416 research outputs found
Computer Vision System for Non-Destructive and Contactless Evaluation of Quality Traits in Fresh Rocket Leaves (Diplotaxis Tenuifolia L.)
La tesi di dottorato Ăš incentrata sull'analisi di tecnologie non distruttive per il controllo della
qualitĂ dei prodotti agroalimentari, lungo l'intera filiera agroalimentare. In particolare, la tesi
riguarda l'applicazione del sistema di visione artificiale per valutare la qualitĂ delle foglie di
rucola fresh-cut. La tesi Ăš 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 qualitĂ 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 variabilitĂ dovuta alle diverse pratiche agricole, (ii) la senescenza dei prodotti confezionati
e non, e (iii) lo sviluppo e sfruttamento dei vantaggi di nuovi modelli piĂč semplici rispetto al
machine learning utilizzato negli esperimenti precedenti. Il lavoro di ricerca di questa tesi di
dottorato Ăš stato svolto dall'UniversitĂ 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). LâattivitĂ di
ricerca Ăš 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 qualitĂ
della produzione e dell'ambiente utilizzando pratiche agricole a basso input e la valutazione
non distruttiva della qualitĂ 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
Assessing berry number for grapevine yield estimation by image analysis: case study with the white variety âEncruzadoâ
Mestrado em Engenharia de Viticultura e Enologia (Double degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de CiĂȘncias. Universidade do PortoNowadays, yield estimation represents one of the most important topics in viticulture. It can lead to a better vineyard management and to a better organization of harvesting operations in the vineyard and in the cellar. In recent years, image analysis has become an important tool to improve yield forecast, with the advantages of saving time and being non-invasive. This research aims to estimate the yield of the white cultivar âEncruzadoâ using visible berry number counted in the images aquired at veraison and near harvest, using a manual RGB camera and the robot VINBOT. Images were collected in laboratory and in the field at the experimental vineyard of the Instituto Superior de Agronomia (ISA) in Lisbon. In the field images the number of visible berries per canopy meter was higher at maturation than at veraison, respectively 72.6 and 66.3. Regarding the percentage of visible berries, 30.2% where visible at veraison and 24.1% at maturation. Concerning percentage of berries occluded by other berries it was observed 28.7% at veraison and 24.3% at maturation. Regression analysis showed that the number of berries in the image explained a very high proportion of bunch weight variability, R2=0.64 at veraison and 0.91 at maturation. Regression analysis also showed that the canopy porosity explained a very high proportion of visible berries variability, R2=0.81 at veraison and 0.88 at maturation. The obtained regression models underestimated the yield with an higher error at veraison than at maturation. This underestimation indicates that the use of visible berry number on the images to estimate yield still needs further research to improve the algorithms accuracyN/
High-throughput analysis and advanced search for visually-observed phenotypes
Title from PDF of title page (University of Missouri--Columbia, viewed on May 13, 2013).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Chi-Ren ShyuIncludes bibliographical references.Vita.Ph. D. University of Missouri--Columbia 2012."May 2012"The trend in many scientific disciplines today, especially in biology and genetics, is towards larger scale experiments in which a tremendous amount of data is generated. As imaging of data becomes increasingly more popular in experiments related to phenotypes, the ability to perform high-throughput big data analyses and to efficiently locate specific information within these data based on increasingly complicated and varying search criteria is of great importance to researchers. This research develops several methods for high-throughput phenotype analysis. This notably includes a registration algorithm called variable object pattern matching for mapping multiple indistinct and dynamic objects across images and detecting the presence of missing, extra, and merging objects. Research accomplishments resulted in a number of unique advanced search mechanisms including a retrieval engine that integrates multiple phenotype text sources and domain ontologies and a search method that retrieves objects based on temporal semantics and behavior. These search mechanisms represent the first of their kind in the phenotype community. While this computational framework is developed primarily for the plant community, it has potential applications in other domains including the medical field.Includes bibliographical references
