Fractals Study and Its Application

The overall of this paper is a review of fractal in many areas of application. The review exposes fractal definition, analysis, and its application. Most applications discussed are based on analysis from geometric and image processing studies. Patterns of some fractals will be discussed. Some simulation results are supplied to illustrate the discussion. Simulation resulted are from various software and tools. Some principles of fractals with informative patterns have been simulated. Whereas the simulations could support some recommendations for prospective purposes and applications. The prospective application may help in predictive pattern of many fields. The predictive pattern will lead to pattern control and pattern disruptions

Colour and Naming in Healthy and Aphasic People

Abstract The purpose of this study was to create a paradigm suitable for people with aphasia and healthy subjects to evaluate the influence of colour on naming pictures of objects. We designed a completely new stimulus set based on images of 140 common real objects that were inspired by the Snodgrass and Vanderwart picture set (1980). We were especially interested whether there is a difference in performance between the aphasic patients and the group of healthy controls. Adding chromatic information to pictures of objects shows only a small effect in verification and categorisation tasks. However, when observers are required to name objects, colour speeds performance and enhances accuracy (Rossion & Pourtois, 2004). The present study contrasts two different claims as to why colour may benefit object naming. The first is that colour simply aids the segmentation of the object from its background (Wichmann et al., 2002). The second is that colour may help to elicit a wider range of associations with the object, thereby enhancing lexical access (Bisiach, 1966). To distinguish between these processes an equal number of pictures containing high and low colour diagnostic objects were presented against either fractal noise or uniform backgrounds in a naming task to aphasic subjects with anomia and to healthy controls. Performance for chromatic stimuli was compared with that for monochrome stimuli equated in luminance. Results show that colour facilitates naming significantly in both subject groups and there was no significant difference between objects with high or low colour diagnostic values. We also found that object segmentation and the lexical access seem to occur in parallel processes, rather than in an additive way

Customized sorting and packaging machine

India is a country which has a cornerstone of agriculture. And as it comes to fruit/vegetable sorting and packaging in India, human labor has been a vital part. With manual hand picking, it is a very laborious task to classify the quality of fruits/vegetables and simultaneously pack them. One leading-edge technology for the fulfilment of this purpose is ‘Image Processing’ technology which is extremely fast and cost-efficient. Our whole idea revolves around the fact that each fruit will be inspected, sort and simultaneously packed. For the same, a low cost automated mechatronic system has designed consisting of a solitary mechanical arrangement, which is controlled and synchronized through electronic components. Fruits/vegetables are sorted as high-quality and low-quality on the basis of physical appearance and weight. For this, a suitable algorithm is designed using the Open CV library. And the sorting is done using Arduino Uno and Raspberry pi. Hence the aim is to develop a sorting and packaging facility that can be established at the very root level itself which will be economically compact and accurate and will give more justice to farmers

GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer

This thesis is divided into two parts:Part I: Analysis of Fruits, Vegetables, Cheese and Fish based on Image Processing using Computer Vision and Deep Learning: A Review. It consists of a comprehensive review of image processing, computer vision and deep learning techniques applied to carry out analysis of fruits, vegetables, cheese and fish.This part also serves as a literature review for Part II.Part II: GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer. This part introduces to an end-to-end deep neural network architecture that can predict the degree of acceptability by the consumer for a guava based on sensory evaluation

Computer vision based classification of fruits and vegetables for self-checkout at supermarkets

