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Individual items of any agricultural commodity are different from each other in terms of colour, shape or size. Furthermore, as they are living thing, they change their quality attributes over time, thereby making the development of accurate automatic inspection machines a challenging task. Machine vision-based systems and new optical technologies make it feasible to create non-destructive control and monitoring tools for quality assessment to ensure adequate accomplishment of food standards. Such systems are much faster than any manual non-destructive examination of fruit and vegetable quality, thus allowing the whole production to be inspected with objective and repeatable criteria. Moreover, current technology makes it possible to inspect the fruit in spectral ranges beyond the sensibility of the human eye, for instance in the ultraviolet and near-infrared regions. Machine vision-based applications require the use of multiple technologies and knowledge, ranging from those related to image acquisition (illumination, cameras, etc.) to the development of algorithms for spectral image analysis. Machine vision-based systems for inspecting fruit and vegetables are targeted towards different purposes, from in-line sorting into commercial categories to the detection of contaminants or the distribution of specific chemical compounds on the product’s surface. This chapter summarises the current state of the art in these techniques, starting with systems based on colour images for the inspection of conventional colour, shape or external defects and then goes on to consider recent developments in spectral image analysis for internal quality assessment or contaminant detection.

This study was conducted to develop a fast and accurate computer-based machine vision system for detecting irregular potatoes in real-time. Supported algorithms were specifically developed and programmed for image acquisition and processing, controlling the whole process, saving the classification results and monitoring the progress of all operations. A database of images was first formulated from potatoes with different shapes and sizes, and then some essential geometrical features such as perimeter, centroid, area, moment of inertia, length and width were extracted from each image. Also, eight shape parameters originated from size features and Fourier transform were calculated for each image in the database. All extracted shape parameters were entered in a stepwise linear discriminant analysis to extract the most important parameters that most characterized the regularity of potatoes. Based on stepwise linear discriminant analysis, two shape features (roundness and extent) and four Fourier-shape descriptors were found to be effective in sorting regular and irregular potatoes. The average correct classification was 96.5% for a training set composed of 228 potatoes and then the algorithm was validated in another testing set composed of 182 potatoes in a real-time operation. The experiments showed that the success of in-line classification of moving potatoes was 96.2%. Concurrently, the well-shaped potatoes were classified by size achieving a 100% accuracy indicating that the developed machine vision system has a great potential in automatic detection and sorting of misshapen products. (c) 2012 Elsevier Ltd. All rights reserved.

The best alternative for reducing citrus production costs is mechanization. Machine vision is a reliable technology for the automatic inspection of fresh fruits and vegetables that can be adapted to harvesting machines. In these, fruits can be inspected before sending them to the packinghouse and machine vision provides important information for subsequent processing and avoids spending further resources in non-marketable fruit. The present work describes a computer vision system installed on a harvesting machine developed jointly by IVIA and a Spanish enterprise. In this machine, hand pickers directly drop the fruit as they collect it, which results in an important increase of productivity. The machine vision system is placed over rollers in order to inspect the produce, and separate those that can be directly sent to the fresh market from those that do not meet minimal quality requirements but can be used by the processing industry, based on color, size and the presence of surface damages. The system was tested under field conditions.

BACKGROUND: The sterile insect technique (SIT) is acknowledged around the world as an effective method for biological pest control of Ceratitis capitata (Wiedemann). Sterile insects are produced in biofactories where one key issue is the selection of the progenitors that have to transmit specific genetic characteristics. Recombinant individuals must be removed as this colony is renewed. Nowadays, this task is performed manually, in a process that is extremely slow, painstaking and labour intensive, in which the sex of individuals must be identified. The paper explores the possibility of using vision sensors and pattern recognition algorithms for automated detection of recombinants. RESULTS: An automatic system is proposed and tested to inspect individual specimens of C. capitata using machine vision. It includes a backlighting system and image processing algorithms for determining the sex of live flies in five high-resolution images of each insect. The system is capable of identifying the sex of the flies by means of a program that analyses the contour of the abdomen, using fast Fourier transform features, to detect the presence of the ovipositor. Moreover, it can find the characteristic spatulate setae of males. Simulation tests with 1000 insects (5000 images) had 100% success in identifying male flies, with an error rate of 0.6% for female flies. CONCLUSION: This work establishes the basis for building a machine for the automatic detection and removal of recombinant individuals in the selection of progenitors for biofactories, which would have huge benefits for SIT around the globe. Copyright © 2008 Society of Chemical Industry

The quality standards of citrus for fresh consumption are mainly based on the absence of bruises and rotting, as well as an adequate shape, color, and size. Current industrial systems for inspection require the processing of images in a few milliseconds, inspecting the whole surface of the fruit moving on a conveyor belt. Most of them are limited to the visible region of the electromagnetic spectrum, but the identification of defects is an important task that still remains unresolved. Information provided by near infrared or ultraviolet regions of the spectrum may improve the accuracy of fruit identification and the detection of dangerous damages or diseases that evolve or are invisible to our eyes. Furthermore, some techniques open the possibility of inspecting the internal parts of the fruits. This chapter summarizes research related to the application of machine vision, including different imaging technologies and techniques for the automated inspection of the quality of citrus fruits.