Assessing Berry Number for Grapevine Yield Estimation by Image Analysis: Case Study with the Red Variety âSyrahâ
Mestrado em Engenharia de Viticultura e Enologia (Double degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de CiĂȘncias. Universidade do PortoThe yield estimation provides information that help growers to make decisions in order to optimize crop growth and to organize the harvest operations in field and in the cellar. In most vineyard estates yield is forecasted using manual methods. However, image analysis methods, which are less invasive low cost and more representative are now being developed. The main objective of this work was to estimate yield through data obtained in the frame of Vinbot project during the 2019 season. In this thesis, images of the grapevine variety Syrah taken in the laboratory and in the vineyards of the âInstituto Superior de Agronomiaâ in Lisbon were analyzed. In the laboratory the images were taken manually with an RGB camera, while in the field vines were imaged either manually and by the Vinbot robot. From these images, the number of visible berries were counted with MATLAB. From the laboratory values, the relationships between the number of visible berries and actual bunch weight and berry number were studied. From the data obtained in the field, it was analyzed the visibility of the berries at different levels of defoliation and the relationship between the area of visible bunches and the visible berries. Berry-by-berry occlusion showed a value of 6.4% at pea-size, 14.5% at veraison and 25% at maturation. In addition, high and significant determination coefficient were obtained between actual yield and visible berries. The comparison of estimated yield, obtained using the regression models with actual yield, showed an underestimation at all the three phonological stages. This low accuracy of the developed models show that the use of algorithms based on visible berry number on the images to estimate yield still needs further researchN/
UAV Oblique Imagery with an Adaptive Micro-Terrain Model for Estimation of Leaf Area Index and Height of Maize Canopy from 3D Point Clouds
Leaf area index (LAI) and height are two critical measures of maize crops that are used in ecophysiological and morphological studies for growth evaluation, health assessment, and yield prediction. However, mapping spatial and temporal variability of LAI in fields using handheld tools and traditional techniques is a tedious and costly pointwise operation that provides information only within limited areas. The objective of this study was to evaluate the reliability of mapping LAI and height of maize canopy from 3D point clouds generated from UAV oblique imagery with the adaptive micro-terrain model. The experiment was carried out in a field planted with three cultivars having different canopy shapes and four replicates covering a total area of 48 Ă 36 m. RGB images in nadir and oblique view were acquired from the maize field at six different time slots during the growing season. Images were processed by Agisoft Metashape to generate 3D point clouds using the structure from motion method and were later processed by MATLAB to obtain clean canopy structure, including height and density. The LAI was estimated by a multivariate linear regression model using crop canopy descriptors derived from the 3D point cloud, which account for height and leaf density distribution along the canopy height. A simulation analysis based on the Sine function effectively demonstrated the micro-terrain model from point clouds. For the ground truth data, a randomized block design with 24 sample areas was used to manually measure LAI, height, N-pen data, and yield during the growing season. It was found that canopy height data from the 3D point clouds has a relatively strong correlation (R2 = 0.89, 0.86, 0.78) with the manual measurement for three cultivars with CH90 . The proposed methodology allows a cost-effective high-resolution mapping of in-field LAI index extraction through UAV 3D data to be used as an alternative to the conventional LAI assessments even in inaccessible regions