The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be divided into many significant fields namely computer vision, data sciences, real time analytics and Natural Language Processing (NLP). All these applications are being used to help computer based systems to operate more usefully in everyday contexts. Computer vision research is currently active in a wide range of areas such as the development of autonomous vehicles, object recognition, Content Based Image Retrieval (CBIR), image segmentation and terrestrial analysis from space (i.e. crop estimation). Despite significant prior research, the area of object recognition still has many topics to be explored. This PhD thesis focuses on using advanced machine learning approaches to enable the automated recognition of fresh produce (i.e. fruits and vegetables) at supermarket self-checkouts. This type of complex classification task is one of the most recently emerging applications of advanced computer vision approaches and is a productive research topic in this field due to the limited means of representing the features and machine learning techniques for classification. Fruits and vegetables offer significant inter and intra class variance in weight, shape, size, colour and texture which makes the classification challenging. The applications of effective fruit and vegetable classification have significant importance in daily life e.g. crop estimation, fruit classification, robotic harvesting, fruit quality assessment, etc. One potential application for this fruit and vegetable classification capability is for supermarket self-checkouts. Increasingly, supermarkets are introducing self-checkouts in stores to make the checkout process easier and faster. However, there are a number of challenges with this as all goods cannot readily be sold with packaging and barcodes, for instance loose fresh items (e.g. fruits and vegetables). Adding barcodes to these types of items individually is impractical and pre-packaging limits the freedom of choice when selecting fruits and vegetables and creates additional waste, hence reducing customer satisfaction. The current situation, which relies on customers correctly identifying produce themselves leaves open the potential for incorrect billing either due to inadvertent error, or due to intentional fraudulent misclassification resulting in financial losses for the store. To address this identified problem, the main goals of this PhD work are: (a) exploring the types of visual and non-visual sensors that could be incorporated into a self-checkout system for classification of fruits and vegetables, (b) determining a suitable feature representation method for fresh produce items available at supermarkets, (c) identifying optimal machine learning techniques for classification within this context and (d) evaluating our work relative to the state-of-the-art object classification results presented in the literature. An in-depth analysis of related computer vision literature and techniques is performed to identify and implement the possible solutions. A progressive process distribution approach is used for this project where the task of computer vision based fruit and vegetables classification is divided into pre-processing and classification techniques. Different classification techniques have been implemented and evaluated as possible solution for this problem. Both visual and non-visual features of fruit and vegetables are exploited to perform the classification. Novel classification techniques have been carefully developed to deal with the complex and highly variant physical features of fruit and vegetables while taking advantages of both visual and non-visual features. The capability of classification techniques is tested in individual and ensemble manner to achieved the higher effectiveness. Significant results have been obtained where it can be concluded that the fruit and vegetables classification is complex task with many challenges involved. It is also observed that a larger dataset can better comprehend the complex variant features of fruit and vegetables. Complex multidimensional features can be extracted from the larger datasets to generalise on higher number of classes. However, development of a larger multiclass dataset is an expensive and time consuming process. The effectiveness of classification techniques can be significantly improved by subtracting the background occlusions and complexities. It is also worth mentioning that ensemble of simple and less complicated classification techniques can achieve effective results even if applied to less number of features for smaller number of classes. The combination of visual and nonvisual features can reduce the struggle of a classification technique to deal with higher number of classes with similar physical features. Classification of fruit and vegetables with similar physical features (i.e. colour and texture) needs careful estimation and hyper-dimensional embedding of visual features. Implementing rigorous classification penalties as loss function can achieve this goal at the cost of time and computational requirements. There is a significant need to develop larger datasets for different fruit and vegetables related computer vision applications. Considering more sophisticated loss function penalties and discriminative hyper-dimensional features embedding techniques can significantly improve the effectiveness of the classification techniques for the fruit and vegetables applications

Implementation of a EU wide indicator for the rural-agrarian landscape In support of COM(2006)508 “Development of agri-environmental indicators for monitoring the integration of environmental concerns into the Common Agricultural Policy”

The report explains the conceptual and methodological development of the agrienvironmental indicator on landscape state and diversity, calculated in support of COM(2006)508 “Development of agri-environmental indicators for monitoring the integration of environmental concerns into the common agricultural policy”. The indicator is based on three components: the degree of naturalness of the rural-agrarian landscape, intended as the influence exerted by society on the agrarian landscape with its agricultural activities and modifications of the original natural state introduced by farming practices; the physical structure, intended as land cover and its spatial organisation as a product of land management (organisation of different land cover types, plot size, fragmentation, diversity etc.); the societal awareness of the rural-agrarian landscape, as the society perceives, values and assesses landscape quality; the society plans, manages, and uses the landscape for productive or non productive purposes.JRC.H.1-Water Resource

