cv agriculture 2010

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2010 International Journal of Computer Applications (0975 8887)

Volume 1No. 4

1

PROSPECTS OF COMPUTER VISION AUTOMATED

GRADING AND SORTING SYSTEMS IN

AGRICULTURAL AND FOOD PRODUCTS FOR

QUALITY EVALUATION

Narendra V G1 and Hareesh K S2

1Sr. Lecturer Dept. Of CSE, Manipal Institute Of Technology, Manipal, Karnataka, India-576 1042Reader Dept. Of CSE, Manipal Institute Of Technology, Manipal, Karnataka, India-576 104

AbstractThe paper presents the recent development and application of image analysis and computer vision systems in sorting and

grading of products in the field of agricultural and food. Basic concepts and technologies associated with computer vision,a tool used in image analysis and automated sorting and grading is highlighted.

For the ever-increasing population, losses in handling and processing and the increased expectation of food products of

high quality and safety standards, there is need for the growth of accurate, fast and objective quality determination of the

characteristics of food and agricultural food products. Computer vision and image analysis, are non-destructive and cost-effective technique for sorting and grading of agricultural and food products during handling processes and commercial

purposes. Different approaches based on image analysis and processing identified is related to variety of applications in

agricultural and food products.

Keywords: Quality, Sorting and Grading, Automation, Image Processing, Machine Vision;

1. INTRODUCTION

Technological advancement is gradually finding

applications in the agricultural and food products, in

response to one of the greatest challenges i.e. meetingthe need of the growing population. Efforts are being

geared up towards the replacement of human operatorwith automated systems, as human operations are

inconsistent and less efficient. Automation means every

action that is needed to control a process at optimum

efficiency as controlled by a system that operates using

instructions that have been programmed into it or

response to some activities. Automated systems in most

cases are faster and more precise. However, there are

some basic infrastructures that must necessarily be in

place in automation [1].

The technology of image analysis is relatively young and

its origin can be traced back to the 1960s. It hasexperienced tremendous growth both in theory and in

application. It has found application in areas such as

medical diagnostics, automated manufacturing, aerial

surveillance, remote sensing and very recently in thefield of automated sorting and grading of agricultural

and food products. Computer vision is a novel

technology for acquiring and analyzing an image of a

real scene by computers and other devices in order to

obtain information or, to control machines or

processes [47].

In Timmermans [51] opinion, computer vision systemincludes the capturing, processing and analyzing

images to facilitate the objective and non-destructiveassessment of visual quality characteristics in

agricultural and food products. The techniques used in

image analysis include image acquisition, image pre-

processing and image interpretation, leading to

quantification and classification of images and objects

of interest within images.

Harvesting and packing account for the major portion

of the effort and cost incurred by farmers producing

fresh fruits and vegetables to market. However the

processing and manufacturing sectors require product

sorting and grading for commercial and productionpurposes. There is continuous growth in the

development of mechanical harvesting system, and the

need for automated inspection, as well as grading

systems so that the losses incurred during harvesting,production and marketing can be minimized. With

these, the need arises to not only grow and harvest a

quality crop, but also to pack in a consistent and

acceptable manner to gain or to maintain market share


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as well as prepare materials, which suits processingoperations. These cannot be achieved without sorting

and grading.

Sorting and grading of an agricultural and food products

is accomplished based on appearance, texture, colour,shape and sizes. Manual sorting and grading are based

on traditional visual quality inspection performed by

human operators, which is tedious, time-consuming,

slow and non-consistent. A cost effective, consistent,

superior speed and accurate sorting can be achieved with

machine vision assisted sorting and grading. Automated

sorting and grading had undergone substantial growth in

the field of agricultural and food, in the developed and

developing nations because of availability of

infrastructures. Computer application in agriculture andfood industries have been applied in the areas of sorting,

grading of fresh products, detection of defects such ascracks, dark spots and bruises on fresh fruits and seeds.

