Search results for "N01 Agricultural engineering"

showing 5 items of 5 documents

Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics

2021

[EN] The main cause of flesh browning in 'Rojo Brillante' persimmon fruit is mechanical damage caused during harvesting and packing. Innovation and research on nondestructive techniques to detect this phenomenon in the packing lines are necessary because this type of alteration is often only seen when the final consumer peels the fruit. In this work, we have studied the application of hyperspectral imaging in the range of 450-1040 nm to detect mechanical damage without any external symptoms. The fruit was damaged in a controlled manner. Later, images were acquired before and at 0, 1, 2 and 3 days after damage induction. First, the spectral data captured from the images were analysed through…

Health (social science)N01 Agricultural engineeringEXPRESION GRAFICA EN LA INGENIERIANondestructivePlant ScienceTP1-1185BiologyHealth Professions (miscellaneous)MicrobiologyArticlecomputer visionChemometricsBrowningChemometricsH20 Plant diseasesDiospyros kakiSpectral databrowningFruit qualityChemical technologyfruit qualityDiospyros kakiHyperspectral imagingfood and beverageschemometricsQ01 Food science and technologynondestructiveQ02 Food processing and preservationHorticulturePrincipal component analysisH50 Miscellaneous plant disordersComputer visionBrowning<i>Diospyros kaki</i>Food Science
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Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images

2021

Anthracnose is one of the primary diseases that affect olive production before and after harvest, causing severe damage and economic losses. The objective of this work is to detect this disease in the early stages, using hyperspectral images and advanced modelling techniques of Deep Learning (DL) and convolutional neural networks (CNN). The olives were artificially inoculated with the fungus. Hyperspectral images (450–1050 nm) of each olive were acquired until visual symptoms of the disease were observed, in some cases up to 9 days. The olives were classified into two classes: control, inoculated with water, and fungi composed of olives inoculated with the fungus. The ResNet101 architecture…

N01 Agricultural engineeringQuality inspectionqualitySpectral imagingU30 Research methodsFungiComputer visionH20 Plant diseasesOlea europaea
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Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images

2021

Abstract Anthracnose is one of the primary diseases that affect olive production before and after harvest, causing severe damage and economic losses. The objective of this work is to detect this disease in the early stages, using hyperspectral images and advanced modelling techniques of Deep Learning (DL) and convolutional neural networks (CNN). The olives were artificially inoculated with the fungus. Hyperspectral images (450–1050 nm) of each olive were acquired until visual symptoms of the disease were observed, in some cases up to 9 days. The olives were classified into two classes: control, inoculated with water, and fungi composed of olives inoculated with the fungus. The ResNet101 arc…

N01 Agricultural engineeringbusiness.industryDeep learningFungiHyperspectral imagingForestryPattern recognitionHorticultureBiologyVisual symptomsConvolutional neural networkComputer Science ApplicationsQuality inspectionSpectral imagingN20 Agricultural machinery and equipmentU30 Research methodsComputer visionArtificial intelligenceH20 Plant diseasesOlea europaeabusinessAgronomy and Crop ScienceComputers and Electronics in Agriculture
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Discrimination of common defects in loquat fruit cv. ‘Algerie’ using hyperspectral imaging and machine learning techniques

2021

Abstract Loquat (Eriobotrya japonica L.) is an important fruit for the economy of some regions of Spain that is very susceptible to mechanical damage and physiological disorders. These problems depreciate its value and prevent it from being exported. Visible (VIS) and near infrared (NIR) hyperspectral imaging was used to discriminate between external and internal common defects of loquat cv. ‘Algerie’. Two classifiers, random forest (RF) and extreme gradient boost (XGBoost), and different spectral pre-processing techniques were evaluated in terms of their capacity to distinguish between sound and defective features according to three approaches. In the first approach the fruit pixels were c…

0106 biological sciencesN01 Agricultural engineeringEriobotryaHorticulture01 natural sciences040501 horticultureNon-destructiveClassification rateH20 Plant diseasesArtificial visionMathematicsPixelbiologybusiness.industryHyperspectral imagingPattern recognition04 agricultural and veterinary sciencesClassificationbiology.organism_classificationQualityRandom forestEriobotrya japonicaMultivariate analysisN20 Agricultural machinery and equipmentArtificial intelligence0405 other agricultural sciencesbusinessAgronomy and Crop Science010606 plant biology & botanyFood SciencePostharvest Biology and Technology
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Application of near Infrared Spectroscopy to the Quality Control of Citrus Fruits and Mango

2016

NIR spectroscopy is a proved tool to measure the optical properties of the samples, which are related to their chemical and textural properties. This technology can be used for determining the internal and external quality of fruits. Accordingly, many studies have been reported for long time to assess the quality of different fresh fruits by using reflectance measurements acquired with visible-NIR spectroscopy. We have been working on the estimation of the quality of fruits using computer vision for more than twenty years, always focused on problems that affect the local industry. As the region of Valencia (Spain) is one of the main producers and exporters of citrus fruits worldwide, most o…

N01 Agricultural engineeringbiologyEXPRESION GRAFICA EN LA INGENIERIATECNOLOGIA DE ALIMENTOSIniaCitrus fruits04 agricultural and veterinary sciencesbiology.organism_classificationQ01 Food science and technology040401 food scienceAgricultural scienceMangoes0404 agricultural biotechnologyNear infrared spectroscopyMathematics
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