The presence of skin defects is one of the most influential factors in the price of fruit. The detection of defects during packing operations ensures that only fruits with a good quality reach the market. Moreover, the identification of the type of each defect will increase both the quality of the fruit and also the producer's profit. At the present time, fruit with slight defects is marketed together with sound fruit, thus depreciating the quality of the batch, or it is removed together with seriously damaged fruit, thereby causing economic losses. Most current computer vision systems used in the automatic quality inspection of food are limited to the visible region of the electromagnetic spectrum as they tend to imitate the human eye. However, non-visible information, such as that provided by near-infrared or ultraviolet regions of the spectrum, can improve the inspection by detecting specific defects or allowing the detection of non-visible damages. This work summarises our research in the application of near-infrared, ultraviolet and fluorescence computer vision systems in the identification of the most common defects of citrus fruits, and proposes a fruit sorting algorithm that combines this different spectral information (including visible) to classify fruit according to the type of defect. Results showed that the contribution of non-visible information can improve the detection and identification of some defects. Compared with the results from colour images, the detection accuracy of anthracnose increased from 86% by using NIR images; and the accuracy of green mould was increased from 65% to 94% by using images of fluorescence. (c) 2007 Elsevier Ltd. All rights reserved.

Current grading lines for fresh fruit use sensors that measure weight, size, sometimes colour or firmness. However, none of them measures so far an important organoleptic criteria for the consumer: sugar content. Image analysis can provide colour information on the fruit, while near infrared spectrophotometric data can be used in order to determine sugar content. Sensor fusion methodology is aimed at improving the sugar content prediction by combining both on-line non-destructive sensors. The fusion process began by a diagnosis of each sensor, followed by a neural network modelisation that uses the information provided by the sensors. It was found that the repeatability of the classification of the fruits based on sugar content was improved when two sensors were combined. The sensors and the fusion process were implemented on-line within a robotics device currently running at 3.5 seconds per fruit., Les lignes de tri actuelles destinées à l'évaluation de la qualité des fruits frais utilisent des capteurs qui mesurent le poids, la taille, et parfois la couleur ou la fermeté des fruits. Toutefois, aucune de ces lignes n'est équipée d'un système de mesure du paramètre organoleptique le plus important pour le consommateur: la teneur en sucres. L'analyse d'image peut fournir une information colorimétrique du fruit, tandis que la spectrophotométrie peut être utilisée pour déterminer la teneur en sucres de manière non-destructive. La fusion de capteurs vise à améliorer la prédiction non destructive de la teneur en sucres, en combinant ces deux capteurs en ligne. La fusion s'opère en deux temps; dans un premier temps, un diagnostic est réalisé sur l'état de la mesure fournie par chaque capteur, puis un réseau de neurones combine les informations fournies par chacun des capteurs. Les résultats démontrent une plus grande répétabilité de la classification des fruits à partir de leur teneur en sucres, lorsque la fusion de capteurs est mise en oeuvre. Les capteurs et le système de fusion ont été installés en ligne au sein d'un système robotisé fonctionnant actuellement à environ 3.5 s par fruit.

Publisher Summary Some of the most important aspects that need to be taken into consideration when developing a hyperspectral inspection system for citrus include the geometry of the fruit, the emission spectrum of the lighting source, and their interaction. Because many citrus fruits are almost spherical, each point of their surface reflects the electromagnetic radiation differently toward the camera. This causes a gradual darkening of the image especially the further pixels from the light source, which is a phenomenon that must be artificially corrected. In addition, the variation of the efficiency of the filters with the wavelength should be also taken into consideration to enable the appropriate corrections to obtain true reflectance images. Hyperspectral systems are an important tool for the quality inspection of citrus fruits, offering the possibility of designing machines for the automatic identification of blemishes. This is particularly important for early rot detection, one of the major problems faced by this sector. However, a realistic implementation of such systems probably still requires an important effort in adequately reducing the number of input bands.

