Advanced automated recognition: a true integration of ai and nature for multifaceted image analysis

Abstract

Image classification using deep neural networks is a powerful technique in the field of computer vision, enabling machines to understand and categorize visual data with high accuracy. Deep neural networks, particularly convolutional neural networks (CNNs), have revolutionized image classification tasks due to their ability to automatically learn hierarchical features from raw pixel data. This thesis mainly focuses on automated categorization of diseased and healthy leaf samples, distinct foliage species, estimation of fruits and vegetables freshness as well as automated shoreline detection and curation of new databases for machine printed and handwritten documents classification in Bengali language. The export of fresh fruits plays a pivotal role in both national and international economies, providing a significant source of income for countless farmers. Ensuring the freshness and edibility of these fruits is crucial for maintaining quality during export and minimizing financial losses. Recent advances in deep neural networks have led to their successful application in non-invasive image classification tasks, including the quality assessment of various plant parts such as leaves, fruits, and vegetables. These methods are not only more time-efficient but also cost-effective. However, a major challenge faced by researchers in developing these automated systems is the scarcity of appropriate databases necessary for training. Addressing this gap, I have developed a new database called “FruityHub”, which includes 1786 image frames of eight different fruit types: Dragon Fruits, Net Melons, Strawberries, Star Fruit, Zucchini, Mango, Pineapple, and Plum. Each fruit category in FruityHub is classified into three conditions: Fresh, Damaged, and Severely Damaged, aiding in the automated assessment of fruit quality based on characteristics like skin colour, texture, and shape. I have also evaluated the effectiveness of this database using fourteen deep neural network models, including AlexNet, Vgg16, ResNet152, MobileNetv2, ShuffleNetv2, SqueezeNet, InceptionV3, InceptionResNetv2, DenseNet121, NasNetLarge, NasNetMobile, GhostNetv2, EfficientNetB7, and Xception. The performance of these models was assessed using metrics such as Test Accuracy, Recall, Precision, and F1 Score. The burgeoning field of automated leaf classification has made significant strides with the advent of advanced deep neural technologies. My latest research introduces "GreenMedNet," a groundbreaking and computationally efficient deep neural network tailored specifically for the classification of insulin leaves into diseased and healthy categories, given their intrinsic hyperglycemic properties. This study marks a pivotal advancement with the development of the "GreenMedNet" architecture, which ingeniously captures multiscale contextual information while maintaining a streamlined parameter count. A cornerstone of my innovation lies in the creation of a novel activation function that merges the advantages of existing functions in a scalable manner, effectively safeguarding the network against common pitfalls like vanishing gradients and the dying ReLU issue. This enhancement not only preserves the non-linear dynamics essential for deep learning but also optimizes the selection of hyperparameters to boost the overall efficiency of the network. Supporting my research is the meticulously curated "Nature’s Diabetes Helpers: Insulin Leaves Repository." This custom database comprises 600 meticulously balanced images, split evenly between healthy and damaged classes, providing a robust foundation for training, and testing my model. "GreenMedNet" has demonstrated remarkable effectiveness, achieving a Test Accuracy of 99.17%, complemented by high scores in Precision, Recall, and F1 metrics. This impressive performance not only confirms the network's robustness but also highlights its superiority over existing models, establishing "GreenMedNet" as a significant contribution to the field of leaf classification technology. In the dynamic field of vegetable quality assessment, innovative automated technologies have revolutionized the way I evaluate freshness. These systems, which analyze external morphology, texture, and colour, stand out as both efficient and economical solutions. They play an instrumental role in ensuring that only the finest vegetables reach both local and international markets, thereby substantially enhancing the revenue streams of the global food industry. The significance of this technological advancement has not gone unnoticed in the research community. Enthusiastic about its potential, scientists have been keen to harness advanced deep learning technologies to accurately categorize vegetables into distinct quality classes. However, the success of these cutting-edge methods largely depends on the availability and quality of training and validation databases. This presents a notable challenge, as there is a pressing need for more comprehensive and representative datasets to refine and validate these automated systems effectively. Optical Character Recognition (OCR) plays a critical role in the digitization of documents, making textual content searchable and editable, thus streamlining workflows, and extracting actionable insights. It not only enhances accessibility for those with visual impairments but also underpins e-government initiatives and optimizes document management systems. OCR technology significantly enriches customer interaction platforms, assists in adhering to financial and legal regulations, and synchronizes seamlessly with advanced technologies such as Computer Vision, Natural Language Processing, and Machine Learning. As a cornerstone technology, OCR transforms analog documents into valuable digital formats, promoting greater operational efficiency, accuracy, and accessibility across various industries in today's digital landscape. In the realm of OCR applications, the distinction between handwritten and machine-printed texts is critical, necessitating tailored processing techniques for each. Handwritten texts require sophisticated algorithms, often based on neural networks with Long Short-Term Memory (LSTM) layers, to interpret diverse and complex writing styles. Conversely, machine-printed texts can be processed using relatively straightforward algorithms such as template matching due to their uniformity. Adaptive pre-processing strategies are essential, with techniques for handwritten texts focusing on normalizing writing styles and managing cursive scripts, while machine-printed texts may involve binarization and noise reduction. For feature extraction, handwritten OCR systems analyze characteristics like loops and slant angles, whereas machine-printed OCR systems concentrate on geometric properties. The selection of training data also varies significantly; handwritten text recognition systems benefit from datasets rich in writing style variability, whereas machine-printed systems utilize more standardized font collections. By employing document-type specific processing and adaptive learning techniques, OCR systems can significantly enhance both accuracy and processing efficiency, thereby optimizing the overall performance of OCR technologies. Automated prediction and assessment of coastline changes using advanced technologies like deep learning networks and machine learning methods are gaining significant attention for their reliability and efficiency. This research area is crucial in today's context, playing a vital role in sustainable coastal development, environmental conservation, disaster risk mitigation, and decision-making impacting both human communities and coastal ecosystems. While automated shoreline change prediction has been proposed by some, it remains relatively underexplored. My study aims to fill this gap by conducting a comprehensive analysis of existing methods, covering various aspects such as data sources and methodological approaches. By delving deeply into this subject, My work seeks to provide valuable insights to researchers and offer solutions to unexplored territories within this domain. The works presented in this thesis offer several noteworthy contributions, including: FruityHub: A diverse collection of fruits created for edibility estimation. GreenMedNet: A computationally inexpensive hyperparameter optimised for efficient classification of healthy and damaged insulin leaves. Natural vegetable progression evaluation through new developed database (VDBB). Nature’s Best vs. Bruised: A Veggie Evaluation BengaliPrintDB: A Repository of Machine-Printed Bengali Documents In-depth survey of automated shoreline alteration identification methods This thesis addresses three critical problems selected for their profound real-world significance. Through detailed analysis of statistical data from reputable sources, the practical importance of each topic is thoroughly explored. Additionally, an extensive literature review is provided for each problem, highlighting deficiencies in existing approaches and proposing novel methods to address these shortcomings.