The new technologies of image analysis and machine

vision have not been fully explored in the developmentof automated machine in agricultural and food

industries. There is increasing evidence that machine

vision is being adopted at commercial level [22].

The method used by the farmers and distributors to sort

and grade agricultural and food products are through

traditional quality inspection and handpicking which is

time-consuming, laborious and less efficient. Sun et al.[48] observed that the basis of quality assessment is

often subjective with attributes such as appearance,

smell, colour, texture and flavor frequently examined by

human inspectors. Francis [13] found that human

perception could easily be fooled. It is pertinent toexplore the possibilities of adopting faster systems,which will save time and more accurate in sorting and

grading of agricultural and food products. One of such

reliable method is the automated computer vision system

for sorting and grading.

Advances in computer technology have produced a surgeof interest in image analysis during the last decade and

the potential of this technique for the guidance or control

of agricultural and food processes have been recognized

[37]. Series of studies have been conducted in recent

years to investigate the application of computer vision

technology to sorting and grading of fresh agriculturaland food products. Yam and Spyridon [60] used a simple

digital imaging method for measuring and analyzing

colour of food surfaces and found that the method allowsmeasurements and analysis of the colour of food

surfaces that are adequate for food engineering research.Payne and Shearer [33] developed a machine vision

algorithm for grading of fresh market produce according

to colour and damage while Alchanatis et al [3], used a

neural network based classifier and colour machine

vision instead of the conventional use of black andwhite cameras and geometric features for automatic

classification of tissue culture segments of potato

plantlets. It was found that instead of using only

geometric features combining it with colour

significantly increased the separability of the classes inthe feature space.

Real time detection of defects in fruits using a general

hardware and image processing techniques was

reported by Delwiche and Crowe [9]. Two fruits

(apples and peaches) were tested at the rate of five

fruits/second to evaluate system performance. They

developed an algorithm to acquire and analyze two

combined near infrared (NIR) images of each fruit in

real time with a pipeline image processing system.Their result showed that the system was capable of

executing the sorting algorithm at a rate of 14fruits/second, which was greater than conventional

fruit conveying rates. In this submission, it was

observed that acquiring more than 2 images per fruitand using more than 6 lines of structured illumination

per fruit would reduce the sorting errors slightly in the

case of the apples and greatly improve the system

performance with peaches. Algorithms were

developed by Panigrahi and Misra [32] to measure

fractal-based feature and dimensions (length, width,

area and perimeter) of ear corn images to discriminate

among various shapes. The analysis showed that thecombination of the feature is able to discriminate the

shape differences while Carrion et al [8] described

sorting based on an unsupervised vision system.

Raji [36] developed an algorithm for determining thearea of 2-dimensional objects by image analysis. Thiscan be adapted in detection of leave type as a form of

sorting to select the desired ones during harvesting.

The discrimination of leave from weed using feature

identification by analysis was investigated and

reported by Tsheko [53].

Bull [7] observed the image capture techniques, which

could be or are being used in the field of the

agricultural and food, to generate image that can be

analyzed and used as feedbacks for an automated

systems. The appropriate technique to be adopted by

each industry would depend on the property of thesample to be monitored, the nature of the sample, its

environments and other practical restrictions such as

imaging time. With increased awareness andsophistication on the part of the consumers and the

expectation for improved quality in consumeragricultural and food products which has increased the

need for enhanced quality monitoring in field of

agricultural and food, there is a need to develop

various techniques of image analysis to meet the


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demand of the growing population. Quality is the sum ofthese attributes that can lead to the production of

products acceptable to the consumer when they are

combined.

In the food industries in developing countries, there isgrowing need for automation due to the fact that the

labour intensive manual processes are not efficient,

accurate and effective. In the developed countries on the

other hand Gunasekaran [14] observed that the food

industry is now ranked among the top 10 industries using

this technology.

