Diseño e implementación de nuevas tecnologías basadas en visión artificial para la inspección no destructiva de la calidad de fruta en campo y mínimamente procesada

Esta tesis trata de avanzar en sistemas de visión por computador aplicados a la inspección automática de la calidad de frutas y verduras en dos entornos en los que hasta la fecha no se ha trabajado en profundidad como son la inspección en campo antes de la recepción de la fruta por la central hortofrutícola y la inspección automática de la calidad de fruta procesada. Se pretende así rellenar un hueco importante en la aplicación de la visión por computador como una herramienta al servicio del sector en la inspección de frutas y verduras. El desarrollo de técnicas de visión por computador en la inspección de la calidad de los productos agrícolas se debe a la necesidad de encontrar una alternativa a los métodos de inspección manual tradicionales para eliminar el contacto con el producto, aumentar la fiabilidad y objetividad, introducir flexibilidad a las líneas de confección e incrementar la productividad y competitividad de nuestras empresas. Esta tecnología está ampliamente extendida para la inspección de fruta en fresco en almacenes de confección pero, sin embargo, todavía no se ha aplicado en campo por las dificultades técnicas que conlleva este entorno, y tampoco en el sector de la fruta mínimamente procesada, debido a la fragilidad y dificultad de manipulación del producto, la complejidad de la inspección y el relativo menor valor económico respecto de la fruta en fresco. En esta tesis se aborda, por una parte, la creación de un sistema de visión por computador instalado en una plataforma de asistencia a la recolección de cítricos sobre la que se analiza la fruta a la vez que se recolecta y se clasifica en diversas categorías en función de su color, tamaño o calidad (presencia de defectos externos). Los mayores problemas del trabajo en campo se refieren a una iluminación inestable, movimientos y vibraciones, energía eléctrica limitada o efectos de la intemperie. Para ello es necesario diseñar un sistema de visión por computador compacto, robusto, rápido y muy efiCubero García, S. (2012). Diseño e implementación de nuevas tecnologías basadas en visión artificial para la inspección no destructiva de la calidad de fruta en campo y mínimamente procesada [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/15999Palanci

Nuevas tecnologías para la preselección automática de cítricos en el campo

El trabajo de esta tesina aborda el desarrollo de un sistema de inspección automática para el análisis de la calidad de los cítricos en línea mediante visión por computador. Este sistema debe ser capaz de inspeccionar y clasificar la fruta en tiempo real en tres categorías diferentes, trabajando en condiciones de campo sobre una maquina en movimiento, atendiendo a parámetros de color y a estimaciones sobre la presencia de daños en su piel extraídas a partir del análisis de las imágenes.Cubero García, S. (2009). Nuevas tecnologías para la preselección automática de cítricos en el campo. Universitat Politècnica de València. http://hdl.handle.net/10251/59047Archivo delegad

Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing

[EN] The use of remote sensing to map the distribution of plant diseases has evolved considerably over the last three decades and can be performed at different scales, depending on the area to be monitored, as well as the spatial and spectral resolution required. This work describes the development of a small low-cost field robot (Remotely Operated Vehicle for Infection Monitoring in orchards, XF-ROVIM), which is intended to be a flexible solution for early detection of Xylella fastidiosa (X. fastidiosa) in olive groves at plant to leaf level. The robot is remotely driven and fitted with different sensing equipment to capture thermal, spectral and structural information about the plants. Taking into account the height of the olive trees inspected, the design includes a platform that can raise the cameras to adapt the height of the sensors to a maximum of 200 cm. The robot was tested in an olive grove (4 ha) potentially infected by X. fastidiosa in the region of Apulia, southern Italy. The tests were focused on investigating the reliability of the mechanical and electronic solutions developed as well as the capability of the sensors to obtain accurate data. The four sides of all trees in the crop were inspected by travelling along the rows in both directions, showing that it could be easily adaptable to other crops. XF-ROVIM was capable of inspecting the whole field continuously, capturing geolocated spectral information and the structure of the trees for later comparison with the in situ observations.This work was partially supported by funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 727987 Xylella Fastidiosa Active Containment Through a multidisciplinary-Oriented Research Strategy (XF-ACTORS).Rey, B.; Aleixos Borrás, MN.; Cubero-García, S.; Blasco Ivars, J. (2019). Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing. Remote Sensing. 11(3). https://doi.org/10.3390/rs11030221113Martelli, G. P., Boscia, D., Porcelli, F., & Saponari, M. (2015). The olive quick decline syndrome in south-east Italy: a threatening phytosanitary emergency. European Journal of Plant Pathology, 144(2), 235-243. doi:10.1007/s10658-015-0784-7Olmo, D., Nieto, A., Adrover, F., Urbano, A., Beidas, O., Juan, A., … Landa, B. B. (2017). First Detection of Xylella fastidiosa Infecting Cherry (Prunus avium) and Polygala myrtifolia Plants, in Mallorca Island, Spain. Plant Disease, 101(10), 1820-1820. doi:10.1094/pdis-04-17-0590-pdnSaponari, M., Giampetruzzi, A., Loconsole, G., Boscia, D., & Saldarelli, P. (2019). Xylella fastidiosa in Olive in Apulia: Where We Stand. Phytopathology®, 109(2), 175-186. doi:10.1094/phyto-08-18-0319-fiVergara-Díaz, O., Zaman-Allah, M. A., Masuka, B., Hornero, A., Zarco-Tejada, P., Prasanna, B. M., … Araus, J. L. (2016). A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization. Frontiers in Plant Science, 7. doi:10.3389/fpls.2016.00666Thenkabail, P. S., & Lyon, J. G. (Eds.). (2016). Hyperspectral Remote Sensing of Vegetation. doi:10.1201/b11222Calderón, R., Navas-Cortés, J. A., Lucena, C., & Zarco-Tejada, P. J. (2013). High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sensing of Environment, 139, 231-245. doi:10.1016/j.rse.2013.07.031Gonzalez-Dugo, V., Hernandez, P., Solis, I., & Zarco-Tejada, P. (2015). Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in the Context of Wheat Phenotyping. Remote Sensing, 7(10), 13586-13605. doi:10.3390/rs71013586Hernández-Clemente, R., Navarro-Cerrillo, R., Ramírez, F., Hornero, A., & Zarco-Tejada, P. (2014). A Novel Methodology to Estimate Single-Tree Biophysical Parameters from 3D Digital Imagery Compared to Aerial Laser Scanner Data. Remote Sensing, 6(11), 11627-11648. doi:10.3390/rs61111627Colaço, A. F., Molin, J. P., Rosell-Polo, J. R., & Escolà, A. (2018). Application of light detection and ranging and ultrasonic sensors to high-throughput phenotyping and precision horticulture: current status and challenges. Horticulture Research, 5(1). doi:10.1038/s41438-018-0043-0Ma, Q., Su, Y., Luo, L., Li, L., Kelly, M., & Guo, Q. (2018). Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data. Ecological Indicators, 95, 298-310. doi:10.1016/j.ecolind.2018.07.050Ma, Q., Su, Y., & Guo, Q. (2017). Comparison of Canopy Cover Estimations From Airborne LiDAR, Aerial Imagery, and Satellite Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9), 4225-4236. doi:10.1109/jstars.2017.2711482Martinelli, F., Scalenghe, R., Davino, S., Panno, S., Scuderi, G., Ruisi, P., … Dandekar, A. M. (2014). Advanced methods of plant disease detection. A review. Agronomy for Sustainable Development, 35(1), 1-25. doi:10.1007/s13593-014-0246-1Calderón, R., Navas-Cortés, J., & Zarco-Tejada, P. (2015). Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas. Remote Sensing, 7(5), 5584-5610. doi:10.3390/rs70505584Zarco-Tejada, P. J., Camino, C., Beck, P. S. A., Calderon, R., Hornero, A., Hernández-Clemente, R., … Navas-Cortes, J. A. (2018). Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nature Plants, 4(7), 432-439. doi:10.1038/s41477-018-0189-7Aasen, H., Honkavaara, E., Lucieer, A., & Zarco-Tejada, P. (2018). Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote Sensing, 10(7), 1091. doi:10.3390/rs10071091Vicent, A., & Blasco, J. (2017). When prevention fails. Towards more efficient strategies for plant disease eradication. New Phytologist, 214(3), 905-908. doi:10.1111/nph.14555Wang, X., Singh, D., Marla, S., Morris, G., & Poland, J. (2018). Field-based high-throughput phenotyping of plant height in sorghum using different sensing technologies. Plant Methods, 14(1). doi:10.1186/s13007-018-0324-5Bourgeon, M. A., Gée, C., Debuisson, S., Villette, S., Jones, G., & Paoli, J. N. (2016). « On-the-go » multispectral imaging system to characterize the development of vineyard foliage with quantitative and qualitative vegetation indices. Precision Agriculture, 18(3), 293-308. doi:10.1007/s11119-016-9489-yUnderwood, J. P., Hung, C., Whelan, B., & Sukkarieh, S. (2016). Mapping almond orchard canopy volume, flowers, fruit and yield using lidar and vision sensors. Computers and Electronics in Agriculture, 130, 83-96. doi:10.1016/j.compag.2016.09.014Zampetti, E., Papa, P., Di Flaviano, F., Paciucci, L., Petracchini, F., Pirrone, N., … Macagnano, A. (2017). Remotely Controlled Terrestrial Vehicle Integrated Sensory System for Environmental Monitoring. Sensors, 338-343. doi:10.1007/978-3-319-55077-0_43Hiremath, S. A., van der Heijden, G. W. A. M., van Evert, F. K., Stein, A., & ter Braak, C. J. F. (2014). Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter. Computers and Electronics in Agriculture, 100, 41-50. doi:10.1016/j.compag.2013.10.005Pérez-Ruiz, M., Gonzalez-de-Santos, P., Ribeiro, A., Fernandez-Quintanilla, C., Peruzzi, A., Vieri, M., … Agüera, J. (2015). Highlights and preliminary results for autonomous crop protection. Computers and Electronics in Agriculture, 110, 150-161. doi:10.1016/j.compag.2014.11.010Weiss, M., Baret, F., Smith, G. J., Jonckheere, I., & Coppin, P. (2004). Review of methods for in situ leaf area index (LAI) determination. Agricultural and Forest Meteorology, 121(1-2), 37-53. doi:10.1016/j.agrformet.2003.08.001Hosoi, F., & Omasa, K. (2006). Voxel-Based 3-D Modeling of Individual Trees for Estimating Leaf Area Density Using High-Resolution Portable Scanning Lidar. IEEE Transactions on Geoscience and Remote Sensing, 44(12), 3610-3618. doi:10.1109/tgrs.2006.881743Stein, M., Bargoti, S., & Underwood, J. (2016). Image Based Mango Fruit Detection, Localisation and Yield Estimation Using Multiple View Geometry. Sensors, 16(11), 1915. doi:10.3390/s16111915Saponari, M., Boscia, D., Altamura, G., Loconsole, G., Zicca, S., D’Attoma, G., … Martelli, G. P. (2017). Isolation and pathogenicity of Xylella fastidiosa associated to the olive quick decline syndrome in southern Italy. Scientific Reports, 7(1). doi:10.1038/s41598-017-17957-