Advanced Image Acquisition, Processing Techniques and Applications
"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution
Low-cost and automated phenotyping system âPhenomenonâ for multi-sensor in situ monitoring in plant in vitro culture
Background: The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable. Results: An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed. Conclusion: The technical realization of âPhenomenonâ allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time
Optimization of Single and Layered Surface Texturing
In visualization problems, surface shape is often a piece of data that must be shown effectively. One factor that strongly affects shape perception is texture. For example, patterns of texture on a surface can show the surface orientation from foreshortening or compression of the texture marks, and surface depth through size variation from perspective projection. However, texture is generally under-used in the scientific visualization community. The benefits of using texture on single surfaces also apply to layered surfaces. Layering of multiple surfaces in a single viewpoint allows direct comparison of surface shape. The studies presented in this dissertation aim to find optimal methods for texturing of both single and layered surfaces. This line of research starts with open, many-parameter experiments using human subjects to find what factors are important for optimal texturing of layered surfaces. These experiments showed that texture shape parameters are very important, and that texture brightness is critical so that shading cues are available. Also, the optimal textures seem to be task dependent; a feature finding task needed relatively little texture information, but more shape-dependent tasks needed stronger texture cues. In visualization problems, surface shape is often a piece of data that must be shown effectively. One factor that strongly affects shape perception is texture. For example, patterns of texture on a surface can show the surface orientation from foreshortening or compression of the texture marks, and surface depth through size variation from perspective projection. However, texture is generally under-used in the scientific visualization community. The benefits of using texture on single surfaces also apply to layered surfaces. Layering of multiple surfaces in a single viewpoint allows direct comparison of surface shape. The studies presented in this dissertation aim to find optimal methods for texturing of both single and layered surfaces. This line of research starts with open, many-parameter experiments using human subjects to find what factors are important for optimal texturing of layered surfaces. These experiments showed that texture shape parameters are very important, and that texture brightness is critical so that shading cues are available. Also, the optimal textures seem to be task dependent; a feature finding task needed relatively little texture information, but more shape-dependent tasks needed stronger texture cues
Grapevine yield estimation using image analysis for the variety Arinto
Mestrado em Engenharia de Viticultura e Enologia (Double Degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de CiĂȘncias. Universidade do PortoYield estimation can lead to difficulties in the vineyard and winery, if it is done inaccurately following
wrong procedures, doing a non-representative sampling or for the human error. Moreover, the
traditional yield estimation methods are time consuming and destructive because they need
someone that goes into the vineyard to count the yield components and that take out from the
vineyard inflorescence or bunches to count and weight the flowers and the berries. To avoid these
problems and the errors that can occur on this way, the development and application of new and
innovative techniques to estimate the yield through the analysis of RGB images taken under field
conditions are under study from different groups of research.
In our research work weâve studied the application of counting the yield components in the images
throughout all the growing season. Furthermore, weâve studied two different algorithms that starting
from the survey of canopy porosity and/or visible bunches area, can help to do an estimation of the
yield.
The most promising yield estimation, based on the counting of the yield components done through
image analysis, was found to be at the phenological stage of four leaves out, which shown a mean