This study therefore sets out to review the state and level

of application of image processing and machine vision in

automated sorting and grading with its application andsteps to be taken in improving and developing the

emerging technology beyond its present state toagricultural and food products sorting and grading.

2. TECHNIQUES IN IMAGE PROCESSING

2.1. Equipment and TechniquesA computer vision system consists of two basic

components which are image acquisition: illumination

and image capture device (camera) and image analysis:

an image capture board (frame grabber or digitizer) and

analysis software. A typical laboratory set for image

processing is as shown in Figure 1.

Figure 1: Components of a computer vision system [56] .

2.2. Computer Vision System

The hardware configuration of computer-based

Computer vision systems is relatively standard.

Typically, a computer vision system consists of: An illumination device, which illuminates the sample

under test A solid-state CCD array camera, to acquire an image A frame-grabber, to perform the A/D (analog-to-digital) conversion of scan lines into picture elements or

pixels digitized in aNrow byMcolumn image

A personal computer or microprocessor system, to

provide disk storage of images and computational

capability with vendor-supplied software and specific

application programs

A high-resolution colour monitor, which aids invisualizing images and the effects of various image

analysis routines.

3. IMAGE ACQUISITION

3.1. IlluminationComputer Vision Systems are affected by the level and

quality of illumination as with the human eye. The

performance of the illumination system greatly

influences the quality of image and plays an important

role in the overall efficiency and accuracy of the

system. Illumination systems are the light sources. The

light focuses on the materials (especially when used).

Lighting type, location and colour quality play an

important role in bringing out a clear image of the

object. Lighting arrangements are grouped into front-or back-lighting [15]. Front lighting serve as

illumination focusing on the object for better detectionof external surface features of the product while back-

lighting is used for enhancing the background of the

object. Light sources used include incandescent lamps,fluorescent lamps, lasers, X-ray tubes and infra-red

lamps.

3.2. 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 infraredspectroscopy. 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 toconvert 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.

4. IMAGE PRE-PROCESSINGThis refers to the initial processing of the raw image.

The images captured or taken are transferred onto a

computer and are converted to digital images. Digital

images though displayed on the screen as pictures, are

digits, which are readable by the computer and areconverted to tiny dots or picture elements representing

the real objects. In some cases preprocessing is done to

improve the image quality by suppressing undesireddistortions referred to as noise or by the

enhancement of important features of interest.

The images or pictures are transformed into computer

digital readable format (i.e. digitized) if a digital

camera did not take them by the image board digitizer.


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The digitized format is then transferred and used as theinput data by the image processing software to carry out

the necessary processes. Each or a combination of the

digits represent the feature a small portion of the image

called picture element (pixel). Objects are described as

black and white pictures which are represented by digitsranging from 0 to 255 where 0 is black and 255 is white.

Each pixel in coloured pictures is represented by 3 digits

representing RGB [Red, Green, Blue] components with

each being (0 to 255) darkest to lightest RGB. An

arrangement of these digits in row-column format gives

a representation of the image. With this arrangement

using the matrix theory does the analysis in image

processing. Further details can be obtained in Tsheko

[53], Raji [36], Raji et al [37] and any text or reports onimage processing and computer machine vision.

Figure 2: Different levels in the image processing[43].

Image acquisition and image pre-processing arecategorized as low-level processing while the

intermediate and the high level processing stages asclassified by Sun [43] (Figure 2) are further processing

stages required when integrating the preprocessing

stages into an application device such as a sorter and

grader.

The intermediate-level processing involves imagesegmentation, image representation and image

description. 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 andregion 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 features are characterizedand extracted together. Figure 3 shows a thresholding

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 baseddecision. This process is useful in colour (maturity)

and feature based (defect and damages detection)

sorting.

Original Image Threshold image

Figure 3. Thresholding

Edge based segmentation relies on detection by edge-

to-edge operators, which detect discontinuities in grey

level, the pixel, colour, texture etc. Edge detection is

useful in shape and size sorting. An example of edge

detection result is shown in Figure 4. The applicationof this process was reported by Raji et al [37] who

demonstrated the detection of defects in the shape of

bread and biscuit samples on a processing line.