The Bayesian approach to classification is intended to solve questions concerning how to assign a class to an observed pattern using probability estimations. Red, green and blue (RGB) or hue, saturation and lightness (HSL) values of pixels in digital colour images can be considered as feature vectors to be classified, thus leading to Bayesian colour image segmentation. Bayesian classifiers are also used to sort objects but, in this case, reduction of the dimensionality of the feature vector is often required prior to the analysis. This chapter shows some applications of Bayesian learning techniques in computer vision in the agriculture and agri-food sectors. Inspection and classification of fruit and vegetables, robotics, insect identification and process automation are some of the examples shown. Problems related with the natural variability of colour, sizes and shapes of biological products, and natural illuminants are also discussed. Moreover, implementations that lead to real-time implementation are explained.

The pomegranate is a fruit with excellent organoleptic and nutritional properties, but the fact that it is difficult to peel affects its commercialisation and decreases its potential consumption. One solution is to market the arils of pomegranate in a ready-to-eat form. However, after the peeling process, unwanted material, such as internal membranes and defective arils, is extracted together with good arils and must be removed on the packing line because the presence of such material shortens the shelf life of the product or deteriorates its appearance. For different reasons, the commercial sorting machines that are currently available for similar commodities (cherries, nuts, rice, etc.) are not capable of handling and sorting pomegranate arils, thus making it necessary to build specific equipment. This work describes the development of a computer vision-based machine to inspect the raw material coming from the extraction process and classify it in four categories. The machine is capable of detecting and removing unwanted material and sorting the arils by colour. The prototype is composed of three units, which are designed to singulate the objects to allow them be inspected individually and sorted. The inspection unit relies on a computer vision system. Two image segmentation methods were tested: one uses a threshold on the R/G ratio and the other is a more complex approach based on Bayesian Linear Discriminant Analysis (LDA) in the RGB space. Both methods offered an average success rate of 90% on a validation set, the former being more intuitive for the operators, as well as faster and easier to implement, and for these reasons it was included in the prototype. Subsequently, the complete machine was tested in industry by working in real conditions throughout a whole pomegranate season, in which it automatically sorted more than nine tons of arils.

The detection of rotten citrus in packing lines is carried out manually under ultraviolet illumination, which is dangerous for workers. Light emitted by the rotten region of the fruit due to the ultraviolet-induced fluorescence is used by the operator to detect the damages. This procedure is required because the low contrast between the damaged and sound skin under visible illumination difficult their detection. We study a set of techniques aimed to detect rottenness in citrususing visible and near infrared lighting trough an hyperspectral imaging system. Methods for selecting a proper set of wavelengths are investigated such as correlation analysis, mutual information, stepwise or genetic algorithms. The image segmentation relies on the combination of band selection techniques and pixel classification methods such as classification and regression trees and linear discriminant analysis.

This study proposes a method for correcting the adverse effects produced by the curvature of spherical objects in acquiring images with a computer vision system. Its suitability has been illustrated in a specific case of citrus fruits. The images of this kind of fruit are darker in areas nearer the edge than in the centre, and this makes them more difficult to analyse. This methodology considers the fruit as being a Lambertian ellipsoidal surface and produces a 3D model of the fruit. By doing it becomes possible to calculate the part of the radiation that should really reach the camera and to make the intensity of the radiation uniform over the whole of the fruit surface captured by the camera, no matter what region is being sampled. Some tests have been carried out in order to prove that using the proposed correction methodology the reflectance in all the surface of the fruit is similar, minimising the differences from the central area to the peripheral areas. The methodology presented here has been tested using a hyperspectral computer vision system based on tunable liquid crystal filters and it has proved to be effective for minimising the adverse side effects produced by the curvature of the fruit on the intensity of the radiation captured by the camera. The experiments show that applying our method homogenises the grey level of the pixels belonging to the same class, regardless of the region of the fruit surface they are from; standard deviation is also reduced, which facilitates subsequent classification tasks. (c) 2007 Elsevier Ltd. All rights reserved.

Nowadays, the detection of fruit infected with Penicillium sp. fungi on packing lines is carried out manually under ultraviolet illumination. Ultraviolet sources induce visible fluorescence of essential oils, present in the skin of citrus and which are released by the action of fungi, thus increasing the contrast between sound and rotten skin. This work analyses a set of techniques aimed at detecting rotten citrus without the use of UV lighting. The techniques used include hyperspectral image acquisition, pre-processing and calibration, feature selection and segmentation using linear and non-linear methods for classification of fruits. Different methods such as correlation analysis, mutual information, stepwise, and genetic algorithms based on linear discriminant analysis (LDA) are studied to select the most relevant bands. Image segmentation relies on the combination of efficient band selection techniques and also on pixel classification methods such as classification and regression trees (CART) and LDA. The results were obtained using a large dataset of images of mandarins cv. “Clemenules” by applying the CART method. The hyperspectral computer vision system proposed here is capable of detecting damage caused by Penicillium digitatum in mandarins using a reduced set of optimally selected bands.

