Image Analysis using Color Co-occurrence Matrix Textural Features for Predicting Nitrogen Content in Spinach

This study aimed to determine the nitrogen content of spinach leaves by using computer imaging technology. The application of Color Co-occurrence Matrix (CCM) texture analysis was used to recognize the pattern of nitrogen content in spinach leaves. The texture analysis consisted of 40 CCM textural features constructed from RGB and grey colors. From the 40 textural features, the best features-subset was selected by using features selection method. Features selection method can increase the accuracy of image analysis using ANN model to predict nitrogen content of spinach leaves. The combination of ANN with Ant Colony Optimization resulted in the most optimal modelling with mean square error validation value of 0.0000083 and the R2 testing-set data = 0.99 by using 10 CCM textural features as the input of ANN. The computer vision method using ANN model which has been developed can be used as non-invasive sensing device to predict nitrogen content of spinach and for guiding farmers in the accurate application of their nitrogen fertilization strategies using low cost computer imaging technology

Gyroscope Pivot Bearing Dimension and Surface Defect Detection

Because of the perceived lack of systematic analysis in illumination system design processes and a lack of criteria for design methods in vision detection a method for the design of a task-oriented illumination system is proposed. After detecting the micro-defects of a gyroscope pivot bearing with a high curvature glabrous surface and analyzing the characteristics of the surface detection and reflection model, a complex illumination system with coaxial and ring lights is proposed. The illumination system is then optimized based on the analysis of illuminance uniformity of target regions by simulation and grey scale uniformity and articulation that are calculated from grey imagery. Currently, in order to apply the Pulse Coupled Neural Network (PCNN) method, structural parameters must be tested and adjusted repeatedly. Therefore, this paper proposes the use of a particle swarm optimization (PSO) algorithm, in which the maximum between cluster variance rules is used as fitness function with a linearily reduced inertia factor. This algorithm is used to adaptively set PCNN connection coefficients and dynamic threshold, which avoids algorithmic precocity and local oscillations. The proposed method is used for pivot bearing defect image processing. The segmentation results of the maximum entropy and minimum error method and the one described in this paper are compared using buffer region matching, and the experimental results show that the method of this paper is effective

Robotic concrete inspection with illumination-enhancement

Existing automated concrete inspection methods are intractable: capturing images under ambient conditions which can vary substantially. Furthermore, an opportunity may have been overlooked: utilizing illumination techniques to enhance defect contrast during imaging which may improve automatic defect detection accuracy. In this work, we present a robotic-mountable lighting apparatus that implements contrast enhancing illumination techniques in an automated package in order to improve crack detection and classification in concrete. Geometrical lighting techniques; directional and angled, were tested on three cracked concrete slab samples. Results from blind/referenceless image spatial quality evaluation (BRISQUE) show that both directional and varied angled lighting influence the quality in different associated regions in an image. Furthermore, the region-based crack detection algorithm Faster R-CNN attained a higher accuracy when images were enhanced with directional lighting during all samples tested. The direction of highest accuracy was not consistent over samples, and is likely dependant on features such as crack location, width, orientation etc. This emphasises the importance of adaptive lighting: illuminating the surface with the most suitable conditions based on an initial observation of the feature or defect. This system represents the initial step in a fully-automated and optimised concrete inspection system capable of defect capture, classification, localization and segmentation

BİLGİSAYARLI GÖRÜ SİSTEMLERİ İÇİN SİSTEM TASARIMI VE KONTROLÜ

Bilgisayarlı görme uygulamaları için aydınlatma ve sahne önemlidir. Nesnelerin özelliklerini belirlemek ve istenilen kalitede görüntü alabilmek, aydınlatma koşullarının olabildiğince sabit ve kontrollü olması ile sağlanır. Çalışmada bilgisayarlı görü uygulamalarında görüntünün dış koşullardan etkilenmemesi için bir ışık havuzu oluşturulmuştur. Sistemindeki LED aydınlatma armatürlerinin sürülmesi, ışık miktarının ve renk yelpazesinin bilgisayar ile kontrol edilmesi sağlanmıştır. Ayrıca iki farklı aydınlatma kaynağı kullanılarak mesafelere göre ışık havuzunun orta noktasına düşen aydınlık düzeyinin hem lüksmetre hem de görüntü işleme teknikleri ile belirlenmiştir

