Multispectral images of peach related to firmness and maturity at harvest

wo multispectral maturity classifications for red soft-flesh peaches (‘Kingcrest’, ‘Rubyrich’ and ‘Richlady’ n = 260) are proposed and compared based on R (red) and R/IR (red divided by infrared) images obtained with a three CCD camera (800 nm, 675 nm and 450 nm). R/IR histograms were able to correct the effect of 3D shape on light reflectance and thus more Gaussian histograms were produced than R images. As fruits ripened, the R/IR histograms showed increasing levels of intensity. Reference measurements such as firmness and visible spectra also varied significantly as the fruit ripens, firmness decreased while reflectance at 680 nm increased (chlorophyll absorption peak)

VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits

[EN] In this work an N-way partial least squares regression discriminant analysis (NPLS-DA) methodology is developed to detect symptoms of disease caused by Penicillium digitatum in citrus fruits (green mould) using visible/near infrared (VIS/NIR) hyperspectral images. To build the discriminant model a set of oranges and mandarins was infected by the fungus and another set was infiltrated just with water for control purposes. A double cross-validation strategy is used to validate the discriminant models. Finally, permutation testing is used to select a few bands offering the best correct classification rates in the validation set. The discriminant models developed here can be potentially implemented in a fruit packinghouse to detect infected citrus fruits at their arrival from the field with affordable multispectral (3 5 channels) cameras installed in the packinglines.This research was partially funded by the Spanish Ministry of Science and Innovation through grants DPI2011-28112-C04-02 and DPI2014-55276-C05-1R, and by INIA through grant RTA2012-00062-C04-01. In all cases with the support of European FEDER funds. Authors thank Lluis Palou from the Centro de Tecnologia Postcosecha at the IVIA for the help and supervision in the innoculation process of the fruits.Folch Fortuny, A.; Prats-Montalbán, JM.; Cubero-García, S.; Blasco Ivars, J.; Ferrer, A. (2016). VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits. Chemometrics and Intelligent Laboratory Systems. 156:241-248. https://doi.org/10.1016/j.chemolab.2016.05.005S24124815

Sensors for product characterization and quality of specialty crops—A review

This review covers developments in non-invasive techniques for quality analysis and inspection of specialty crops, mainly fresh fruits and vegetables, over the past decade up to the year 2010. Presented and discussed in this review are advanced sensing technologies including computer vision, spectroscopy, X-rays, magnetic resonance, mechanical contact, chemical sensing, wireless sensor networks and radiofrequency identification sensors. The current status of different sensing systems is described in the context of commercial application. The review also discusses future research needs and potentials of these sensing technologies. Emphases are placed on those technologies that have been proven effective or have shown great potential for agro-food applications. Despite significant progress in the development of non-invasive techniques for quality assessment of fruits and vegetables, the pace for adoption of these technologies by the specialty crop industry has been slow

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers\u27 expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects\u27 attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import-export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods\u27 application in the detection and classification of insect infestation in fruits and vegetables

Review: computer vision applied to the inspection and quality control of fruits and vegetables

This is a review of the current existing literature concerning the inspection of fruits and vegetables with the application of computer vision, where the techniques most used to estimate various properties related to quality are analyzed. The objectives of the typical applications of such systems include the classification, quality estimation according to the internal and external characteristics, supervision of fruit processes during storage or the evaluation of experimental treatments. In general, computer vision systems do not only replace manual inspection, but can also improve their skills. In conclusion, computer vision systems are powerful tools for the automatic inspection of fruits and vegetables. In addition, the development of such systems adapted to the food industry is fundamental to achieve competitive advantages

Automatic early detection of decay in citrus fruit using optical technologies and machine learning techniques