Figure 4: Edge based segmentation

Region based segmentation involves the groupingtogether and extraction of similar pixels to form

regions representing single objects within the image.

(Figures 5). In this process the other regions are

deleted leaving only the feature of interest.

High level processing deals with recognition and

interpretation, typically using statistical classifiers or

multiplayer neural networks of region of interest.

These steps provide information necessary for the

process or machine control for quality sorting andgrading.


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Figure 5. Region based segmentationAn interaction of all these levels and knowledge

database are very important and essential for more

precise decision-making and is seen as an integral part of

the image processing process.

These theories when applied to images of products taken

can be used to extract features, which are needed for the

necessary processes. Generally, edge detection to

determine shape and feature extraction to determine

differences in colour is useful in sorting and harvesting.

5. APPLICATIONS IN AGRICULTURAL AND

FOOD PROCESSING OPERATIONS

Computer vision systems are being used increasingly in

the field of agricultural and food products for qualityassurance purpose. The system offers the generation of

precise descriptive data and reduction of tedious humaninvolvement. Computer vision system has proven

successful for the objective, online measurement of

several agricultural and food products with applicationranging from routine inspection to the computer vision

system guided robotic control [48]. Some of the areas

where the techniques have been applied in agricultural

and food processing which need to be developed further

for commercial purposes include.

5.1 Bakery Products

Appearance of baked products is an important qualityattribute which influences the visual perceptions of

customers and hence potential demands of the products.

The appearance of the internal and external features

contributes to the overall impression of the products

quality. Computer vision system has been used tomeasure characteristics such as colour, size and shapewith a view to sorting them to products with same

characteristics before packing.

Raji et al [37] developed a programme in FORTRAN

using the principle of edge detection in image analysis to

determine the edge of sliced breads and biscuits (roundand rectangular) with a view to detecting defects

(breakage). Scott [41] described a system, which

measures the defects in baked loaves of bread, by

analyzing its weight and slope of the top. The internal

structure (crumb grain) of bread and cake was also

examined by machine vision [40]. Dos Mohammed et al[10] also developed a system for the automated visual

inspection of muffins.

Visual features such as colour and size indicate the

quality of many prepared consumer foods. Sun [43]investigated this in research on pizza in which pizza

topping percentage and distribution were extracted from

pizza images. Combining three algorithms used to

segment many different types of pizzas as the traditional

segmentation techniques were found to be inadequatefor this application developed a new segmentation

algorithm. It was found that the new region-based

segmentation technique could effectively group pixels

of the same topping together. As the result, topping

exposure percentage can be easily determined. Thestudy reported that the accuracy of measuring the

topping percentage by the new algorithm reached

90%.

The evaluation of the functional properties of cheese is

assessed to ensure the necessary quality is achieved,

especially for specialized applications such as

consumer food toppings or ingredients. Wang and Sun

[55] developed a computer vision method to evaluate

the melting and browning of cheese. This novel non-contact method was employed to analyze the

characteristics of cheddar and mozzarella cheesesduring cooking and the results showed that the method

provided an objective and easy approach for analyzing

cheese functional properties [56,57]. Ni andGunasekaran [29] developed an image-processing

algorithm to recognize individual cheese shred and

automatically measure the shred length. It was found

that the algorithm recognized shreds well, even when

they were overlapping. It was also reported that the

shred length measurement errors were as low as 0.2%

with a high of 10% in the worst case.

The application of this method is therefore a promising

approach to solving quality control inspection in the

bakery products. This will improve the quality of

baked products, where there are no standard sizes,

shape and texture. The required system though mayadd to the cost of production but the added advantagewill far outweigh the initial cost involved.

