On-line measurement of broken rice percentage from image analysis of length and shape

This thesis investigates the design of an on-line signal processing system to measure the percentage of a batch of rice grains that are broken. The objectives of the investigation were: to provide information contributing to the hardware design; to devise one or more approaches for each processing stage; to evaluate and validate these approaches, where necessary recommending the most suitable; and to verify the overall accuracy and robustness of the proposed system. The fundamental processing stages were proposed and, for each stage, the possible approaches were investigated. A configurable experimental apparatus was assembled to enable an iterative development of algorithm and hardware design: an important aspect of the methodology was to locate an acceptable operating point in the space of possible configurations. The processing stages comprised geometric camera calibration; segmentation of the grains from the background; detection of single grains; measurement of grain length and characterisation of shape; classification; and finally corrections for bias and conversion to the required unit of measurement, percentage by mass. For each stage, an analysis of error and uncertainty was undertaken. This provides an indication of the expected accuracy and bias of the proposed system. There were several elements of novel work completed in the course of this investigation. Some novel components were introduced into the calibration procedure; a tiled approach was used in the segmentation stage, to accommodate diverse illumination across the field of view. For the shape analysis, an innovative method is employed to estimate the posterior class density, and the analysis of expected bias includes a treatment of finite aperture but also how the probability of observing a single grain is conditional upon its length. The results obtained from the experimental apparatus and prototype device indicate that it is feasible to obtain on-line measurements that are well within the required tolerance. This would enable such a device to be deployed in a number of important industrial applications

Analysis of Flow, Breakage and Coating of Corn Seeds in a Mixer

In the seed processing industry, rotary batch seed coaters are widely used for providing a protective coating layer to the seeds consisting of various ingredients including fertilisers and crop protection chemicals. Seed motion and mixing are important in ensuring uniform coating; hence the focus of this work is to address the mixing efficiency and coating uniformity of corn seeds in a rotary batch seed mixer. In the present study, the Discrete Element Method (DEM) is used to analyse the motion and coating uniformity of the seeds. A comprehensive study has been carried out addressing the influence of the shape of corn seeds for accurately simulating their flow in the mixer using two commonly used techniques: (i) manipulation of coefficient of rolling friction of spherical particles, and (ii) clumping multiple spheres. Both methods were successful in simulating the flow of seeds in the mixer, however the former method is found to be an empirical approach rather than predictive. A coating model is used for predicting the coating uniformity of corn seeds in the mixer. Effect of various process parameters on variation of coating mass among the seeds is investigated. For the seed mixer, the baffle clearance gap, baffle geometry and position of the atomiser disc were found to be the key influential process parameters affecting the coating uniformity of corn seeds. Other process parameters such as the base rotational speed, baffle angle, width and curvature had less effects on coating variability. A study has also been carried out on tailoring the existing methods of measuring the extent of breakage of particles for seeds. Four breakage criteria were proposed and assessed for consideration of mass of broken seeds, and the most suitable methods are suggested. The simulations developed here are generic and can be applied to a wide range of coating processes, such as particle and tablet coating. The proposed methodology for measuring the extent of breakage of corn seeds can also be used for other types of seeds

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Active shape models with focus on overlapping problems applied to plant detection and soil pore analysis

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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

Using digital image analysis for assessing the quality of wheat and barley

This thesis explores the issues involved in developing a relatively low-cost digital imaging analysis (DIA) system fot the quality assessment of wheat and barley using commonly available equipment. It also explores the capability of such a system to provide rapid and accurate assessments.Master of Applied Science by researc

Deep Vision in Optical Imagery: From Perception to Reasoning

Deep learning has achieved extraordinary success in a wide range of tasks in computer vision field over the past years. Remote sensing data present different properties as compared to natural images/videos, due to their unique imaging technique, shooting angle, etc. For instance, hyperspectral images usually have hundreds of spectral bands, offering additional information, and the size of objects (e.g., vehicles) in remote sensing images is quite limited, which brings challenges for detection or segmentation tasks. This thesis focuses on two kinds of remote sensing data, namely hyper/multi-spectral and high-resolution images, and explores several methods to try to find answers to the following questions: - In comparison with natural images or videos in computer vision, the unique asset of hyper/multi-spectral data is their rich spectral information. But what this “additional” information brings for learning a network? And how do we take full advantage of these spectral bands? - Remote sensing images at high resolution have pretty different characteristics, bringing challenges for several tasks, for example, small object segmentation. Can we devise tailored networks for such tasks? - Deep networks have produced stunning results in a variety of perception tasks, e.g., image classification, object detection, and semantic segmentation. While the capacity to reason about relations over space is vital for intelligent species. Can a network/module with the capacity of reasoning benefit to parsing remote sensing data? To this end, a couple of networks are devised to figure out what a network learns from hyperspectral images and how to efficiently use spectral bands. In addition, a multi-task learning network is investigated for the instance segmentation of vehicles from aerial images and videos. Finally, relational reasoning modules are designed to improve semantic segmentation of aerial images

Feasibility of Physical Properties and Soft X-ray Attenuation Properties for Non-destructive Determination of Quality of Nutmeat in In-shell Pecans

Biosystems Engineerin

Integrating passive ubiquitous surfaces into human-computer interaction

Mobile technologies enable people to interact with computers ubiquitously. This dissertation investigates how ordinary, ubiquitous surfaces can be integrated into human-computer interaction to extend the interaction space beyond the edge of the display. It turns out that acoustic and tactile features generated during an interaction can be combined to identify input events, the user, and the surface. In addition, it is shown that a heterogeneous distribution of different surfaces is particularly suitable for realizing versatile interaction modalities. However, privacy concerns must be considered when selecting sensors, and context can be crucial in determining whether and what interaction to perform.Mobile Technologien ermöglichen den Menschen eine allgegenwärtige Interaktion mit Computern. Diese Dissertation untersucht, wie gewöhnliche, allgegenwärtige Oberflächen in die Mensch-Computer-Interaktion integriert werden können, um den Interaktionsraum über den Rand des Displays hinaus zu erweitern. Es stellt sich heraus, dass akustische und taktile Merkmale, die während einer Interaktion erzeugt werden, kombiniert werden können, um Eingabeereignisse, den Benutzer und die Oberfläche zu identifizieren. Darüber hinaus wird gezeigt, dass eine heterogene Verteilung verschiedener Oberflächen besonders geeignet ist, um vielfältige Interaktionsmodalitäten zu realisieren. Bei der Auswahl der Sensoren müssen jedoch Datenschutzaspekte berücksichtigt werden, und der Kontext kann entscheidend dafür sein, ob und welche Interaktion durchgeführt werden soll