Search results for "Image"

showing 10 items of 6818 documents

Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs…

2020

Abstract Image processing and artificial intelligence (AI) techniques have been applied to analyze, evaluate and classify mulberry fruit according to their ripeness (unripe, ripe, and overripe). A total of 577 mulberries were graded by an expert and the images were captured by an imaging system. Then, the geometrical properties, color, and texture characteristics of each segmented mulberry was extracted using two feature reduction methods: Correlation-based Feature Selection subset (CFS) and Consistency subset (CONS). Artificial Neural Networks (ANN) and Support Vector Machine (SVM) were applied to classify mulberry fruit. ANN classification with the CFS subset feature extraction method res…

0106 biological sciencesArtificial neural networkbusiness.industryFeature extractionPattern recognitionFeature selectionImage processing04 agricultural and veterinary sciencesHorticulturecomputer.software_genreRipeness01 natural sciencesExpert system040501 horticultureMachine vision systemSupport vector machineArtificial intelligence0405 other agricultural sciencesbusinessAgronomy and Crop Sciencecomputer010606 plant biology & botanyFood ScienceMathematicsPostharvest Biology and Technology
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Airborne-laser-scanning-derived auxiliary information discriminating between broadleaf and conifer trees improves the accuracy of models for predicti…

2020

Managing forests for ecosystem services and biodiversity requires accurate and spatially explicit forest inventory data. A major objective of forest management inventories is to estimate the standing timber volume for certain forest areas. In order to improve the efficiency of an inventory, field based sample-plots can be statistically combined with remote sensing data. Such models usually incorporate auxiliary variables derived from canopy height models. The inclusion of forest type variables, which quantify broadleaf and conifer volume proportions, has been shown to further improve model performance. Currently, the most common way of quantifying broadleaf and conifer forest types is by ca…

0106 biological sciencesCanopysekametsätMean squared errorForest managementBiodiversityClimate changeairborne laser scanningManagement Monitoring Policy and Law010603 evolutionary biology01 natural sciencesforest type mapStatisticscanopy height modelimage-based point cloudsNature and Landscape ConservationForest inventorymetsäsuunnitteluForestryPercentage pointmetsänarviointipuutavaranmittausOrdinary least squaresordinary least squares regression modelsEnvironmental sciencemixed and heterogeneously structured forestkaukokartoitushigh-precision forest inventorymetsänhoitobest fit modelsmerchantable timber volumelaserkeilaus010606 plant biology & botanyForest Ecology and Management
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Comparison of input data with different spatial resolution in landscape pattern analysis – A case study from northern Latvia

2017

A suitable spatial scale needs to be selected in geographical and landscape ecological research, and this requires great consideration as different scales have profound effect on derived landscape spatial patterns. Numerous studies have investigated the effects of different scales on landscape metrics using simulated patterns, but few have been conducted to compare different data sources with variable scale for regional- and landscape-scale assessments. Possibly this has occurred because researchers have been prone to use the best available source, a well-known standard, and easiest to use. This study was conducted to assess the impact of input data resolution on values of landscape pattern…

0106 biological sciencesCartographic generalization010504 meteorology & atmospheric sciencesGeography Planning and DevelopmentForestryLand cover010603 evolutionary biology01 natural sciencesVariable (computer science)GeographyThematic mapHabitatTourism Leisure and Hospitality ManagementSpatial ecologyScale (map)CartographyImage resolution0105 earth and related environmental sciencesGeneral Environmental ScienceApplied Geography
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Conservation and people: Towards an ethical code of conduct for the use of camera traps in wildlife research

2020

International audience; Abstract 1. Camera trapping is a widely employed tool in wildlife research, used to estimate animal abundances, understand animal movement, assess species richness and understand animal behaviour. In addition to images of wild animals, research cameras often record human images, inadvertently capturing behaviours ranging from innocuous actions to potentially serious crimes. 2. With the increasing use of camera traps, there is an urgent need to reflect on how researchers should deal with human images caught on cameras. On the one hand, it is important to respect the privacy of individuals caught on cameras, while, on the other hand, there is a larger public duty to re…