5.2. FruitsExternal qualities i.e. sizes, shapes and colour are

considered of paramount importance in the marketing

and sale of fruit. Presence of blemishes influencesconsumer perceptions and therefore determines the

level of acceptability prior to purchase. Computer

vision system has been used for the automated

inspection and grading of fruit to increase product

throughput.

The number of fruits (ripe and unripe) on a tree has

been counted by image analysis prior to harvesting.

This process involved the development of fruitlocation algorithms [24]. Images were taken from a

distance of about 150cm and all the fruit on the treeplaced within the vision field of a camera were

counted. The visible fruits were considered to be those

that the human eye can distinguish on the monitor.

Algorithms based on the red/green relation and on


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threshold achieved the highest detection percentages oncitrus fruit.

The adoption can also be extended to the peasants

through tractor hiring bodies or by forming cooperatives

to acquire such devices, which only need to be attachedto a tractor. The problem of unplanned fruit trees, which

may make machine movement very difficult, will also

need to be addressed. The high losses incurred presently

on fruits in the market comes as a result of the damages

and bruises as well as potential damage region inflicted

on the fruits falling from the trees due to the method of

harvesting of shaking the trees or the branches. The

adoption can also be extended to the peasants through

tractor hiring bodies or by forming cooperatives to

acquire such devices, which only need to be attached to atractor. The problem of unplanned fruit trees, which may

make machine movement very difficult, will also need tobe addressed. The high losses incurred presently on

fruits in the market comes as a result of the damages and

bruises as well as potential damage region inflicted onthe fruits falling from the trees due to the method of

harvesting of shaking the trees or the branches.

Strawberry appearance and fruit quality are dependent

on a number of pre- and post- harvest factors, hence

variation occurs, necessitating the need for sorting.

Nagata et al. [26] investigated the use of computer vision

to sort fresh strawberries, based on size and shape. Theexperimental results show that the developed system was

able to sort the 600 strawberries tested with an accuracy

of 94-98% into three grades based on shape and five

grades on size. Another automatic strawberry sorting

system was developed by Bato et al. [6]. Average shapeand size accuracies of 98 and 100%, respectively, wereobtained regardless of the fruit orientation angle with

judgement time within 1.18 s.

Prior to export, papayas are subjected to inspection for

the purpose of quality control and grading. For size

grading, the fruit is weighted manually hence thepractice is tedious, time consuming and labour intensive.

Therefore, a computer vision system for papaya size

grading using shape characteristic analysis. The shape

characteristics consisting of area, mean diameter and

perimeter were extracted from the papaya images.

Slamet Riyadi et al. [43] classified according tocombinations of the three features to study the

uniqueness of the extracted features. The proposed

technique showed the ability to perform papaya sizeclassification with more than 94% accuracy.

5.3. VegetablesThe need to be responsive to market demands places a

greater emphasis on quality assessment resulting in the

greater need for improved and more accurate grading

and sorting practices. Computer vision system has

shown to be a viable means of meeting these increasedrequirements for the vegetables field.

Plantlet segments of potato were subcultures for

classification by colour machine vision system by

Alchanatis et al., [3]. In related development, twoalgorithms were developed for analyzing digital binary

images and estimating the location of stem root joints

in processing carrots [5]. Both algorithms were

capable of estimating the stem/root location with a

standard deviation of 5mm. Also Haworth and Searcy

[17] classified carrots on surface defects, curvature

and brokenness. A line scans images with discrete

Fourier transform was developed for the classification

of broccoli heads for assessing its maturity. For the

160 observations from each of three broccoliscultivates, an accuracy of 85% was achieved for

multiple cultivars.

Mushrooms discolouration is undesirable in

mushroom houses and it reduces market value. Thecolour and shape of the cap is the most important

consideration of fresh mushrooms. Felfodi and

Vizhanyo [11] used mushroom images recorded by a

machine vision system to recognize and identify

discolouration caused by bacterial disease. The

method identified all the diseased spot as diseased

and none of the healthy mushrooms parts weredetected as diseased. Reed et al [38] used camera-based technology to select mushroom by size for

picking by a mushroom harvester.

