Evaluation of image processing technique and discriminant analysis methods in postharvest processing of carrot fruit

Abstract The most important process before packaging and preserving agricultural products is sorting operation. Sort of carrot by human labor is involved in many problems such as high cost and product waste. Image processing is a modern method, which has different applications in agriculture including classification and sorting. The aim of this study was to classify carrot based on shape using image processing technique. For this, 135 samples with different regular and irregular shapes were selected. After image acquisition and preprocessing, some features such as length, width, breadth, perimeter, elongation, compactness, roundness, area, eccentricity, centroid, centroid nonhomogeneity, and width nonhomogeneity were extracted. After feature selection, linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were used to classify the features. The classification accuracies of the methods were 92.59 and 96.30, respectively. It can be stated that image processing is an effective way in improving the traditional carrot sorting techniques.

[1]  Hiroshi Okamoto,et al.  Detection of potato tubers using an ultraviolet imaging-based machine vision system , 2010 .

[2]  Parviz Ahmadi Moghaddam,et al.  Online tomato sorting based on shape, maturity, size, and surface defects using machine vision , 2013 .

[3]  P. Pathmanaban,et al.  Recent application of imaging techniques for fruit quality assessment , 2019 .

[4]  Mohammad Momeny,et al.  Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks , 2020 .

[5]  E. Abbas.  Effect of GA3 on Growth and Some Physiological Characterizes in Carrot Plant (Daucus Carota L.) , 2011 .

[6]  Francisco Manzano-Agugliaro,et al.  High speed intelligent classifier of tomatoes by colour, size and weight , 2012 .

[7]  Xiao Zhuang,et al.  Grape size detection and online gradation based on machine vision , 2017 .

[8]  Rui Li,et al.  Classification of Kiwifruit Grades Based on Fruit Shape Using a Single Camera , 2016, Sensors.

[9]  S. Md. Iqbal,et al.  Estimation of Volume and Mass of Axi-Symmetric Fruits Using Image Processing Technique , 2015 .

[10]  Ali Jafari,et al.  Developing an orientation and cutting point determination algorithm for a trout fish processing system using machine vision , 2019, Comput. Electron. Agric..

[11]  Dorin Boldor,et al.  IMAGE PROCESSING METHOD TO DETERMINE SURFACE AREA AND VOLUME OF AXI-SYMMETRIC AGRICULTURAL PRODUCTS , 2002 .

[12]  Mahmoud Omid,et al.  Development of a lemon sorting system based on color and size. , 2010 .

[13]  Mahdi Ghasemi-Varnamkhasti,et al.  Detecting maturity of persimmon fruit based on image processing technique , 2015 .

[14]  Margarita Ruiz-Altisent,et al.  Olive classification according to external damage using image analysis. , 2008 .

[15]  M. Omid,et al.  Estimating volume and mass of citrus fruits by image processing technique , 2010 .

[16]  K. Kheiralipour,et al.  Application of infrared thermal imaging technique and discriminant analysis methods for non-destructive identification of fungal infection of potato tubers , 2019, Journal of Food Measurement and Characterization.

[17]  K. Kheiralipour,et al.  Introducing new shape features for classification of cucumber fruit based on image processing technique and artificial neural networks , 2017 .

[18]  Yudong Zhang,et al.  Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine , 2012, Sensors.

[19]  Asghar Mousavi,et al.  Sorting and Grading of Cherries on the Basis of Ripeness, Size and Defects by Using Image Processing Techniques , 2012 .

[20]  S. Rafiee,et al.  Detection of fungal infection in pistachio kernel by long-wave near-infrared hyperspectral imaging technique , 2016 .

[21]  Naoshi Kondo,et al.  Machine vision based soybean quality evaluation , 2017, Comput. Electron. Agric..

[22]  Sing Kiong Nguang,et al.  Low cost sensor for volume and surface area computation of axi-symmetric agricultural products , 2007 .

[23]  A. Koç.  Determination of watermelon volume using ellipsoid approximation and image processing , 2007 .

[24]  Sajad Sabzi,et al.  Machine vision based expert system to estimate orange mass of three varieties , 2017 .

[25]  Deng Limiao,et al.  Identifying carrot appearance quality by transfer learning , 2019, Journal of Food Process Engineering.

[26]  Yi-Chao Yang,et al.  Recent Advances in the Application of Hyperspectral Imaging for Evaluating Fruit Quality , 2015, Food Analytical Methods.

[27]  Mahmoud Omid,et al.  Comparing data mining classifiers for grading raisins based on visual features , 2012 .

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

[29]  M. JAVAN-NIKKHAH,et al.  Classifying Healthy and Fungal Infected-Pistachio Kernel by Thermal Imaging Technology , 2015 .

[30]  Xu Liming,et al.  Automated strawberry grading system based on image processing , 2010 .

[31]  Monica Carfagni,et al.  A machine vision system for real-time and automatic assessment of olives colour and surface defects , 2011 .