OBTENCIÓN DE INGREDIENTES FUNCIONALES PARA LA FORMULACIÓN DE ALIMENTOS ENRIQUECIDOS CON EXTRACTOS VEGETALES. INFLUENCIA DEL TRATAMIENTO DE CONSERVACIÓN SOBRE ALGUNOS COMPUESTOS BIOACTIVOS

Tesis por compendioLa presente tesis doctoral se centra en la obtención de nuevos ingredientes ricos en compuestos bioactivos a partir de tejidos vegetales (caqui y pimiento) sometidos a distintos tratamientos de conservación como las altas presiones hidrostáticas (APH) y la pasteurización, con la finalidad de formular nuevos alimentos funcionales. Se estudió el efecto de un tratamiento específico de APH (200 MPa/6 min/25 ºC) y otro de pasteurización (70 ºC/15min) sobre la estructura y el contenido en algunos compuestos bioactivos del caqui. Tanto las APH como la pasteurización causaron cambios estructurales en el tejido parenquimático, favorecieron la precipitación de taninos y la formación de células tánicas, lo que podría relacionarse con la pérdida de astringencia del fruto. Las APH mejoraron la extractabilidad de compuestos carotenoides y mantuvieron las propiedades antioxidantes del fruto. Esta técnica podría ser una alternativa al tratamiento de pasteurización convencional. Asimismo el caqui tratado por APH podría ser empleado en la formulación de nuevos alimentos funcionales, tales como bebidas lácteas enriquecidas con caqui. Las nuevas bebidas lácteas, con idéntico contenido en carotenoides, se formularon haciendo uso de caqui no tratado, sometido a APH y pasteurizado; y tres matrices lácteas diferentes: leche entera, semi-desnatada y desnatada. Las bebidas elaboradas con caqui tratado por APH presentaron unas adecuadas propiedades reológicas ya que ni gelificaron como las elaboradas con caqui no tratado, ni sedimentaron como las formuladas con caqui pasteurizado. Los consumidores percibieron las nuevas bebidas lácteas enriquecidas con caqui como bebidas altamente antioxidantes. Las que más gustaron fueron las elaboradas con caqui tratado por APH independientemente del tipo de leche utilizada y las elaboradas con caqui no tratado y leche entera. Por tanto, el tratamiento por APH permite formular bebidas lácteas enriquecidas con caqui con alto valor nutricional, variable contenido graso y elevada aceptabilidad independientemente de la estacionalidad del fruto. Por otro lado, se cuantificaron y localizaron algunos compuestos bioactivos y se determinaron algunas propiedades fisicoquímicas en tres tipos de pimiento: rojo, verde y amarillo. El contenido en compuestos bioactivos de cada tipo de pimiento estuvo condicionado por su estructura. El tipo de pimiento más adecuado para obtener extractos ricos en compuestos carotenoides sería el rojo, mientras que el amarillo sería apropiado para obtener extractos ricos en compuestos fenólicos con elevada actividad antioxidante. Por último, si se pretende obtener extractos con elevado contenido en fibra dietética el más adecuado sería el pimiento verde. Se estudió el efecto de diferentes tratamientos de APH (100, 200, 300 y 500 MPa/15 min/25 ºC) y de un tratamiento de pasteurización (70 ºC/10 min) sobre la estructura de pimiento rojo. Además, se determinó el efecto de dichos tratamientos sobre el contenido en algunos compuestos bioactivos y textura. Tanto las APH como la pasteurización provocaron cambios microestructurales, aunque los tratamientos que menos impacto tuvieron fueron las APH a 500 MPa y la pasteurización. Estos tratamientos fueron a su vez los que menos afectaron al contenido en compuestos bioactivos y textura del pimiento rojo. Las APH podrían ser una alternativa a la pasteurización convencional dado que el contenido en compuestos bioactivos y la textura fue similar en ambos casos. Asimismo, podrían desarrollarse nuevos alimentos funcionales mediante el uso de tejido de pimiento rojo sometido a APH a 500 MPa y/o pasteurización. Las modificaciones microestructurales causadas en el tejido de pimiento rojo como consecuencia de la aplicación de APH y pasteurización, provocaron variaciones en los parámetros morfométricos y de textura de la imagen. La dimensión fractal de textura, el contraste, el momento de diferencia inversa y la entropía fueron parámetros de textura apropiados para caracterizar el efecto de las APH y la pasteurización sobre la textura de pimiento rojo. El daño celular causado por los tratamientos de conservación se observó mejor a escalas bajas. Para el desarrollo de las nuevas salsas bechamel enriquecidas con pimiento rojo se emplearon dos tipos de almidón de maíz (nativo y modificado) a dos concentraciones diferentes (4 y 6 g/100 g) y diferentes cantidades de pimiento (0, 5 y 15 g/100 g). Se estudiaron sus propiedades reológicas, microestructura y características sensoriales. El efecto de la incorporación de pimiento sobre las propiedades reológicas dependió del tipo de almidón utilizado. Las salsas presentaron una considerable auto-fluorescencia intrínseca debido al elevado contenido en carotenoides del pimiento. Las salsas que más gustaron a los consumidores fueron las elaboradas con almidón modificado, más cremosas y consistentes. Los consumidores las encontraron beneficiosas para la salud ya que el pimiento rojo proporciona antioxidantes y valor nutricional y mejora el sabor de la salsa. Así, sería posible formular nuevas salsas bechamel, funcionales, cremosas, con alto valor nutricional, elevada aceptabilidad, buenas propiedades reológicas y estabilidad con pimiento y almidón modificado.Hernández Carrión, M. (2014). OBTENCIÓN DE INGREDIENTES FUNCIONALES PARA LA FORMULACIÓN DE ALIMENTOS ENRIQUECIDOS CON EXTRACTOS VEGETALES. INFLUENCIA DEL TRATAMIENTO DE CONSERVACIÓN SOBRE ALGUNOS COMPUESTOS BIOACTIVOS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48482TESISCompendi