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

Comparison of latent variable-based and artificial intelligence methods for impurity detection in PET recycling from NIR hyperspectral images

[EN] In polyethylene terephthalate's (PET)'s recycling processes, separation from polyvinyl chloride (PVC) is of prior relevance due to its toxicity, which degrades the final quality of recycled PET. Moreover, the potential presence of some polymers in mixed plastics (such as PVC in PET) is a key aspect for the use of recycled plastic in products such as medical equipment, toys, or food packaging. Many works have dealt with plastic classification by hyperspectral imaging, although only some of them have been directly focused on PET sorting and very few on its separation from PVC. These works use different classification models and preprocessing techniques and show their performance for the problem at hand. However, still, there is a lack of methodology to address the goal of comparing and finding the best model and preprocessing technique. Thus, this paper presents a design of experiments-based methodology for comparing and selecting, for the problem at hand, the best preprocessing technique, and the best latent variable-based and/or artificial intelligence classification method, when using NIR hyperspectral images. There is a lack of methodology to address the goal of comparing and finding the best model and preprocessing technique. Thus, this paper presents a design of experiments-based methodology for comparing and selecting, for the problem at hand, the best preprocessing technique, and the best latent variable-based and/or artificial intelligence classification method when using near-infrared hyperspectral images.Universitat Politecnica de Valencia, Grant/Award Number: UPV-FE-16-B18This research was partially supported by the Universitat Politècnica de València under the project UPV‐FE‐16‐B18.Galdón-Navarro, B.; Prats-Montalbán, JM.; Cubero-García, S.; Blasco Ivars, J.; Ferrer, A. (2018). Comparison of latent variable-based and artificial intelligence methods for impurity detection in PET recycling from NIR hyperspectral images. Journal of Chemometrics. 32(1):1-14. https://doi.org/10.1002/cem.2980S11432

In-line Application of Visible and Near-Infrared Diffuse Reflectance Spectroscopy to Identify Apple Varieties