absolute percent error (MA%E) of 32 ± 2% and an correlaion coefficient (r Obs,Est) between observed
and estimated shoots of 0.62.
The two algorithms used different models: for estimating the area of the bunches covered by leaves
and to estimate the weight of the bunches per linear canopy meter. When the area of the bunches
without leaf occlusion was estimated, an average percentage of occlusion generated by the bunches
on the other bunches of 8%, 6% and 12% respectively at pea size, veraison and maturation, was
used to estimate the total area of the bunches. When the total area of the bunches per linear canopy
meter was estimated the two models to estimate the grape weight were used. Finally, to estimate
the weight at harvest, the growth factors of 6.6 and 1.7 respectively, at pea size and veraison were
used. The first algorithm shown a MA%E, between the estimated and observed values of yield, of -
33.59%, -9.24% and -11.25%, instead the second algorithm shown a MA%E of -6.81%, -1.35% and
0.01% respectively at pea-size, veraison and maturationN/
Mineral identification using data-mining in hyperspectral infrared imagery
Les applications de lâimagerie infrarouge dans le domaine de la gĂ©ologie sont principalement des applications hyperspectrales. Elles permettent entre autre lâidentification minĂ©rale, la cartographie, ainsi que lâestimation de la portĂ©e. Le plus souvent, ces acquisitions sont rĂ©alisĂ©es in-situ soit Ă lâaide de capteurs aĂ©roportĂ©s, soit Ă lâaide de dispositifs portatifs. La dĂ©couverte de minĂ©raux indicateurs a permis dâamĂ©liorer grandement lâexploration minĂ©rale. Ceci est en partie dĂ» Ă lâutilisation dâinstruments portatifs. Dans ce contexte le dĂ©veloppement de systĂšmes automatisĂ©s permettrait dâaugmenter Ă la fois la qualitĂ© de lâexploration et la prĂ©cision de la dĂ©tection des indicateurs. Câest dans ce cadre que sâinscrit le travail menĂ© dans ce doctorat. Le sujet consistait en lâutilisation de mĂ©thodes dâapprentissage automatique appliquĂ©es Ă lâanalyse (au traitement) dâimages hyperspectrales prises dans les longueurs dâonde infrarouge. Lâobjectif recherchĂ© Ă©tant lâidentification de grains minĂ©raux de petites tailles utilisĂ©s comme indicateurs minĂ©ral -ogiques. Une application potentielle de cette recherche serait le dĂ©veloppement dâun outil logiciel dâassistance pour lâanalyse des Ă©chantillons lors de lâexploration minĂ©rale. Les expĂ©riences ont Ă©tĂ© menĂ©es en laboratoire dans la gamme relative Ă lâinfrarouge thermique (Long Wave InfraRed, LWIR) de 7.7m Ă 11.8 m. Ces essais ont permis de proposer une mĂ©thode pour calculer lâannulation du continuum. La mĂ©thode utilisĂ©e lors de ces essais utilise la factorisation matricielle non nĂ©gative (NMF). En utlisant une factorisation du premier ordre on peut dĂ©duire le rayonnement de pĂ©nĂ©tration, lequel peut ensuite ĂȘtre comparĂ© et analysĂ© par rapport Ă dâautres mĂ©thodes plus communes. Lâanalyse des rĂ©sultats spectraux en comparaison avec plusieurs bibliothĂšques existantes de donnĂ©es a permis de mettre en Ă©vidence la suppression du continuum. Les expĂ©rience ayant menĂ©s Ă ce rĂ©sultat ont Ă©tĂ© conduites en utilisant une plaque Infragold ainsi quâun objectif macro LWIR. Lâidentification automatique de grains de diffĂ©rents matĂ©riaux tels que la pyrope, lâolivine et le quartz a commencĂ©. Lors dâune phase de comparaison entre des approches supervisĂ©es et non supervisĂ©es, cette derniĂšre sâest montrĂ©e plus appropriĂ© en raison du comportement indĂ©pendant par rapport Ă lâĂ©tape dâentraĂźnement. Afin de confirmer la qualitĂ© de ces rĂ©sultats quatre expĂ©riences ont Ă©tĂ© menĂ©es. Lors dâune premiĂšre expĂ©rience deux algorithmes ont Ă©tĂ© Ă©valuĂ©s pour application de regroupements en utilisant lâapproche FCC (False Colour Composite). Cet essai a permis dâobserver une vitesse de convergence, jusquâa vingt fois plus rapide, ainsi quâune efficacitĂ© significativement accrue concernant lâidentification en comparaison des rĂ©sultats de la littĂ©rature. Cependant des essais effectuĂ©s sur des donnĂ©es LWIR ont montrĂ© un manque de prĂ©diction de la surface du grain lorsque les grains Ă©taient irrĂ©guliers avec prĂ©sence dâagrĂ©gats minĂ©raux. La seconde expĂ©rience a consistĂ©, en une analyse quantitaive comparative entre deux bases de donnĂ©es de Ground Truth (GT), nommĂ©e rigid-GT et observed-GT (rigide-GT: Ă©tiquet manuel de la rĂ©gion, observĂ©e-GT:Ă©tiquetage manuel les pixels). La prĂ©cision des rĂ©sultats Ă©tait 1.5 fois meilleur lorsque lâon