Inspection and grading of agricultural and food products by computer vision systems—a review

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Abstract

Computer vision is a rapid, economic, consistent and objective inspection technique, which has expanded into many diverse industries. Its speed and accuracy satisfy ever-increasing production and quality requirements, hence aiding in the development of totally automated processes. This non-destructive method of inspection has found applications in the agricultural and food industry, including the inspection and grading of fruit and vegetable. It has also been used successfully in the analysis of grain characteristics and in the evaluation of foods such as meats, cheese and pizza. This paper reviews the progress of computer vision in the agricultural and food industry, then identifies areas for further research and wider application the technique.

Introduction

Computer vision is a relatively young discipline with its origin traced back to the 1960s (Baxes, 1994). Following an explosion of interest during the 1970s, it has experienced continued growth both in theory and application. Sonka et al. (1999) reported that more than 1000 papers are published each year in the expanding fields of computer vision and image processing. Applications of these techniques have now expanded to various areas such as medical diagnostic, automatic manufacturing and surveillance, remote sensing, technical diagnostics, autonomous vehicle and robot guidance.

Computer vision is the construction of explicit and meaningful descriptions of physical objects from images (Ballard and Brown, 1982). Timmermans (1998) states that it encloses the capturing, processing and analysis of two-dimensional images, with others noting that it aims to duplicate the effect of human vision by electronically perceiving and understanding an image (Sonka et al., 1999). The basic principle of computer vision is described in Fig. 1. Image processing and image analysis are the core of computer vision with numerous algorithms and methods available to achieve the required classification and measurements (Krutz et al., 2000).

Computer vision systems have been used increasingly in the food and agricultural industry for inspection and evaluation purposes as they provide suitably rapid, economic, consistent and objective assessment (Sun, 2000). They have proved to be successful for the objective measurement and assessment of several agricultural products (Timmermans, 1998). Over the past decade advances in hardware and software for digital image processing have motivated several studies on the development of these systems to evaluate the quality of diverse and processed foods (Locht et al., 1997, Gerrard et al., 1996). Computer vision has long been recognised as a potential technique for the guidance or control of agricultural and food processes (Tillett, 1990). Therefore, over the past 20 years, extensive studies have been carried out, thus generating many publications.

The majority of these studies focused on the application of computer vision to product quality inspection and grading. Traditionally, quality inspection of agricultural and food products has been performed by human graders. However, in most cases these manual inspections are time-consuming and labour-intensive. Moreover the accuracy of the tests cannot be guaranteed (Park et al., 1996). By contrast it has been found that computer vision inspection of food products, was more consistent, efficient and cost effective (Lu et al., 2000, Tao et al., 1995a). Also with the advantages of superior speed and accuracy, computer vision has attracted a significant amount of research aimed at replacing human inspection. Recent research has highlighted the possible application of vision systems in other areas of agriculture, including the analysis of animal behaviour (Sergeant et al., 1998), applications in the implementation of precision farming and machine guidance (Tillett and Hague, 1999), forestry (Krutz et al., 2000) and plant feature measurement and growth analysis (Warren, 1997).

Besides the progress in research, there is increasing evidence of computer vision systems being adopted at commercial level. This is indicated by the sales of ASME (Application Specific Machine Vision) systems into the North American food market, which reached 65 million dollars in 1995 (Locht et al., 1997). Gunasekaran (1996) reported that the food industry is now ranked among the top ten industries using machine vision technology. This paper reviews the latest development of computer vision technology with respect to quality inspection in the agricultural and food industry.

Section snippets

Assessment of fruits and nuts

Computer vision has been widely used for the inspection and grading of fruits. It offers the potential to automate manual grading practices and thus to standardise techniques and eliminate tedious inspection tasks. Kanali et al. (1998) reported that the automated inspection of produce using machine vision not only results in labour savings, but can also improve inspection objectivity.

Mushrooms

Computer vision has been shown to be a viable approach to inspection and grading of vegetables (Shearer and Payne, 1990). Heinemann et al. (1994) assessed the quality features of the common white Agaricus bisporus mushroom using image analysis in order to inspect and grade the mushrooms by an automated system. Of the 25 samples examined misclassification by the vision system ranged from 8 to 56% depending upon the quality feature evaluated, but averaged about 20%. The study also reported that

Wheat

Grain quality attributes are very important for all users and especially the milling and baking industries. Computer vision has been used in grain quality inspection for many years. An early study by Zayas et al. (1989) used machine vision to identify different varieties of wheat and to discriminate wheat from non-wheat components. In later research Zayas et al. (1996) found that wheat classification methods could be improved by combining morphometry (computer vision analysis) and hardness

Pizza

Visual features such as colour and size indicate the quality of many prepared consumer foods. Sun (2000) investigated this in research on pizza in which pizza topping percentage and distribution were extracted from pizza images. A new segmentation algorithm was developed by combining three algorithms used to segment many different types of pizzas as the traditional segmentation techniques were found to be inadequate for this application. Fig. 3(a) and (b) shows a sample image before and after

3-D technique

In 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 vision technique has been developed to derive a geometric description from a series of 2-D images (Sonka et al., 1999). In practice this technique might be useful for food inspection. For example, when studying the shape features of a piece

Advantages and disadvantages

Table 2 summarises the advantages and disadvantages of computer vision to different sectors of the agricultural and horticultural industries. The capabilities of digital image analysis technology to generate precise descriptive data on pictorial information have contributed to its more widespread and increased use (Sapirstein, 1995). Quality control in combination with the increasing automation in all fields of production has led to the increased demand for automatic and objective evaluation of

Conclusions

The paper reviews the recent developments in computer vision for the agricultural and food industry. 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 all sectors. Even though adequately efficient and accurate algorithms have been produced,

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