapes and Wine: Techniques and Concepts
[2]  
[Anonymous], 2010, Image processing and pattern recognition: fundamentals and techniques
[3]  
Barbagallo MG, 2011, S AFR J ENOL VITIC, V32, P129
[4]   Performance of a system for apple surface defect identification in near-infrared images [J].
Bennedsen, BS ;
Peterson, DL .
BIOSYSTEMS ENGINEERING, 2005, 90 (04) :419-431
[5]   Citrus sorting by identification of the most common defects using multispectral computer vision [J].
Blasco, J. ;
Aleixos, N. ;
Gomez, J. ;
Molto, E. .
JOURNAL OF FOOD ENGINEERING, 2007, 83 (03) :384-393
[6]   Machine vision system for automatic quality grading of fruit [J].
Blasco, J ;
Aleixos, N ;
Moltó, E .
BIOSYSTEMS ENGINEERING, 2003, 85 (04) :415-423
[7]   Automatic sorting of satsuma, (Citrus unshiu) segments using computer vision and morphological features [J].
Blasco, J. ;
Aleixos, N. ;
Cubero, S. ;
Gomez-Sanchis, J. ;
Molto, E. .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2009, 66 (01) :1-8
[8]   Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables [J].
Cubero, Sergio ;
Aleixos, Nuria ;
Molto, Enrique ;
Gomez-Sanchis, Juan ;
Blasco, Jose .
FOOD AND BIOPROCESS TECHNOLOGY, 2011, 4 (04) :487-504
[9]   Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions [J].
Diago, Maria-Paz ;
Correa, Christian ;
Millan, Borja ;
Barreiro, Pilar ;
Valero, Constantino ;
Tardaguila, Javier .
SENSORS, 2012, 12 (12) :16988-17006
[10]  
Electronic Computers O.N., 1960, Electronic Computers, IRE Transactions on, P260, DOI DOI 10.1109/TEC.1961.5219197