Los cítricos representan el cultivo frutal de mayor valor en términos de comercio internacional, siendo España el primer exportador mundial de cítricos para consumo en fresco. Sin embargo, la presencia de podredumbres causadas por hongos del género Penicillium se encuentra entre los principales problemas que afectan la postcosecha y comercialización de cítricos. Un número reducido de frutas infectadas puede contaminar una partida completa de cítricos durante el almacenamiento de la fruta por largos períodos de tiempo o en el transporte al extranjero, lo que conlleva grandes pérdidas económicas y el desprestigio de los productores de cítricos. Por lo tanto, la detección temprana de infecciones por hongos de forma efectiva y la eliminación de la fruta infectada son asuntos de especial interés en los almacenes de confección de fruta para impedir la propagación de las infecciones fúngicas, asegurando de esta forma una excelente calidad de la fruta y la ausencia total de fruta infectada. En este sentido, la presente tesis doctoral se centra en abordar un reto tan importante para la industria citrícola como es la automatización del proceso de detección de podredumbres incipientes, con el fin de proporcionar alternativas a la inspección manual con peligrosa luz ultravioleta que permitan realizar esta detección de forma más eficiente y, en consecuencia, reducir potencialmente el uso de fungicidas. En concreto, esta tesis doctoral avanza en el campo de la detección automática de podredumbres en cítricos mediante sistemas ópticos y técnicas de aprendizaje automático. Específicamente, se investigan tres técnicas ópticas diferentes que operan en las regiones del visible e infrarrojo cercano del espectro electromagnético, incluyendo la técnica de imagen basada en backscattering, visión hiperespectral y espectroscopía. Los sistemas ópticos usados en esta tesis no están limitados a la parte visible del espectro, por lo que sus capacidades superan a las del ojo humano y a las de los sistemas de visión convencionales basados en cámaras de color, lo cual resulta de especial interés para detectar daños en cítricos que son difícilmente visibles a simple vista, como las podredumbres en estadios tempranos de infección. Además, se exploran numerosas técnicas de aprendizaje automático de reducción de la dimensionalidad de los datos y clasificación, con la finalidad de usar las medidas ópticas de los cítricos para discriminar la fruta afectada por podredumbre de la fruta sana. Las tres técnicas ópticas, junto con métodos de aprendizaje automático adecuados, proporcionan buenos resultados en la clasificación de la piel de los frutos cítricos en sana o podrida, consiguiendo un porcentaje de muestras bien clasificadas superior al 90% para ambas clases, a pesar de la gran similitud entre ellas. En vista de los resultados obtenidos, esta tesis doctoral sienta las bases para la futura implementación de las técnicas ópticas estudiadas en un sistema comercial de clasificación automática de fruta destinado a la detección de podredumbres en cítricos.Citrus fruit is the highest value fruit crop in terms of international trade, with Spain being the first worldwide exporter of citrus fruit for fresh consumption. However, the presence of decay caused by Penicillium spp. fungi is among the main problems affecting postharvest and marketing processes of citrus fruit. A small number of decayed fruit can infect a whole consignment, during long-term storage or fruit shipping to export markets, thus involving enormous economic losses and the blackening of the reputation of citrus producers. Therefore, effective early detection of fungal infections and removal of infected fruit are issues of major concern in commercial packinghouses in order to prevent the spread of the infections, thus ensuring an excellent fruit quality and absolute absence of infected fruit. In this respect, this doctoral thesis focuses on addressing such an important challenge for the citrus industry as the automation of the detection of early symptoms of decay, in order to provide alternatives to human inspection under dangerous ultraviolet illumination, thus accomplishing this detection task more efficiently and, consequently, leading to a possible reduction of the use of fungicides. Specifically, this doctoral thesis advances in the field of the automatic detection of decay in citrus fruit using optical systems and machine learning methods. In particular, three different optical techniques operating in the visible and near-infrared spectral regions are investigated, including hyperspectral imaging, light backscattering imaging and spectroscopy. The optical systems used in this thesis are not limited to the visible part of the electromagnetic spectrum, thus presenting capabilities beyond those of the naked human eye and traditional computer vision systems based on colour cameras, this fact being of special interest for detecting hardly-visible damage in citrus fruit, such as decay at early stages. Furthermore, a vast number of machine learning techniques aimed at data dimensionality reduction and classification are explored for dealing with the optical measurements of citrus fruit in order to discriminate fruit with symptoms of decay from sound fruit. The three optical techniques, coupled with suitable machine learning methods, investigated in this doctoral thesis provide good results in the classification of skin of citrus fruit into sound or decaying, with a percentage of well-classified samples above 90% for both classes despite their similarity. In the light of the results, this doctoral thesis lays the foundation for the future establishment of the explored optical technologies on a commercial fruit sorter aimed at decay detection in citrus fruit

Mechanical damage characteristics and nondestructive testing techniques of fruits: a review

Abstract Fruits will be subjected inevitably to various external forces in the process of harvesting, transportation, processing, and storage, which will cause mechanical damage. The research on mechanical properties and damage mechanisms of fruit can effectively control its loss. In this study, fruits are divided into different types according to their morphology and structure. The impact, vibration, static pressure, and other mechanical damage on fruits are studied. It is important to identify the damaged parts of fruit after damage quickly and accurately. Therefore, this study analyzes the application of nondestructive testing technologies such as spectral detection technology, NMR (nuclear magnetic resonance) detection technology, and acoustic and electrical characteristics detection technology in fruit damage detection

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

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 datasets, 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, non-destructive 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 the 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