A Study on Image Based Techniques for Fruits and Meat by IJIRST

IJIRST â&#x20AC;&#x201C;International Journal for Innovative Research in Science & Technology| Volume 3 | Issue 10 | March 2017 ISSN (online): 2349-6010

A Study on Image Based Techniques for Fruits and Meat Dr. A. Jaganathan Principal / Secretary Food Craft Institute, Hoshiarpur, Punjab, India

S. Muguntha Kumar Assistant Professor Department of Hotel Management & Catering Science Muthayammal College of Arts & Science, Namakkal, Tamilnadu, India

Abstract In this paper presents the recent development and application of image analysis and computer technology system in an automatic fruit recognition system. It is very much essential to through light on basic concepts and technologies associated with computer technology system, a tool used in image analysis of fruit characterization. In India the ever-increasing population, losses in handling and processing and the increased expectation of food products of high quality and safety standards, there is a need for the growth of accurate, fast and objective quality determination of fruits. A number of challenges had to be overcome to enable the system to perform automatic recognition of the kind of fruit or fruit variety using the images from the camera. Many different types of fruits are subject to significant variation in color and texture, depending on how ripe they are. There are many processes in agriculture where decisions are made based on the appearance of the product. Applications for grading the fruit by its quality, size or ripeness are based on its appearance, as well as a decision on whether it is healthy or diseased. The objective of this paper is to provide in depth introduction of machine vision system, its components and recent work reported on an automatic fruit characterization system. Keywords: Image Analysis, Fruits, Computer Technology, Meat _______________________________________________________________________________________________________ I.

INTRODUCTION

In Indianâ&#x20AC;&#x2122;s Agriculture is an economyâ&#x20AC;&#x2122;s mainstay and it comprises 18.5% of the gross domestic products. Due to the advance of cultivation technology, the total cultivation areas and yields for agricultural products have increased rapidly in recent years, generating tremendous market values. However this is indicating vast potential for India to emerge as a major exporter of agricultural produce, its share in global market is very low due to very high post harvest losses in handling and processing, mismanagement of trades and procurements, lack of knowledge of preservation and quick quality evaluation techniques. Ensuring product recognition and quality is one of the most important and challenging tasks of the industries before export of food and agricultural produce. The Computer technology is a rapid, economic, consistent and objective inspection technique, which has expanded into many diverse industries. II. COMPUTER TECHNOLOGY Computer technology has experienced growth with its applications expanding in diverse fields: medical diagnostic imaging; factory automation; remote sensing; forensics; autonomous vehicle and robot guidance. Computer technology is the construction of explicit and meaningful descriptions of physical objects from images (Ballard & Brown, 1992). The term which is synonymous with machine vision embodies several processes. Images are acquired with a physical image sensor and dedicated computing hardware and software are used to analyse the images with the objective of performing a predefined visual task. Machine vision is also recognised as the integrated use of devices for non-contact optical sensing and computing and decision processes to receive and interpret an image of a real scene automatically. The technology aims to duplicate the effect of human vision by electronically perceiving and understanding an image. In computer technology is a relatively young discipline with its origin traced back to the 1960s. Following an explosion of interest during the 1970s, it has experienced continued growth both in theory and application. Computer technology is considered study and application of methods which allows computers to examine and extract image contents or content of multidimensional data in general to facilitate solving a specific vision problem, such as pattern classification problem. There are six main areas of computer technology which are sensing, preprocessing, segmentation, description, recognition, and interpretation. Examples of computer technology applications include systems for controlling processes such as an industrial robot or an autonomous vehicle. Another example is detecting events for visual surveillance or organizing information for indexing databases of images and image sequences. Moreover, modelling objects or environments such as medical image analysis or topographical modelling is another use of computer technology. Computer technology system is also applied for agriculture issues such as a system for monitoring crops growth and weeds under rain shelter or a computer technology system for detecting weeds in cereal crops. The recognition system can be applied for educational purpose to enhanced learning,

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A Study on Image Based Techniques for Fruits and Meat (IJIRST/ Volume 3 / Issue 10/ 043)

