Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest - A review

[EN] Computer vision systems are becoming a scientific but also a commercial tool for food quality assessment. In the field, these systems can be used to predict yield, as well as for robotic harvesting or the early detection of potentially dangerous diseases. In postharvest handling, it is mostly used for the automated inspection of the external quality of the fruits and for sorting them into commercial categories at very high speed. More recently, the use of hyperspectral imaging is allowing not only the detection of defects in the skin of the fruits but also their association to certain diseases of particular importance. In the research works that use this technology, wavelengths that play a significant role in detecting some of these dangerous diseases are found, leading to the development of multispectral imaging systems that can be used in industry. This article reviews recent works that use colour and non-standard computer vision systems for the automated inspection of citrus. 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 fruits. Particular attention is paid to inspection for the early detection of some dangerous diseases like citrus canker, black spot, decay or citrus Huanglongbing.This work was supported by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA) through projects RTA2012-00062-C04-01 and RTA2012-00062-C04-03 with the support of European FEDER funds. The authors would like to thank and acknowledge the contributions that were made by all the students, postdocs, technicians and visiting scholars in the Precision Agriculture Laboratory at the University of Florida and the Computer Vision Laboratory at the Agricultural Engineering Centre of IVIA.Cubero García, S.; Lee, WS.; Aleixos Borrás, MN.; Albert Gil, FE.; Blasco Ivars, J. (2016). 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Development of a Hyperspectral Computer Vision System Based on Two Liquid Crystal Tuneable Filters for Fruit Inspection. Application to Detect Citrus Fruits Decay

Hyperspectral systems are characterised by offering the possibility of acquiring a large number of images at different consecutive wavebands. To ensure reliable and repeatable results using this kind of optical sensors, the intensity shown by the objects in the different spectral images must be independent from the differences in sensitivity of the system for the different wavelengths. The spectral efficiency of the acquisition devices and the spectral emission of the lighting system vary across the spectrum and the images, and therefore the results can reproduce these variations if the system is not properly calibrated and corrected. This is particularly complex, when several LCTF devices are used to obtain large spectral ranges. This work presents the development of a hyperspectral system based on two liquid crystal tuneable filters for the acquisition of images of spherical fruits. It also proposes a methodology for acquiring and segmenting images of citrus fruits aimed at detecting decay in citrus fruits that has been capable of correctly classifying 98 % of pixels as rotten or non-rotten and 95 % of fruit

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

Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables

Over the past decade, hyperspectral imaging has been rapidly developing and widely used as an emerging scientific tool in nondestructive fruit and vegetable quality assessment. Hyperspectral imaging technique integrates both the imaging and spectroscopic techniques into one system, and it can acquire a set of monochromatic images at almost continuous hundreds of thousands of wavelengths. Many researches based on spatial image and/or spectral image processing and analysis have been published proposing the use of hyperspectral imaging technique in the field of quality assessment of fruits and vegetables. This chapter presents a detailed overview of the introduction, latest developments and applications of hyperspectral imaging in the nondestructive assessment of fruits and vegetables. Additionally, the principal components, basic theories, and corresponding processing and analytical methods are also reported in this chapter

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

Advance in Citrus Postharvest Management: Diseases, Cold Storage and Quality Evaluation

Citrus is a fruit crop grown in different Mediterranean countries. Generally, harvested fruits are used for fresh consumption or are processed (mainly to produce juices). In this chapter, the authors discuss the state of art on citrus postharvest with a scientific approach, evaluating the current knowledge about the physiology and pathology of citrus fruits and the main causes of deterioration. In addition, the authors explain the main facilities for the cold storage of citrus fruit with particular reference to the rapid-cooling techniques and treatments needed prior to shipment of citrus fruits (refer shipment). In the last part of the chapter, the non-destructive methods for the quality evaluation are presented

Postharvest Technologies of Fresh Citrus Fruit: Advances and Recent Developments for the Loss Reduction during Handling and Storage