Potatoes have many possible shapes, which need to be

graded for sale into uniform classes for differentmarkets. This created difficulties for shape separation.A Fourier analysis based shape separation method for

grading of potatoes using machine vision for

automated inspection was developed by Tao et al.

[50]. A shape separator based on harmonics of the

transform was defined. Its accuracy of separation was

89% for 120 potato samples, in agreement withmanual grading. Earlier, Lefebvre et al. [19] studied

the use of computer vision for locating the position of

pulp extraction automatically for the purpose of

further analysis on the extracted sample. An image

acquisition system was also constructed for mounting

on a sweet potato harvester for the purpose of yieldand grade monitoring [58]. It was found that culls

were differentiated from saleable sweet potatoes with

classification rates as high as 84%.

Chilli is a variety grown extensively consumed byalmost all the population. It has a high processing

demand and proper sorting is required before filling or

canning. A sorter that, Federico Hahn [12] classifies

chilli by three different width sizes was built. The


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conveyor used baby suckers to align each chilli duringsensing. Chilli width was determined by means of a

photodiode scanner, which detected the incoming

radiation sent by a laser line generator. Chilies

presenting necrosis were detected with a radiometer and

removed to increase product quality. Horizontal andvertical widths were measured for 200 chilies. The

accuracy on the necrosis detection and width

classification was of 96.3 and 87%, respectively. On-line

necrosis measurements were 85% accurate when only

the relative reflectance at 550nm was used.

Vegetable quality is frequently referred to size, shape,

mass, firmness, colour and bruises from which fruits can

be classified and sorted. However, technological by

small and middle producers implementation to assessthis quality is unfeasible, due to high costs of software,

equipment as well as operational costs. Based on theseconsiderations, the Antonio Carlos Loureiro Lino et al.

[4] research is to evaluate a new open software that

enables the classification system by recognizing fruitshape, volume, colour and possibly bruises at a unique

glance. The software named ImageJ, compatible with

Windows, Linux and MAC/OS, is quite popular in

medical research and practices, and offers algorithms to

obtain the above-mentioned parameters. The software

allows calculation of volume, area, averages, border

detection, image improvement and morphological

operations in a variety of image archive formats.

Some other earlier studies of computer vision associated

with vegetable grading and inspection include colour and

defect sorting of bell peppers [42]. Morrow et al. [25]

presented the techniques of vision inspection ofmushrooms, apples and potatoes for size, shape andcolour. The use of computer vision for the location of

stem/root joint in carrot has also been assessed [5].

Feature extraction and pattern recognition techniques

were developed by Howarth and Searcy [17] to

characterize and classify carrots for forking, surface

defects, curvature and brokenness. The rate ofmisclassification was reported to be below 15% for the

250 samples examined. More recently sweet onions were

line scanned for internal defects using X-ray imaging

[52]. An overall accuracy of 90% was achieved when

spatial and transform features were evaluated for product

classification.

Image analysis and machine vision in general from the

foregoing can be said to offer a fast and reliable processin product sorting and grading, separation and detection

of some other facilities.

5.4. GrainsGrain quality attributes are very important for all users

and especially the milling and baking industries.

Computer vision has been used in grain qualityinspection for many years. An early study by Zayas et

al. [61] used machine vision to identify different

varieties of wheat and to discriminate wheat from non-

wheat components.

In later research Zayas et al. [62] found that wheatclassification methods could be improved by

combining morphometry (computer vision analysis)

and hardness analysis. Hard and soft recognition rates

of 94% were achieved for the seventeen varieties

examined. Twenty-three morphological features were

used for the discriminant analysis of different cereal

grains using machine vision [23]. Classification

accuracies of 98, 91, 97,100 and 91% were recorded

for CWRS (Canada Western Red Spring) wheat,

CWAD (Canada Western Amber Durum) wheat,barley, oats and rye, respectively. 25 kernels per image

were captured from a total of 6000 for each grain typeexamined.