especially for small kids and Down syndrome patients, of agricultural and food products pattern recognition. It can be used in grocery store which makes the customers label their purchases using automatic recognition based on computer technology. Computer technology is the construction of explicit and meaningful descriptions of physical objects from images. Recognition system has emerged as a â&#x20AC;&#x2DC;grand challenge' for computer technology, with the longer term aim of being able to achieve near human levels of recognition for tens of thousands of categories under a wide variety of conditions. It is central to optical character recognition, voice recognition, and handwriting recognition. It uses methods from statistics, machine learning and other areas. Typical applications are text classification to recognize different type of texts such as spam and non spam E-mails, speech recantation for specified purposes such as translating different languages to English, hand written recognition for postal codes, or the automatic face recognition which deals with digital images as input to the pattern recognition systems. In previous years, several types of image analysis techniques are applied to analyze the agricultural images such as fruits and vegetables, for recognition and classification purposes. Visual inspection is used extensively for the quality assessment of meat products applied to processes from the initial grading through to consumer purchases. McDonald and Chen (1990) investigated the possibility of using image-based beef grading in some of the earliest studies in this area. They discriminated between fat and lean in longissimus dorsi muscle based on reflectance characteristics, however poor results were reported. Recently greater accuracy was found in a study by Gerrard et al. (1996) where R2 (correlation coefficient) values of 0.86 and 0.84 for predicted lean colour and marbling were recorded, respectively, for 60 steaks using image analysis. Li et al. (1997) measured image texture as a means of predicting beef tenderness. Colour, marbling and textural features were extracted from beef images and analysed using statistical regression and neural networks. Their findings indicated that textural features were a good indicator of tenderness. Image analysis was also used for the classification of muscle type, breed and age of bovine meat. The meat slices analysed came from 26 animals with the focus of the analysis based on the connective tissue (directly related to meat tenderness) which contains fat and collagen variables with muscle type, breed, age, and clearly is visible on photographic images. The study led to a representation of each meat sample with a 58 features vector and indicated the potential of image analysis for meat sample recognition. Machine vision has also been used in the analysis of pork loin chop images (Lu et al., 2000; Tan, Morgan, Ludas, Forrest, & Gerrard, 2000). Colour image features were extracted from segmented images (Lu et al., 2000). Both statistical and neural network models were employed to predict colour scores by using the image features as inputs and then compared to the sensory scores of a trained panel. The neural network model was determined as the most accurate with 93% of the 44 samples examined producing a prediction error of less than 0.6. Fig. 4 shows an original and segmented pork loin chop image used in this study. In a similar study over 200 pork lion chops were evaluated using colour machine vision (CMV) (Tan et al., 2000). Agreement between the vision system and the panellists was as high as 90%. The online performance of CMV was also determined in this study with repeatedly classifying 37 samples at a speed of 1 sample per second. A technique for the spectral image characterisation of poultry carcasses for separating tumourous, bruised and skin torn carcasses from normal carcasses was investigated by Park, Chen, Nguyen, and Hwang (1996). Carcasses were scanned by an intensified multi-spectral camera with various wavelength filters (542â&#x20AC;&#x201C;847 nm) with the results indicating that the optical wavelengths of 542 and 700 nm were the most useful for the desired classification. For separating tumourous carcasses from normal ones, the neural network performed with 91% accuracy. Co-occurrence matrix texture features of multi-spectral images were used to identify unwholesome poultry carcasses (Park & Chen, 2001). Both quadratic and linear discriminant models had an accuracy of 97% and 95%, respectively. Defects were also detected using the chromatic content of chicken images (Barni, Cappellini, & Mecocci, 1997). Possible defect areas were first extracted by means of morphological image reconstruction and then classified according to a predefined list of defects. Soborski (1995) investigated the online inspection of shape and size of chicken pieces. III. IMAGE PROCESSING & ITS TECHNIQUES Image processing and analysis techniques is mainly categorized into three levels: high level processing (recognition and interpretation), intermediate level processing (image segmentation and image representation and description) and low level processing (image acquisition and pre-processing). Image Acquisition Image capturing devices or sensors are used to view and generate images of the samples. Some of the devices or sensors used in generating images include scanners, ultrasound, X-ray and near infrared spectroscopy. However, in machine vision, image sensors used are the solid state charged coupled device (CCD) (i.e. camera) technology with some applications using thermionic tube devices. Recent technology has seen the adoption of digital camera, which eliminates the additional component required to convert images taken by photographic and CCD cameras or other sensors to readable format by computer processors. Images captured or taken by digital camera maintain the features of the images with little noise due to its variable resolution. Segmentation Image segmentation is a process of cutting, adding and feature analysis of images aimed at dividing an image into regions that have a strong correlation with objects or areas of interest using the principle of matrix analysis. Segmentation can be achieved by the following techniques: thresholding, edge based segmentation and region based segmentation. Thresholding is used in characterizing image regions based on constant reflectivity or light absorption of their surface. This shows that regions with same

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A Study on Image Based Techniques for Fruits and Meat (IJIRST/ Volume 3 / Issue 10/ 043)

