Citrus sorting by identification of the most common defects using multispectral computer vision

Blasco, José; Aleixos, Nuria; Gómez, Jorge Castilla; Moltó, Enrique

doi:10.1016/j.jfoodeng.2007.03.027

Cited by 179 publications

(91 citation statements)

References 35 publications

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“…Blasco et al (2007b) tested different colour spaces to discriminate among eleven types of defects in the citrus peel and the stem. Using the HSI (Hue, Saturation, Intensity) colour space, success ranged from 43% in detecting scale infestation to 100% in the case of chilling and stem-end injuries.…”

Section: Estimation Of External Properties Of the Fruitmentioning

confidence: 99%

“…Another problem is related with other defects that also present some degree of fluorescence and hence can confuse a potential automated system (Blasco et al 2007b;Slaughter et al 2008;Obenland et al 2010). A way to differentiate among defects presenting fluorescence is by determining the pattern in which the fluorescence is emitted (Momin et al 2013b) but the problem arises when a system has to detect different types of defects, that is, decay lesions that present fluorescence and mild defects that only affect the appearance of the fruit.…”

Section: Detection Of Decay Lesionsmentioning

confidence: 99%

“…Blasco et al (2007b) discriminated among 11 types of external defects of oranges and mandarins using a multispectral system composed of a colour, a NIR and a UV camera. Using the different techniques separately the results were lower than when combining them, since different defects could be detected better using some of the cameras than with the others.…”

Section: Detection Of Other Skin Defectsmentioning

confidence: 99%

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Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review

Cubero

Lee

Albert

et al. 2016

Food Bioprocess Technol

103

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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.

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Section: Estimation Of External Properties Of the Fruitmentioning

confidence: 99%

Section: Detection Of Decay Lesionsmentioning

confidence: 99%

Section: Detection Of Other Skin Defectsmentioning

confidence: 99%

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Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review

Cubero

Lee

Albert

et al. 2016

Food Bioprocess Technol

103

View full text Add to dashboard Cite

show abstract

“…A relação de reconhecimento de padrões na visão computacional se estende desde aplicações em nível industrial para a manipulação física de peças ou objetos reais, através de robôs industriais articulados (TRONCO, 1999;PEDRO, 2008a;PEDRO et al, 2008b;PEDRO & CAURIN, 2008c), como no reconhecimento de culturas agrícolas, identificando irregularidades em frutos, plantas e obtenção de ângulos de guiagem pelas linhas de plantio (BLASCO et al, 2007a;BULANON et al, 2009;SLAUGHTER et al, 2009;WANG et al, 2009;WEI et al, 2009 Bayes é calculado para duas classes, dado pela equação 07.…”

Section: Reconhecimento De Padrõesunclassified

“…Em trabalhos anteriores, Blasco (BLASCO et al, 2007a;2007b), apresentou um método para separação e identificação de regiões irregulares em frutos (laranjas), como os arilos (cobertura das sementes a partir do funículo -falso fruto). Os algoritmos foram aplicados no espaço de cores RGB, com aquisição de cada imagem por 40ms, fazendo com que a inspeção de cada cena é processada em paralelo com a aquisição seguinte.…”

Section: Anthracnose Stem-end Injury Scarring -Scirtothrips Citri unclassified

Fusão sensorial por classificação cognitiva ponderada no mapeamento de cenas naturais agrícolas para análise quali-quantitativa em citricultura

Lulio¹

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Computer systems are used in Precision Agriculture (PA) to provide relational sampling, accuracy and data processing required for agricultural practices and schemes, which are not common to conventional agriculture, demanding higher costs of production and research directed to the remote sensing for mapping and inspection of crop rows. Tasks are carried out using a priori On-the-Go sensors, and proprioceptive and exteroceptive ones, embedded instrumentation, geographic information and existing implements on production and farming, which these activities ensure during the steps of planting, maturation, maintenance and harvesting of a particular culture. Also, these tasks are aided with the use of terrestrial agricultural mobile robots such as autonomous vehicle platforms for locomotion between the crop rows in the acquisition of field data. Given this information and relating the investment and development of these technologies, the goal of this work is to assist the inspection, quantification and qualification of agricultural crops (citrus) of planting area through analyzing and identifying data for sensor fusion, associated with digital image processing, optical thermal imaging and optical flow sensors and reflectance, based on the extraction of real natural scenes objects, identifying items such as fruits, grasses, stems, branches and leaves, thus providing a qualitative and quantitative set of data analyzed culture. As of a computer vision system and sensor fusion, Gigabit Ethernet cameras, sonars, thermal camera, infrared optical flow sensors and monochrome CMOS sensors are embedded in two sides of the vehicle structure, with the same geometric center ratio of the agricultural mobile robot. After data acquisition, image and signal processing techniques are applied for non homogeneous region segmentation, and pattern recognition through statistical classifiers. Such techniques are programmed in MATLAB and OpenCV, embedded in a computer platform. Cognitive classifiers that perform pattern and classes matching are intended on a combination of weighted data fusion techniques, that during locomotion of the agricultural mobile robot, the steps of image processing and combination of parameters for classification are manipulated for analysis retrospectively, conflicting existing data with likely changes resulting in culture and biomass. Figura 3.6: Na sequência de imagens acima, da esquerda para direita tem-se a imagem visível, imagem NIR e imagem UVFL; na sequência de imagens abaixo, da esquerda para direita temse a imagem do fruto com irregularidade, segmentação usando CART, e segmentação usando LDA (Adaptado de BLASCO et al., 2007b) Figura 3.7: Etapas de segmentação e classificação, considerando o índice de diferença normalizado, o filtro de variância, componentes Cb e Cr, e inserção de rótulo no objeto encontrado, separado do plano de fundo (PAYNE et al., 2013) Figura 3.8: Imagens multimodais registradas de folhas de macieiras, apresentando sete níveis de patógenos que causam irregularidades na superfície, 21 d...

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Intelligent Identification System for Poultry Portion Sorting

Khashman¹

2014

Conventional and Advanced Food Processing Technologies

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Citrus sorting by identification of the most common defects using multispectral computer vision

Cited by 179 publications

References 35 publications

Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review

Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review

Fusão sensorial por classificação cognitiva ponderada no mapeamento de cenas naturais agrícolas para análise quali-quantitativa em citricultura

Intelligent Identification System for Poultry Portion Sorting

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