[EN] One of the most studied techniques for the non-destructive determination of the internal quality of fruits has been visible and nearinfrared (VIS-NIR) reflectance spectroscopy. This work evaluates a new non-destructive in-line VIS-NIR spectroscopy prototype for in-line identification of five apple varieties, with the advantage that it allows the spectra to be captured with the probe at the same distance from all the fruits regardless of their size. The prototype was tested using varieties with a similar appearance by acquiring the diffuse reflectance spectrum of the fruits travelling on the conveyor belt at a speed of 0.81 m/s which is nearly 1 fruit/s. Principal component analysis (PCA) was used to determine the variables that explain the most variance in the spectra. Seven principal components were then used to perform linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). QDA was found to be the best in-line classification method, achieving 98% and 85% success rates for red and yellow apple varieties, respectively. The results indicated that the in-line application of VIS-NIR spectroscopy that was developed is potentially feasible for the detection of apple varieties with an accuracy that is similar to or better than a laboratory system.This work was partially funded by the Generalitat Valenciana through project AICO/2015/122 and by INIA and FEDER funds through project RTA2015-00078-00-00. Victoria Cortes Lopez thanks the Spanish Ministry of Education, Culture and Sports for FPU grant (FPU13/04202).Cortes-Lopez, V.; Cubero-García, S.; Blasco Ivars, J.; Aleixos Borrás, MN.; Talens Oliag, P. (2019). In-line Application of Visible and Near-Infrared Diffuse Reflectance Spectroscopy to Identify Apple Varieties. Food and Bioprocess Technology. 12(6):1021-1030. https://doi.org/10.1007/s11947-019-02268-0S10211030126Aleixandre-Tudo, J. 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E., Wubbels, A., van der Sluis, C., & van der Meer, J. M. (2006). Understanding factors affecting near infrared analysis of potato constituents. Journal of Near Infrared Spectroscopy, 14(1), 27–35.He, Y., Li, X., & Shao, Y. (2007). Fast discrimination of apple varieties using Vis/NIR spectroscopy. International Journal of Food Properties, 10(1), 9–18.Hernández, A., He, Y., & García, A. (2006). Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR-spectroscopy techniques. Journal of Food Engineering, 77, 313–319.Huang, H., Yu, H., Xu, H., & Ying, Y. (2008). Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: a review. Journal of Food Engineering, 87(3), 303–313.James, G., Witten, D., Hastie, T., & Tibshirani, R. (2014). An introduction to statistical learning: with applications in R. New York: springer.Jie, D., Xie, L., Rao, X., & Ying, Y. (2014). Using visible and near infrared diffuse transmittance technique to predict soluble solids content of watermelon in an on-line detection system. Postharvest Biology and Technology, 90, 1–6.Kader, A. A., Kasmire, R. F., Mitchell, F. G., Reid, M. S., Sommer, N. F., & Thompson, J. F. (1985). Postharvest technology of horticultural crops (Special publication, mum. 3311, p. 192). Davis: Cooperative Extension, University of California.Kozak, M., & Scaman, C. H. (2008). Unsupervised classification methods in food sciences: discussion and outlook. Journal of the Science of Food and Agriculture, 88(7), 1115–1127.Lammertyn, J., De Baerdemaeker, J., & Nicolaï, B. (2000). Light penetration properties of NIR radiation in fruit with respect to non-destructive quality assessment. Postharvest Biology and Technology, 18(2), 121–132.Liu, F., Jiang, Y., & He, Y. (2009). 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Classification of Apple varieties using near infrared reflectance spectroscopy and fuzzy discriminant C-Means clustering model. Journal of Food Process Engineering, 40, 1–7

Visible and near-infrared diffuse reflectance spectroscopy for fast qualitative and quantitative assessment of nectarine quality