0106 biological sciencesCode of conductmedia_common.quotation_subjectPARTNERS principles for community‐based conservationInternet privacyComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONWildlifeprivacyhuman rights010603 evolutionary biology01 natural sciencesEcology and Environment[SHS]Humanities and Social Sciencessnow leopardGE1-35014. Life underwaterlawQH540-549.5Ethical codemedia_commonEcologyHuman rightscamera trapcode of conductbusiness.industry010604 marine biology & hydrobiology15. Life on landethicsEnvironmental sciencesGeographySnow leopardCamera trapPARTNERS principles for community-based conservationbusinessEcological Solutions and Evidence
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Benchmark database for fine-grained image classification of benthic macroinvertebrates

2018

Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categori…

0106 biological sciencesComputer scienceta1172Sample (statistics)monitorointi02 engineering and technologyneuroverkot01 natural sciencesConvolutional neural network0202 electrical engineering electronic engineering information engineeringkonenäköfine-grained classification14. Life underwaterFine-grained classificationInvertebrateta113ta112Contextual image classificationbusiness.industry010604 marine biology & hydrobiologyDeep learningConvolutional Neural NetworksBenchmark databasedeep learningPattern recognitionDeep learningselkärangattomatvedenlaatu6. Clean waterkoneoppiminenBenthic zoneBenthic macroinvertebratesbiomonitoringSignal ProcessingBiomonitoringta1181lajinmääritys020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligenceWater qualitybusinessbenthic macroinvertebrates
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A nondestructive intelligent approach to real‐time evaluation of chicken meat freshness based on computer vision technique

2019

In this study, the capability of a procedure based on combination of computer vision (CV) and artificial intelligence techniques examined for intelligent and nondestructive prediction of chicken meat freshness during the spoilage process at 4°C. The proposed system comprises the following stages: capture images, image preprocessing, image processing, computing channels, feature extraction, feature selection by a hybrid of genetic algorithm (GA) and artificial neuronal network (ANN), and prediction by using ANN. The number of neurons in input layer was determined 33 (selected features) and freshness used as the output. The ideal ANN model was obtained with 33‐10‐1 topology. The high performa…

0106 biological sciencesCorrelation coefficientbusiness.industryComputer scienceGeneral Chemical Engineeringmedia_common.quotation_subjectFeature extractionProcess (computing)Image processingFeature selection04 agricultural and veterinary sciences040401 food science01 natural sciences0404 agricultural biotechnology010608 biotechnologyGenetic algorithmPreprocessorQuality (business)Computer visionArtificial intelligencebusinessFood Sciencemedia_commonJournal of Food Process Engineering
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Temperate Fish Detection and Classification: a Deep Learning based Approach

2021

A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) …

0106 biological sciencesFOS: Computer and information sciencesComputer Science - Machine LearningComputer scienceComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern Recognition010603 evolutionary biology01 natural sciencesConvolutional neural networkVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420Machine Learning (cs.LG)Artificial IntelligenceClassifier (linguistics)FOS: Electrical engineering electronic engineering information engineeringbusiness.industry010604 marine biology & hydrobiologyDeep learningImage and Video Processing (eess.IV)Process (computing)Pattern recognitionElectrical Engineering and Systems Science - Image and Video ProcessingObject detectionA priori and a posterioriNoise (video)Artificial intelligenceTransfer of learningbusiness
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Developing an orientation and cutting point determination algorithm for a trout fish processing system using machine vision

2019

Abstract Fish processing in small and medium fish supplying centers requires an intelligent system to operate on different sizes. Therefore, an image processing algorithm was developed to extract the proper head and belly cutting points according to the trout dimensions. The algorithm detects the fish orientation and location of pectoral, anal, pelvic, and caudal fins. In this study, each of the trout images was divided into slices along its length in order to segment the fins and extract cutting points. The channel ‘B’ of RGB color space was considered in both initial segmentation and fin detection stages among the examined channels of RGB, HSV, and L*a*b* color spaces. The back-belly and …

