Research on Carrot Grading Based on Machine Vision Feature Parameters

Abstract Carrot grading is a crucial part in the carrot processing and marketing. At present, the grading of carrots mainly depends on manual grading, which is labor intensive and low efficient In this paper, six shape parameters of carrot, including length, maximum diameter, average diameter, area, perimeter and aspect ratio, and six color parameters on R, G, B, H, S and V components were extracted by machine vision. Taking these 12 parameters as input feature parameters, the grading recognition models of back propagation neural network (BPNN), support vector machine (SVM) and extreme learning machine (ELM) are constructed, and compared by the recognition effects. The results show that the image acquisition system constructed in this paper can extract the feature parameters of carrot accurately. As a simple and easy to solve algorithm, the ELM model based on shape and color parameters has the best recognition effect and the recognition accuracy reaches 96.67%. It provides a reference classification method of carrots by digital.

[1]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[2]  Zhen Liu,et al.  Classification of Potato External Quality based on SVM and PCA , 2017 .

[3]  Stephen W. Searcy,et al.  Computer vision determination of the stem/root joint on processing carrots , 1989 .

[4]  Andreas Kamilaris,et al.  Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..

[5]  F. Hahn,et al.  Carrot Volume Evaluation using Imaging Algorithms , 2000 .

[6]  Sitti Wetenriajeng Sidehabi,et al.  The Development of Machine Vision System for Sorting Passion Fruit using MultiClass Support Vector Machine , 2018, Journal of Engineering Science and Technology Review.

[7]  Ferhat Kurtulmuş,et al.  Classification of pepper seeds using machine vision based on neural network , 2016 .

[8]  Douglas Chai,et al.  A comprehensive review of fruit and vegetable classification techniques , 2018, Image Vis. Comput..

[9]  Wang Wei,et al.  Determination of chestnuts grading based on machine vision. , 2010 .

[10]  Baohua Zhang,et al.  Challenges and solutions of optical-based nondestructive quality inspection for robotic fruit and vegetable grading systems: A technical review , 2018, Trends in Food Science & Technology.

[11]  U. Mn.  Image processing method for detection of carrot green-shoulder, fibrous roots and surface cracks , 2013 .

[12]  R Mahendran,et al.  Application of Computer Vision Technique on Sorting and Grading ofFruits and Vegetables , 2012 .

[13]  Abdolabbas Jafari,et al.  Evaluation of support vector machine and artificial neural networks in weed detection using shape features , 2018, Comput. Electron. Agric..

[14]  Deng Limiao,et al.  A Carrot Sorting System Using Machine Vision Technique , 2019, Computer Vision-Based Agriculture Engineering.

[15]  Bai Jie,et al.  Nondestructive detection of chilled mutton freshness based on multi-label information fusion and adaptive BP neural network , 2018, Computers and Electronics in Agriculture.

[16]  Min Zhang,et al.  A novel method using MOS electronic nose and ELM for predicting postharvest quality of cherry tomato fruit treated with high pressure argon , 2018, Computers and Electronics in Agriculture.

[17]  Xiuqin Rao,et al.  Separating clods and stones from potato tubers based on color and shape , 2018, Journal of Food Measurement and Characterization.

[18]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.