A novel resource-constrained insect monitoring system based on machine vision with edge AI

Effective insect pest monitoring is a vital component of Integrated Pest Management (IPM) strategies. It helps to support crop productivity while minimising the need for plant protection products. In recent years, many researchers have considered the integration of intelligence into such systems in the context of the Smart Agriculture research agenda. This paper describes the development of a smart pest monitoring system, developed in accordance with specific requirements associated with the agricultural sector. The proposed system is a low-cost smart insect trap, for use in orchards, that detects specific insect species that are detrimental to fruit quality. The system helps to identify the invasive insect, Brown Marmorated Stink Bug (BMSB) or Halyomorpha halys (HH) using a Microcontroller Unit-based edge device comprising of an Internet of Things enabled, resource-constrained image acquisition and processing system. It is used to execute our proposed lightweight image analysis algorithm and Convolutional Neural Network (CNN) model for insect detection and classification, respectively. The prototype device is currently deployed in an orchard in Italy. The preliminary experimental results show over 70 percent of accuracy in BMSB classification on our custom-built dataset, demonstrating the proposed system feasibility and effectiveness in monitoring this invasive insect species

A comparative study on the performance of neural networks in visual guidance and feedback applications

Vision-based systems increase the flexibility of industrial automation applications by providing non-touching sensory information for processing and feedback. Artificial neural networks (ANNs) help such conformities through prediction in overcoming nonlinear computational spaces. They transform multiple possibilities of outcomes or regions of uncertainty posed by the system components towards solution spaces. Trained networks impart a certain level of intelligence to robotic systems. This paper discusses two applications of machine vision. The 3 degrees of freedom (DOF) robotic assembly provides an accurate cutting of soft materials with visual guidance using pixel elimination. The 6-DOF robot combines visual guidance from a supervisory camera and visual feedback from an attached camera. Using a switching approach in the control strategy, pick and place applications are carried out. With the inclusion of ANN to make the strategies intelligent, both the systems performed better with regard to computational time and convergence. The networks make use of the extracted image features from the scene for different applications. Simulation and experimental results validate the proposed schemes and show the effectiveness of ANN in machine vision applications

Analysis of Outage Probability of WLAN & Evaluating Geometry and Coverage of Energy Efficient Light Sources

In this thesis,the performance of energy efficient light sources in free space is analyzed. Metrics for irradiance based coverage of a light source are proposed and evaluated analytically.These light sources are generated by arranging point sources in various geometries.The coverage metrics of these sources are calculated over a circular region.Numerical results are then obtained to determine the efficiency of these sources, highlighting the usefulness of this work

Avaliação experimental das fontes de incerteza associadas à visão de máquina para subsídio ao projeto de um sistema automatizado de calibração de manômetros