0106 biological sciencesFinbiologyOrientation (computer vision)ForestryImage processing04 agricultural and veterinary sciencesHSL and HSVHorticultureColor spacebiology.organism_classification01 natural sciencesComputer Science ApplicationsRGB color spaceTrout040103 agronomy & agriculture0401 agriculture forestry and fisheriesRGB color modelAgronomy and Crop ScienceAlgorithm010606 plant biology & botanyMathematicsComputers and Electronics in Agriculture
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Use of Leaf and Fruit Morphometric Analysis to Identify and Classify White Mulberry (Morus alba L.) Genotypes

2018

Digital image analysis and multivariate data analysis were used in this study to identify a set of leaf and fruit morphometric traits to discriminate white mulberry (Morus alba L.) cultivars. The trial was conducted using three- to five-year-old potted cuttings of several white mulberry cultivars. 32 leaf morphometric descriptors were recorded in 2011 and 2012 from 11 mulberry cultivars using image analysis of scanned leaves, whereas six fruit descriptors were recorded in 2011 from nine mulberry cultivars. Linear discriminant analysis (LDA) was used to identify a subset of measured variables that could discriminate the cultivars in trial. Biplot analysis, followed by cluster analysis, was p…

0106 biological sciencesLinear discriminant analysifood.ingredientlinear discriminant analysisBiplotPlant ScienceBiology01 natural sciencesCutting0404 agricultural biotechnologyfoodGenotypedescriptordigital image analysisLeaf sizeCultivarlcsh:Agriculture (General)MorphometricsMultivariate analysi<i>Morus alba</i>Digital image analysi04 agricultural and veterinary sciencesLinear discriminant analysislcsh:S1-972040401 food scienceMorus albaHorticulturemultivariate analysisWhite MulberryAgronomy and Crop Sciencebiplot010606 plant biology & botanyFood ScienceAgriculture
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Image analysis methods for assessment of H2O2 production and Plasmopara viticola development in grapevine leaves: application to the evaluation of re…

2013

article i nfo The grapevine downy mildew (Plasmopara viticola) provokes severe damages and destroys the harvest in the absence of an effective protection. Numerous fungicide treatments are thus generally necessary. To promote a sustainable production, alternative strategies of protection including new antifungal molecules, resistant geno- types or elicitor-induced resistance are under trial. To evaluate the relevance of these strategies, resistance tests are required. In this context, three image analysis methods were developed to read the results of tests performed to assessP.viticolasporulation and mycelial development, and H 2 O 2 production in leaves. They have been validated using elic…

0106 biological sciencesMicrobiology (medical)Antifungalmedicine.drug_class[SDV]Life Sciences [q-bio]H2O2Context (language use)01 natural sciencesMicrobiologyImage analysis03 medical and health sciencesPlasmopara viticolamedicinePlant defense against herbivoryImage Processing Computer-Assisted[SDV.BV]Life Sciences [q-bio]/Vegetal BiologyVitisimage analysis;Plasmopara viticola;downy mildew;grapevine;H2O2;resistance testsMolecular Biology[ SDV.MP.MYC ] Life Sciences [q-bio]/Microbiology and Parasitology/MycologyAnalysis method[SDV.MP.MYC]Life Sciences [q-bio]/Microbiology and Parasitology/Mycology030304 developmental biologyDisease ResistancePlant Diseases2. Zero hunger0303 health sciencesResistance (ecology)biologyResistance testsReproducibility of Resultsfood and beveragesHydrogen Peroxidebiology.organism_classificationFungicidePlant LeavesHorticultureAgronomyOomycetesPlasmopara viticola[SDE]Environmental SciencesDowny mildewGrapevine010606 plant biology & botanyDowny mildew
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