Citrus spp. are spread mainly in the Mediterranean basin and represent the largest fruit source for human consumption. Postharvest losses, mainly due to diseases and metabolic disorders of fruits, can cause severe wastage, reaching 30 to 50% of the total production. Preserving quality and extending shelf life are essential objectives for postharvest technological innovation, determined by the proper handling, treatment, storage and transport of harvested produce. Moreover, the application of novel sustainable strategies is critical for the reduction of synthetic fungicide residues on fruit surfaces and the impact on the environment caused by waste disposal of fungicides. In this article, the current knowledge about the safest and more sustainable strategies, as well as advanced postharvest handling and storage technologies, will be critically reviewed

Computer Vision System for Non-Destructive and Contactless Evaluation of Quality Traits in Fresh Rocket Leaves (Diplotaxis Tenuifolia L.)

La tesi di dottorato è incentrata sull'analisi di tecnologie non distruttive per il controllo della qualità dei prodotti agroalimentari, lungo l'intera filiera agroalimentare. In particolare, la tesi riguarda l'applicazione del sistema di visione artificiale per valutare la qualità delle foglie di rucola fresh-cut. La tesi è strutturata in tre parti (introduzione, applicazioni sperimentali e conclusioni) e in cinque capitoli, rispettivamente il primo e il secondo incentrati sulle tecnologie non distruttive e in particolare sui sistemi di computer vision per il monitoraggio della qualità dei prodotti agroalimentari. Il terzo, quarto e quinto capitolo mirano a valutare le foglie di rucola sulla base della stima di parametri qualitativi, considerando diversi aspetti: (i) la variabilità dovuta alle diverse pratiche agricole, (ii) la senescenza dei prodotti confezionati e non, e (iii) lo sviluppo e sfruttamento dei vantaggi di nuovi modelli più semplici rispetto al machine learning utilizzato negli esperimenti precedenti. Il lavoro di ricerca di questa tesi di dottorato è stato svolto dall'Università di Foggia, dall'Istituto di Scienze delle Produzioni Alimentari (ISPA) e dall'Istituto di Tecnologie e Sistemi Industriali Intelligenti per le Manifatture Avanzate (STIIMA) del Consiglio Nazionale delle Ricerche (CNR). L’attività di ricerca è stata condotta nell'ambito del Progetto SUS&LOW (Sustaining Low-impact Practices in Horticulture through Non-destructive Approach to Provide More Information on Fresh Produce History & Quality), finanziato dal MUR-PRIN 2017, e volto a sostenere la qualità della produzione e dell'ambiente utilizzando pratiche agricole a basso input e la valutazione non distruttiva della qualità di prodotti ortofrutticoli.The doctoral thesis focused on the analysis of non-destructive technologies available for the control quality of agri-food products, along the whole supply chain. In particular, the thesis concerns the application of computer vision system to evaluate the quality of fresh rocket leaves. The thesis is structured in three parts (introduction, experimental applications and conclusions) and in 5 chapters, the first and second focused on non-destructive technologies and in particular on computer vision systems for monitoring the quality of agri-food products, respectively. The third, quarter, and fifth chapters aim to assess the rocket leaves based on the estimation of quality aspects, considering different aspects: (i) the variability due to the different agricultural practices, (ii) the senescence of packed and unpacked products, and (iii) development and exploitation of the advantages of new models simpler than the machine learning used in the previous experiments. The research work of this doctoral thesis was carried out by the University of Foggia, the Institute of Science of Food Production (ISPA) and the Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA) of National Research Council (CNR). It was conducted within the Project SUS&LOW (Sustaining Low-impact Practices in Horticulture through Non-destructive Approach to Provide More Information on Fresh Produce History & Quality), funded by MUR- PRIN 2017, and aimed at sustaining quality of production and of the environment using low input agricultural practices and non-destructive quality evaluation