Application of Hyperspectral Technique for Color Classification Avocados Subjected to Different Treatments

Moltó

et al. 2010

430

187

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.

Section: Image Acquisitionmentioning

confidence: 99%

Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables

Cubero

Moltó

et al. 2010

430

187

“…Hyperspectral reflectance imaging was used by Karimi et al (2009) to study the changes in reflectance (350-2500 nm) of avocados coated with different formulations. Wang et al (2011) used a reflectance hyperspectral system (400-720 nm) for the detection of external insect damage in jujube fruits.…”

Section: Estimation Of External Quality Parametersmentioning

confidence: 99%

Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment

Lorente

Gómez-Sanchís

et al. 2011

453

190

Hyperspectral imaging systems are starting to be used as a scientific tool for food quality assessment. A typical hyperspectral image is composed of a set of a relatively wide range of monochromatic images corresponding to continuous wavelengths that normally contain redundant information or may exhibit a high degree of correlation. In addition, computation of the classifiers used to deal with the data obtained from the images can become excessively complex and time consuming for such high dimensional data sets and this makes it difficult to incorporate such systems into an industry that demands standard protocols or high-speed processes. Therefore, recent works have focused on the development of new systems based on this technology that are capable of analysing quality features that cannot be inspected using visible imaging. Many of those studies have also centred on finding new statistical techniques to reduce the hyperspectral images to multispectral ones, which are easier to implement in automatic, nondestructive systems. This article reviews recent works that use hyperspectral imaging for the inspection of fruit and vegetables. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of internal and external features of these products. Particular attention is paid to the works aimed at reducing the dimensionality of the images, with details of the statistical techniques most commonly used for this task.

“…Computer vision has become widely used to automate the inspection of all different types of food commodities like meat (Du and Sun 2009), fish , bakery products (FarreraRebollo et al 2011), grains (Manickavasagan et al 2010) or fruits (Karimi et al 2009). In most cases, its use is aimed at the inspection of external features related with quality, such as size, shape, colour or the presence of damage .…”

Section: Introductionmentioning

confidence: 99%

Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks

Lorente

Gómez-Sanchís

et al. 2011

119

Early automatic detection of fungal infections in post-harvest citrus fruits is especially important for the citrus industry because only a few infected fruits can spread the infection to a whole batch during operations such as storage or exportation, thus causing great economic losses. Nowadays, this detection is carried out manually by trained workers illuminating the fruit with dangerous ultraviolet lighting. The use of hyperspectral imaging systems makes it possible to advance in the development of systems capable of carrying out this detection process automatically. However, these systems present the disadvantage of generating a huge amount of data, which must be selected in order to achieve a result that is useful to the sector. This work proposes a methodology to select features in multiclass classification problems using the receiver operating characteristic curve, in order to detect rottenness in citrus fruits by means of hyperspectral images. The classifier used is a multilayer perceptron, trained with a new learning algorithm called extreme learning machine. The results are obtained using images of mandarins with the pixels labelled in five different classes: two kinds of sound skin, two kinds of decay and scars. This method yields a reduced set of features and a classification success rate of around 89%.