a utlisĂ© la base de donnĂ©es observed-GT que rigid-GT. Pour les deux derniĂšres epxĂ©rience, des donnĂ©es venant dâun MEB (Microscope Ălectronique Ă Balayage) ainsi que dâun microscopie Ă fluorescence (XRF) ont Ă©tĂ© ajoutĂ©es. Ces donnĂ©es ont permis dâintroduire des informations relatives tant aux agrĂ©gats minĂ©raux quâĂ la surface des grains. Les rĂ©sultats ont Ă©tĂ© comparĂ©s par des techniques dâidentification automatique des minĂ©raux, utilisant ArcGIS. Cette derniĂšre a montrĂ© une performance prometteuse quand Ă lâidentification automatique et Ă aussi Ă©tĂ© utilisĂ©e pour la GT de validation. Dans lâensemble, les quatre mĂ©thodes de cette thĂšse reprĂ©sentent des mĂ©thodologies bĂ©nĂ©fiques pour lâidentification des minĂ©raux. Ces mĂ©thodes prĂ©sentent lâavantage dâĂȘtre non-destructives, relativement prĂ©cises et dâavoir un faible coĂ»t en temps calcul ce qui pourrait les qualifier pour ĂȘtre utilisĂ©e dans des conditions de laboratoire ou sur le terrain.The geological applications of hyperspectral infrared imagery mainly consist in mineral identification, mapping, airborne or portable instruments, and core logging. Finding the mineral indicators offer considerable benefits in terms of mineralogy and mineral exploration which usually involves application of portable instrument and core logging. Moreover, faster and more mechanized systems development increases the precision of identifying mineral indicators and avoid any possible mis-classification. Therefore, the objective of this thesis was to create a tool to using hyperspectral infrared imagery and process the data through image analysis and machine learning methods to identify small size mineral grains used as mineral indicators. This system would be applied for different circumstances to provide an assistant for geological analysis and mineralogy exploration. The experiments were conducted in laboratory conditions in the long-wave infrared (7.7ÎŒm to 11.8ÎŒm - LWIR), with a LWIR-macro lens (to improve spatial resolution), an Infragold plate, and a heating source. The process began with a method to calculate the continuum removal. The approach is the application of Non-negative Matrix Factorization (NMF) to extract Rank-1 NMF and estimate the down-welling radiance and then compare it with other conventional methods. The results indicate successful suppression of the continuum from the spectra and enable the spectra to be compared with spectral libraries. Afterwards, to have an automated system, supervised and unsupervised approaches have been tested for identification of pyrope, olivine and quartz grains. The results indicated that the unsupervised approach was more suitable due to independent behavior against training stage. Once these results obtained, two algorithms were tested to create False Color Composites (FCC) applying a clustering approach. The results of this comparison indicate significant computational efficiency (more than 20 times faster) and promising performance for mineral identification. Finally, the reliability of the automated LWIR hyperspectral infrared mineral identification has been tested and the difficulty for identification of the irregular grainâs surface along with the mineral aggregates has been verified. The results were compared to two different Ground Truth(GT) (i.e. rigid-GT and observed-GT) for quantitative calculation. Observed-GT increased the accuracy up to 1.5 times than rigid-GT. The samples were also examined by Micro X-ray Fluorescence (XRF) and Scanning Electron Microscope (SEM) in order to retrieve information for the mineral aggregates and the grainâs surface (biotite, epidote, goethite, diopside, smithsonite, tourmaline, kyanite, scheelite, pyrope, olivine, and quartz). The results of XRF imagery compared with automatic mineral identification techniques, using ArcGIS, and represented a promising performance for automatic identification and have been used for GT validation. In overall, the four methods (i.e. 1.Continuum removal methods; 2. Classification or clustering methods for mineral identification; 3. Two algorithms for clustering of mineral spectra; 4. Reliability verification) in this thesis represent beneficial methodologies to identify minerals. These methods have the advantages to be a non-destructive, relatively accurate and have low computational complexity that might be used to identify and assess mineral grains in the laboratory conditions or in the field
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