Inspection and grading of agricultural and food products by computer vision systems—a review
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,
References (91)
- et al.
Computer vision determination of stem–root joint on processing carrots
Journal of Agricultural Engineering Research
(1989) - et al.
Determining the firmness of a pear using finite element model analysis
Journal of Agricultural Engineering Research
(1999) Computer vision technology for food quality assurance
Trends in Food Science and Technology
(1996)- et al.
Three-dimensional characteristics of fat globules in cheddar cheese
Journal of Dairy Science
(1999) - et al.
Three-dimensional shape recognition using a charge-simulation method to process image features
Journal of Agricultural Engineering Research
(1998) - et al.
Defects segmentation on ‘Golden Delicious’ apples by using colour machine vision
Computers and Electronics in Agriculture
(1998) - et al.
Computer vision and agricultural robotics for disease control: the potato operation
Computers and Electronics in Agriculture
(1993) - et al.
Machine vision techniques for measuring the canopy of tomato seedling
Journal of Agricultural Engineering Research
(1996) - et al.
Evaluation of pork color by using computer vision
Meat Science
(2000) - et al.
Pattern recognition of fruit shape based on the concept of chaos and neural networks
Computers and Electronics in Agriculture
(2000)
Co-occurrence matrix texture features of multi spectral images on poultry carcasses
Journal of Agricultural Engineering Research
Shape characterisation of new apple cultivars by Fourier expansion of digital images
Journal of Agricultural Engineering Research
Use of image analysis to investigate human quality classification of apples
Journal of Agricultural Engineering Research
Colour segmentation based on a light reflection model to locate citrus fruits for robotic harvesting
Computers and Electronics in Agriculture
Location and characterization of the stem-calyx area on oranges by computer vision
Journal of Agricultural Engineering Research
Computer visual tracking of poultry
Computers and Electronics in Agriculture
On-line fusion of colour camera and spectrophotometer for sugar content prediction of apples
Journal of Agricultural Engineering Research
Inspecting pizza topping percentage and distribution by a computer vision method
Journal of Food Engineering
Computer vision based hoe guidance for cereals-an initial trial
Journal of Agricultural Engineering Research
Enhancing colour differences in images of diseased mushrooms
Computers and Electronics in Agriculture
Melting characteristics of cheese: analysis of effects of cooking conditions using computer vision techniques
Journal of Food Engineering
An approach to apple surface feature detection by machine vision
Computers and Electronics in Agriculture
Apple stem and calyx identification with machine vision
Journal of Agricultural Engineering Research
Computer Vision
Study on sorting system for strawberry using machine vision (part 2): development of sorting system with direction and judgement functions for strawberry (Akihime variety)
Journal of the Japanese Society of Agricultural Machinery
Digital Image Processing Principles and Applications
How ‘machine vision’ can help drinks production
Brewing and Distilling International
Real-time colour grading and defect detection of food products. Optics in agriculture, forestry and biological processing SPIE
The International Society of Optical Engineering
Fuzzy models to predict consumer ratings for biscuits based on digital features
IEEE Transactions on Fuzzy Systems
Beef marbling and colour score determination by image processing
Journal of Food Science
Grading pistachio nuts using a neural network approach
Transactions of the ASAE
Shape feature extraction and classification of food material using computer vision
Transactions of the ASAE
Using computer vision for food quality evaluation
Food Technology
Grading of mushrooms using a machine vision system
Transactions of the ASAE
Machine vision inspection of Golden Delicious apples
Applied Engineering in Agriculture Transactions of the ASAE
Physics reises food standards
Physics World
Visual feedback guided robotic cherry tomato harvesting
Transactions of the ASAE
Colour vision in forest and wood engineering
Landwards
Cited by (334)
Evaluation of a new apple in-field sorting system for fruit singulation, rotation and imaging
2023, Computers and Electronics in AgricultureDeep Learning applied to computational biology and agricultural sciences
2022, Bioinformatics in Agriculture: Next Generation Sequencing EraIdentifying strawberry appearance quality based on unsupervised deep learning
2024, Precision Agriculture