[EN] Visible and near-infrared spectroscopy has been widely used as a non-invasive and rapid-assessment technique for the quality control of agricultural products. In this study, 325 samples of nectarines representing two commercial varieties, cv. 'Big Top' and cv. 'Magique', were analysed by visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR). The spectral data were pre-treated and analysed to predict the internal quality of the samples and to discriminate between the two varieties. Good prediction of the internal quality of the samples, using partial least-squares regressions, was observed for both (R (2) (P) of 0.909 and 0.927 and RMSEP of 0.235 and 0.238 for cv. Big Top and Magique, respectively). Discriminant models, using linear discriminant and partial least-squares discriminant analyses, were built to classify the nectarines. Both methods provided good results with rates of 97.44 and 100% of correctly classified samples. The results indicated that visible and near-infrared techniques can be useful and simple methods for quality control and for the correct identification of nectarines in commercial lines as an alternative to the slower and less accurate manual classification.This work was partially funded by the Generalitat Valenciana through project AICO/2015/122 and by the INIA and FEDER funds through projects RTA2012-00062-C04-01 and 03, and RTA2015-00078-00-00. Victoria Lopez Cortes thanks the Spanish Ministry of Education, Culture and Sports for the FPU grant (FPU13/04202). The authors are also grateful to Fruits de Ponent (Lerida) for providing the fruit.Cortes-Lopez, V.; Blasco Ivars, J.; Aleixos Borrás, MN.; Cubero García, S.; Talens Oliag, P. (2017). Visible and near-infrared diffuse reflectance spectroscopy for fast qualitative and quantitative assessment of nectarine quality. 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Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review

[EN] Background: The increasing demand for quality assurance in agro-food production requires sophisticated analytical methods for in-line quality control. One of these techniques is visible and near-infrared (VIS-NIR) spectroscopy, which has low running costs, does not need sample preparation, and is non-destructive, environmentally friendly, and fast. Despite these advantages, only a limited amount of research has been conducted on VIS-NIR in-line applications to measure, control, and predict quality in fruits and vegetables. Scope and approach: The applicability of VIS-NIR spectroscopy for the off-line and in-line monitoring of quality in postharvest products has been addressed in this review. The document focuses on the comparison between the two processes for the same agro-food product, highlighting the main advantages and disadvantages, problems, solutions, and differences. Key findings and conclusions: VIS-NIR techniques, combined with chemometric methods, have shown great potential due to their fast detection speed, and the possibility of simultaneously predicting multiple quality parameters or distinguishing between products according to the objectives. Being able to automate processes is a great advantage compared to routine off-line analyses, mainly due to the savings achieved in time, material, and personnel. However, in numerous cases, in-line implementation has not been accomplished in the corresponding studies, hence the scarcity of real in-line applications. Recent demands, together with the advances being made in the technology and a reduction in the price of equipment, makes VIS-NIR technology an analytical alternative for continuous real-time food quality controls, which will become predominant in the next few years.This work was partially funded by INIA and FEDER funds through research project RTA2015-00078-00-00.Victoria Cortés López thanks the Spanish Ministry of Education, Culture and Sports for the FPU grant (FPU13/04202).Cortes-Lopez, V.; Blasco Ivars, J.; Aleixos Borrás, MN.; Cubero-García, S.; Talens Oliag, P. (2019). Monitoring strategies for quality control of agricultural products using visible and near-infrared spectroscopy: A review. Trends in Food Science & Technology. 85:138-148. https://doi.org/10.1016/j.tifs.2019.01.015S1381488

Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines

[EN] The internal quality of nectarines (Prunus persica L. Batsch var. nucipersica) cv. 'Big Top' (yellow flesh) and 'Magique' (white flesh) has been inspected using hyperspectral transmittance imaging. Hyperspectral images of intact fruits were acquired in the spectral range of 630-900 nm using transmittance mode during their ripening under controlled conditions. The detection of split pit disorder and classification according to an established firmness threshold were performed using PLS-DA. The prediction of the Internal Quality Index (IQI) related to ripeness was performed using PLS-R. The most important variables were selected using interval-PLS. As a result, an accuracy of 94.7% was obtained in the detection of fruits with split pit of the 'Big Top' cultivar. Accuracies of 95.7% and 94.6% were achieved in the classification of the 'Big Top' and 'Magique' cultivars, respectively, according to the firmness threshold. The internal quality was predicted through the IQI with R-2 values of 0.88 and 0.86 for the two cultivars. The results obtained indicate the great potential of hyperspectral transmittance imaging for the assessment of the internal quality of intact nectarines.This work was partially funded by INIA and FEDER funds through project RTA2015-00078-00-00. Sandra Munera thanks INIA for the FPI-INIA grant num. 43 (CPR2014-0082), partially supported by European Union FSE funds.Munera, S.; Blasco Ivars, J.; Amigo, J.; Cubero-García, S.; Talens Oliag, P.; Aleixos Borrás, MN. (2019). Use of hyperspectral transmittance imaging to evaluate the internal quality of nectarines. Biosystems Engineering. 182:54-64. https://doi.org/10.1016/j.biosystemseng.2019.04.001S546418

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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