features are characterized and extracted together. Thresholding is process where only the dark region is of interest, the other regions are converted to the background colour in the threshed image before further processing such as sending signals to a device to take a feature based decision. This process is useful in colour (maturity) and feature based (defect and damages detection) sorting. IV. ASSESSMENT OF FRUITS Computer technology has been widely used for the quality inspection and grading of fruits and vegetables. Computer technology is generally used for grading, defect detection, classification and finding out the ripeness of fruits based on its appearance. Apples The study of apples using computer technology has attracted much interest and reflects the progress of computer technology technology for fruit inspection. P. Sudhakara Rao et al. have adopted HSI model for sorting and grading of fruits by colour and developed a prototype for online sorting of Apples based on colour, size and shape. The basic image capturing system adopted by them is a colour CCD camera and a frame grabber card and it provided the image in RGB model. The RGB model, after normalisation, is first converted into HSI model using a set of converting equations. The image will be analyzed by using advanced image processing algorithms to estimate the colour of image. By representing median density of Hue as a grading criterion, the image processing system achieved around 99 % accuracy in colour inspection of apples. V. Leemans et al have graded apples into four classes according to European standards Two varieties were tested: Golden Delicious and Jonagold. The image database included more than a 1000 images of fruits (528 Golden Delicious, 642 Jonagold) belonging to the three acceptable categories {Extra, I and II} and the reject (each class represents, respectively, about 60, 10 and 20% of the sample size).The image grading was achieved in six steps: image acquisition; ground colour classification; defect segmentation; calyx and stem recognition; defects characterisation and finally the fruit classification into quality classes. The proposed method for apple external quality grading showed correct classification rates of 78 and 72%, for Golden Delicious and Jonagold apples, respectively. The healthy fruit were better graded and an error rate which drops to 5 and 10%, respectively. P. Sudhakara Rao et.al. have developed six different methods to determine the size of this category of apples by applying the known geometrical models such as circle method, parabola method, ellipse method, principal axis method, radius and area signature method and coefficient of variation method. Ismail Kavdir have applied Fuzzy logic (FL) as a decision making support to grade apples. Quality features such as the color, size and defects of apples were measured through different equipment. The same set of apples was graded by both a human expert and a FL system designed for this purpose. Grading results obtained from FL showed 89% general agreement with the results from the human expert, providing good flexibility in reflecting the experts expectations and grading standards into the results. Devrim Unay have proposed a method for Apple Defect Detection and Quality Classification with MLP-Neural Networks. Here The initial analysis of a quality classification system for ‘Jonagold’ and ‘Golden Delicious’ apples were shown. Later, Color, texture and wavelet features are extracted from the apple images. Principal components analysis was applied on the extracted features and some preliminary performance tests were done with single and multi layer perceptrons. The best results were 89.9 and 83.7 per cent for overall and defected pixels of 6 defected images. Dates Yousef Al Ohali have designed and implemented a prototypical computer technology based date grading and sorting system. They have defined a set of external quality features such as flabbiness, size, shape, intensity and defects. The system used RGB images of the date fruits and from these images, it automatically extracted the aforementioned external date quality features. Based on the extracted features it classified dates into three quality categories (grades 1, 2 and 3) defined by experts using back propagation neural network classifier and tested the accuracy of the system on preselected date samples. The test results showed that the system can sort 80% dates accurately. Grapes Dae Gwan Kim have designed technologies of color imaging and texture feature analysis that is used for classifying citrus peel diseases under the controlled laboratory lighting conditions. A total of 39 image texture features were determined from the transformed hue (H), saturation (S), and intensity (I) region-of-interest images using the color co-occurrence method for each fruit sample. The model using 14 selected HSI texture features achieved the best classification accuracy (96.7%), which suggested that it would be best to use a reduced hue, saturation and intensity texture feature set to differentiate citrus peel diseases. Average classification accuracy and standard deviation were 96.0% and 2.3%, respectively, for a stability test of the classification model, indicating that the model is robust for classifying new fruit samples according to their peel conditions. Watermelon Hassan Sadrnia et al have classified the long type watermelon depending on the fruit shapes. Physical characteristics of watermelon such as mass, volume, dimensions, density, spherical coefficient and geometric mean diameter were measured.

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A Study on Image Based Techniques for Fruits and Meat (IJIRST/ Volume 3 / Issue 10/ 043)

Relations and correlations coefficient were obtained between characteristics for normal and non-standard fruit shape. It was found that weight of normalwatermelon could be determined by image analysis with error 2.42%. Orange B Sathya Bama has proposed an approach for quality inspection of orange fruits using combined color texture features. In proposed method, input image is segmented using histogram based thresholding technique to identify whether the portion of image is defective or non defective. For defected samples, texture features were extracted from 3D co-occurance distribution. Sum of squared distance (SSD) is calculated between texture features of training and test fruits. Non defective fruits were considered as extra class whereas class I and class II were moderately and highly defective fruits respectively. A database of 150 samples was collected from mozambi and orange fruits with all quality classes for marketable. 80 images were manually classified for training and remaining was used for testing. Classification rate was greater than 93%. V. CONCLUSIONS This paper on computer technology reviews the recent developments in computer technology for the automatic fruit recognition system with various characteristics as such defect, quality, color, texture etc. Computer technology systems have been used increasingly in industry for inspection and evaluation purposes as they can provide rapid, economic, hygienic, consistent and objective assessment. However, difficulties still exist, evident from the relatively slow commercial uptake of computer technology technology in all sectors. Even though adequately efficient and accurate algorithms have been produced, processing speeds still fail to meet modern manufacturing requirements. However, the survey reveals that still much of the work needs to be concentrated on the fruits recognition in variety of angles of the fruit. At the end, we can say that computer technology is a powerful tool of automation that includes both image processing and image analysis tools. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]

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