The relationship between colour and texture features

of wheat samples to scab infection rate was studiedusing a neural network method [39]. It was found that

the infection rates estimated by the system followed

the actual ones with a correlation coefficient of 0.97

with human panel assessment and maximum and mean

absolute errors of 5 and 2%, respectively. In this study

machine vision-neural network based technique

proved superior to the human panel. Image analysis

has also been used to classify dockage components forCWRS (Canada Western Red Spring) wheat and other

cereals [27]. Morphology, colour and morphology-

colour models were evaluated for classifying the

dockage components. Mean accuracies of 89 and 96%

for the morphology model and 71 and 75% for thecolour model were achieved when tested on the testand training data sets, respectively. Overall 6000

kernels for each grain type were analyzed. Machine

vision was used to identify weeds commonly found in

wheat fields in experimentation by Zhang and

Chaisattapagon [63]. Five shape parameters were used

in leaf shape studies and were found effective indistinguishing broadleaf weed species such as

pigweed, thistle and kochia from wheat.

In order to preserve corn quality it is important to

obtain physical properties and assess mechanical

damage so as to design optimum handling and storageequipment. Measurements of kernel length, width and

projected area independent of kernel orientation have

been performed using machine vision [30]. Thealgorithm accuracy was between 0.86 and 0.89

measured by the correlation coefficient betweenpredicted results and actual sieving for a 500 g sample.

The processing time of the size-grading program was

reported as being between 0.66 and 0.74 s per kernel.

Steenhoek and Precetti [46] performed a study to


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evaluate the concept of two-dimensional image analysisfor classification of maize kernels according to size

category. A total of 320 maize kernels were categorized

into one of 16 size categories based on degree of

roundness and flatness. Classification accuracy of both

machine vision and screen systems was above 96% forround-hole analysis. However, sizing accuracy for

flatness was less than 80%.

Ng et al. [28] developed a machine vision algorithm for

corn kernel mechanical and mould damage

measurement, which demonstrated a standard deviation

less than 5% of the mean value of the 250 grains

examined. They found that this method was more

consistent than other methods available. The automatic

inspection of 600 corn kernels was also performed by Niet al. [31] using machine vision. For whole and broken

kernel identification on-line tests had successfulclassification rates of 91 and 94% for whole and broken

kernels, respectively.

The whiteness of corn has been measured by an on-line

computer vision approach by Liu and Paulsen [20]. For

the 63 samples (50-80 kernels per sample) tested the

technique was found to be easy to perform with a speed

of 3 kernels per s. In other studies Xie and Paulsen [59]

used machine vision to detect and quantify tetrazolium

staining in corn kernels. The tetrazolium-machine vision

algorithm was used to predict heat damage in corn due todrying air temperature and initial moisture content.

As rice is one of the leading food crops of the world its

quality evaluation is of importance to ensure it remains

appealing to consumers. Liu et al. [20] developed adigital image analysis method for measuring the degreeof milling of rice. They compared the method with

conventional chemical analysis and obtained a

coefficient of determination of R2

=/0.9819 for the 680

samples tested. Wan et al. [54] employed three online

classification methods for rice quality inspection:-

namely range selection, neural network and hybridalgorithms. The highest recorded online classification

accuracy was around 91% at a rate of over 1200

kernels/min. The range selection method achieved this

accuracy but required time-consuming and complicated

adjustment. In another study, milled rice from a

laboratory mill and a commercial-scale mill wasevaluated for head rice yield and percentage whole

kernels, using a shaker table and a machine-vision

system called the GrainCheck [21].