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Mecânica, Florianópolis, 2015.A calibração automatizada de manômetros analógicos, através da leitura automática dos instrumentos realizada pela visão de máquina, pode aumentar a qualidade do processo, a partir da redução de incertezas da medição, que são obtidas pela automação da calibração que reduzem erros devido à influência humana sobre o processo e pelo aumento do número de pontos de calibração. Neste trabalho realiza-se uma análise metrológica de como os parâmetros construtivos (distância e alinhamento entre o sistema de captura de imagem e o manômetro; quantidade de manômetros lidos simultaneamente numa mesma imagem; e formato do painel de manômetros) influem sobre o resultado da medição. Para isso realiza-se a identificação das fontes de incertezas que são quantificadas através de métodos experimentais, nos quais são variados: a distância e o alinhamento entre o sistema de captura de imagem e o manômetro, a quantidade de manômetros lidos simultaneamente numa mesma imagem e o formato do painel de manômetros. Para tanto se construiu uma bancada capaz de emular automaticamente as configurações desejadas. Os dados obtidos experimentalmente foram analisados e seus resultados elucidam como a variação dos parâmetros construtivos influencia sobre o resultado da medição. Esses resultados podem ser utilizados para subsídio ao projeto de um sistema automatizado de calibração de manômetros. Abstract : Automated analog gauged calibration that uses automatic machine vision techniques to read the instruments can increase the quality of the reading process. It does so by reducing measurement uncertainties, which are obtained by the automation of the calibration process, which, in turn, reduces the errors that come as a result of human influence in the process, and increases the number of calibration points. In this project, a metrological analysis is made to determine how the constructive parameters (distance and alignment between the image capturing system and the pressure gauge; amount of pressure gauges read simultaneously in a single image; and the gauges panel format) influence the final result of the measurement process. Therefore, it is necessary to identify the sources of uncertainties and quantify the amount of error with experimental methods, in which are the aforementioned variables are set to different configurations and tested. For this purpose a new test rig was built to automatically emulate the desired settings. The experimental data was analyzed and the results elucidate how the variation of constructive parameters influences the measurement result. These results can be used to aid in the design of an automated gauge calibration system

Automated Soil Classification And Identification Using Machine Vision And Artificial Neural Networks

DissertationIntroduction: In the construction industry, one of the major considerations when designing a superstructure is which foundation should be selected. Foundations provide support to superstructures by transferring the load of the structure evenly into the earth. An inappropriate foundation choice could result in damage to the superstructure, or even the collapse of such a structure. The clay content of soil is a major determining factor when selecting a foundation type. Soil containing clay, has the potential to shrink and swell as the water content changes. This heaving of the soil can cause damage to the superstructure built upon it. Determining the amount of clay in a soil sample, is one of the most important steps in the soil classification process. In South Africa, the Hydrometer method is commonly used to determine the clay content of soil samples. This method is a manual, time intensive soil classification method, with doubtful accuracy. This study was undertaken to develop an Automated Soil Classification System (ASCS) that will classify soil more accurately and more expeditiously, making it cost and time effective. This was achieved by applying a Machine Vision (MV) process to soil samples, to generate unique digital soil sample fingerprints for soil samples. This process was then combined with an Artificial Neural Network (ANN), to automatically classify the soil sample from the fingerprints. Methods: Initially a Machine Vision Instrument (MVI) was constructed for the consistent capturing of high fidelity images during the sedimentation process of a soil sample. Software was then developed to process these captured images and generate unique Soil Sample (SS) fingerprints for different soil constitutions. Four investigations were preformed to validate the consistency of the SS fingerprints generated with the MVI. These investigations were: 1. Validation of the SS fingerprint generation process; 2. Validation of the soil sample preparation procedure; 3. Determination of the differentiation ability of the MVI; and 4. Validation of the MVI by generating SS fingerprints for coded (unknown) soil samples. The generated SS fingerprints were then used to train an ANN to recognise and classify soil samples from their respective SS fingerprints. After the training of the ANN, a fifth investigation was undertaken determine the accuracy of the trained ANN and a final, sixth investigation was undertaken to compare the performance of the ASCS to that of the Hydrometer method. Results: The constructed MVI was able to acquire good quality greyscale images during the sedimentation process of soil samples in a consistent manner. Investigations 1 through 4 showed that correlation amongst SS fingerprints, generated from the same soil sample, was in the order of 97%, while the correlation amongst SS fingerprints, generated from multiple soils samples of the same constitution, was in the order of 95%. Investigation five showed that the training of the ANN was successful as the R values obtained after training were greater than 0,98. The sixth and final investigation showed that the accuracy of the ASCS was in the range of 95% and greatly outperformed the Hydrometer method, who’s accuracy varied from approximately 49 to 89%. The ASCS also delivered these results in 28 hours while the Hydrometer method took approximately seven days