Characterizing the tissue of apple air-dried and osmo-air-dried rings by X-CT and OCT and relationship with ring crispness and fruit maturity at harvest measured by TRS

Air-dried apple rings were prepared from ‘Golden Delicious’ apples selected at harvest as less mature and more mature according to the absorption coefficient measured at 670 nm by time-resolved reflectance spectroscopy (TRS), stored in air for 5 months, and subjected to air-drying with (OSMO) and without (noOSMO) osmodehydration pre-treatment (60% sucrose syrup). Selected rings were submitted to microstructural analysis by X-ray computed tomography (X-CT), to subsurface structure analysis by optical coherence tomography (OCT) and to texture and sound emission analysis by bending–snapping test. Higher crispness index, higher number of sound events and higher average sound pressure level (SPL) characterized the OSMO rings. Total porosity was related to SPLav 60, pore fragmentation index to fracturability and specific surface area to the work required to snap the ring. A differentiation of the drying treatments, as well as of the products according to the TRS maturity class at harvest was obtained analyzing by principal component analysis (PCA) microstructure parameters and texture and acoustic parameters. The differences in mechanical and acoustic characteristics between OSMO and noOSMO rings were due to the different subsurface structure as found with OCT analysis

Color Analysis and Image Processing Applied in Agriculture

Color and appearance are perhaps the first attributes that attract us to a fruit or vegetable. Since the appearance of the product generally determines whether a product is accepted or rejected, measuring the color characteristics becomes an important task. To carry out the analysis of this key attribute for agriculture, it is recommended to use an artificial vision system to capture the images of the samples and then to process them by applying colorimetric routines to extract color parameters in an efficient and nondestructive manner, which makes it a suitable tool for a wide range of applications. The purpose of this chapter is to give an overview on recent development of image processing applied to color analysis from horticultural products, more specifically the practical usage of color image analysis in agriculture. As an example, quantitative values of color are extracted from Habanero Chili Peppers using image processing; the images from the samples were obtained using a desktop configuration of machine vision system. The material presented should be useful for students starting on the field, as well as for researchers looking for state-of-the-art studies and practical applications