6. 3-D techniqueIn general, only 2-dimensional (2D) data are needed for

grading, classification, and analysis of most agricultural

images. However, in many applications 3-dimensional

image analysis maybe needed as information on

structure or added detail is required. A 3-D visiontechnique has been developed to derive a geometric

description from a series of 2-D images [44]. In

practice this technique might be useful for food

inspection. For example, when studying the shape

features of a piece of bakery, it is necessary to take 2-D images vertically and horizontally to obtain its

roundness and thickness, respectively.

Kanali et al. [18] investigated the feasibility of using a

charge simulation method (CSM) algorithm to process

primary image features for three dimensional shape

recognition. The required features were transferred to

a retinamodel identical to the prototype artificial retina

and were compressed using the CSM by computing

output signals at work cells located in the retina. Anoverall classification rate of 94% was obtained when

the prototype artificial retina discriminated betweendistinct shapes of oranges for the 100 data sets tested.

Gunasekaran and Ding [16] obtained 3-D images of fat

globules in cheddar cheese from 2-D images. Thisenabled the in situ 3-D evaluation of fat globule

characteristics so as the process parameters and fat

levels may be changed to achieve the required textural

qualities.

7. PROBLEMS OF AUTOMATED SORTING

AND GRADING

The major problem of automated sorting and gradingis one of socio-economic effects, which tend to reduce

employment when the number of operators required in

the processing line is reduced. It is not suitable in

processes where manual skill is necessary or

economically more attractive. It requires higher initialand maintenance cost and there may be the need for aprecise understanding of the process for programming

to achieve the required product quality Equipment

associated problem involves radiance on accurate

sensors to precisely measure process condition and the

increased risk, delays and cost if the automatic system

fails. The farm layout and very low production levelwhich may make it uneconomically viable has also

been highlighted.

8. PROSPECTS OF AUTOMATED SORTING

AND GRADING

The adoption of this emerging technology by firstputting more effort into researches on the appropriate

methods and ways of application will be of immense

benefit to this country. Some of the other associatedbenefits include increased production rates (e.g.

through optimization of equipment utilization), moreefficient operation, production of more consistent

product quality, greater product stability and safety.


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9

With the above in mind, the fruit and vegetableprocessing and marketing industries stand to gain from

this emerging technology. This is because the losses

incurred during the harvesting season on fruit are

enormous. This results from large number of products to

be handled and sold at the same time. These productsconsist of ripe and unripe fruits. The introduction of

automated sorting and grading will encourage the sorting

of the unripe (which can be kept for a relatively longer

period) from the ripe, which are to be sold immediately.

Presently the practice at the fruit market in to sell

baskets of fruits containing both ripe and unripe. These

products are found to get spoilt before they get to the

final destination.

One of the major hindrances to the introduction of thistechnique is the level of production, which remains at

the peasant level. Most of the products found in themarkets are owned by a number of individuals with each

controlling not more than 4 to 5 baskets, which will be

too small for the adoption of an automated technique.However, encouraging wholesalers and retailers for

groups and cooperatives in marketing as practiced in the

village level for maize shelling will be a way out.

The adaptation of computer vision system for quality

evaluation of processed foods is the area for the greatest

potential uptake of this technology, as analysis can be

based on a standard requirement in already automated

controlled conditions. More complex systems are neededfor the automated grading and sorting of fresh produce

because of the greater range in variability of quality and

also as produce orientation may influence results. With

the idea of precision and more environmental friendly

agriculture becoming more realistic the potential forcomputer vision system in this area is immense with theneed in field crop monitoring, assessment and guidance

systems. However, techniques such as 3D and colour

vision will ensure computer vision development

continues to meet the accuracy and quality requirements

needed in this highly competitive and changing industry.

9.CONCLUSIONSThe paper presents the recent developments in computer

vision system in the field of agricultural and food

products. Computer vision 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 vision technology in allsectors. Even though adequately efficient and accurate

algorithms have been produced, processing speeds stillfail to meet modern manufacturing requirements. With

few exceptions, research in this field has dealt with trials

on a laboratory scales thus the area of mechatronics has

been neglected, and hence it needs more focused anddetailed study.

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