This work focuses on the development of a computer vision system for the automatic on-line inspection and classification of Satsuma segments. During the image acquisition the segments are in movement, wet and frequently in contact with other pieces. The segments are transported over six semi-transparent conveyor belts that advance at speed of 1 m/s. During on-line operation, the system acquires images of the segments using two cameras connected to a single computer and process the images in less than 50 ms. Extracting morphological features from the objects, the system identifies automatically pieces of skin and row material and separates entire segments from broken ones, discriminating between those with slight or large breaking degree. Combinations of morphological parameters were employed to decide the quality of each segment, classifying correctly 95% of sound segments.

Presented is a full computer vision system for the identification of post-harvest damage in citrus packing houses. The method is based on the combined use of hyperspectral images and the Mahalanobis kernel classifier. More accurate and reliable results compared to other methods are obtained in several scenarios and acquired images.

Due to the high sorting speed required during fruit inspection and classification in packing lines, most of the current automatic systems, based on machine vision, normally employ supervised techniques oriented towards individual pixels to segment the images of the fruits. These techniques require previous training given by experts in order to classify the colour of each pixel as belonging to any of the regions of interest and frequent training sessions during normal operation throughout the season to adapt the system to the great colour variability present in biological products like fruits. In region-oriented segmentation algorithms, however, the contrast between different areas in the image becomes more important than the individual pixel colour, thus solving the problem related to the variability of the natural colour of fruits. This work proposes a region-oriented segmentation algorithm for detecting the most common peel defects of citrus fruits. Focused on the detection of the regions of interest consisting of the sound peel, the stem and the defects, this method is an original contribution that allows successful segmentation of smaller defects, such as scale. The algorithm was tested on images of different varieties of oranges and mandarins presenting defects, without any further training being given between inspections of different batches, even species, of citrus fruits. Assuming that the most surface of the fruit corresponds to sound peel, the proposed algorithm was able to correctly detect 95% of the defects under study. (c) 2006 Elsevier Ltd. All rights reserved.

Artificial vision systems are powerful tools for the automatic inspection of fruits and vegetables. Typical target applications of such systems include grading, quality estimation from external parameters or internal features, monitoring of fruit processes during storage or evaluation of experimental treatments. The capabilities of an artificial vision system go beyond the limited human capacity to evaluate long-term processes objectively or to appreciate events that take place outside the visible electromagnetic spectrum. Use of the ultraviolet or near-infrared spectra makes it possible to explore defects or features that the human eye is unable to see. Hyperspectral systems provide information about individual components or damage that can be perceived only at particular wavelengths and can be used as a tool to develop new computer vision systems adapted to particular objectives. In-line grading systems allow huge amounts of fruit or vegetables to be inspected individually and provide statistics about the batch. In general, artificial systems not only substitute human inspection but also improve on its capabilities. This work presents the latest developments in the application of this technology to the inspection of the internal and external quality of fruits and vegetables.

Three image analysis methods were studied and evaluated to solve the problem of removing long stems attached to mechanically harvested oranges: colour segmentation based on linear discriminant analysis, contour curvature analysis, and a thinning process which involves iterating until the stem becomes a skeleton. These techniques are able to determine the presence or absence of a stem with certainty, to locate the stems from random views with more than 90% accuracy and from profile images with an accuracy ranging from 92.4% to 100% depending on the method used. Finally, determination of the length and cutting point of the stem is achieved with only 3.8% of failures. (C) 1996 Silsoe Research Institute

With the aim of ensuring the quality and homogenising the colour of commercial batches of pomegranate arils, a new prototype for the automatic inspection of pomegranate arils and their separation into categories by means of air ejectors has been developed. Arils are singulated and transported on conveyor belts. Images of the arils are acquired using two cameras to allow estimation of the quality and are then expelled through different outlets according to the category. New control software were developed that synchronise the advance of the conveyor belts, the acquisition of images and the separation by air ejectors, which also includes mechanisms for the synchronisation and communication between the vision and control computers. A new computer vision system has been developed for the inspection of arils that distinguish between arils and raw material by estimating the colour of the objects and then classify the arils by size and colour. The prototype was tested over a six-month period in a Spanish pomegranate producer, with the inspection of about 5 Mg of product. The new prototype achieves the objective of inspecting and sorting pomegranate arils according to their quality, reaching the performance specifications.

Spain is a major producer of citrus fruit, which must be correctly classified depending on its external quality. Current commercial sorters based on machine vision cannot solve problems like defect detection or correct colour classification due to the low image resolution, which is necessary to achieve adequate production speed. This paper describes a new machine vision system to classify different species of citrus in real time, attending to external quality features of the fruits as size, colour and external defects. A specific hardware has been developed to run the algorithms in parallel, which makes possible work at speed of 10 fruits/s, with an adequate image resolution.