Projects

The Story Chamber is a literary innovation platform that showcases emotionally resonant, narratively coherent fiction generated entirely by artificial intelligence. Unlike AI writing tools that assist human authors, this platform constructs deeply detailed AI author personas, each with a unique backstory, worldview, and voice, that produce unedited fiction guided by sophisticated narrative blueprints. The result is machine-generated literature that feels authored, not assembled. With an emphasis on genre diversity, artistic integrity, and scalable storytelling, The Story Chamber reimagines what authorship means in the age of generative media. It offers readers a new kind of literary experience: fiction crafted by simulated minds, designed with care, and delivered with intent.

My GitHub portfolio entry showcases a range of full-stack, AI-integrated, and automation-driven applications designed to solve real-world creative and technical challenges. For example, the 'Social Media Scheduler' is a unified platform for scheduling and publishing AI-enhanced content across Facebook, Instagram, X (Twitter), Pinterest, Tumblr, and more. Built with Python and Streamlit, it includes secure OAuth integration, content enhancement via GPT/Gemini/Claude, real-time scheduling, and future support for REST APIs and Dockerized deployment. Across all projects, I've personally architected the backend (FastAPI, SQLite/MySQL), built responsive frontends, integrated third-party APIs, and developed mobile apps for iOS and Android — with a focus on extensibility, modular design, and real-world utility.

StomaGAN represents a sophisticated deep learning framework that leverages Generative Adversarial Networks to address the critical challenge of limited training data in stomatal analysis. This pioneering research demonstrates how a modified Deep Convolutional GAN architecture, enhanced with adaptive top-k selection mechanisms, spectral normalisation techniques, and strategic noisy label integration, can generate photorealistic synthetic stomatal images from minimal datasets. The framework successfully addresses training instability issues inherent in traditional GANs while producing high-fidelity artificial data that significantly augments existing stomatal image collections. The synthetic data generated through this methodology has been rigorously validated through its successful application in training deep convolutional neural networks for automated stomatal detection, achieving remarkable accuracy rates on previously unseen real-world imagery. This open-source contribution provides the research community with comprehensive code repositories, curated datasets, and analytical tools, establishing a new paradigm for data augmentation in plant phenotyping applications.

StomataHub constitutes a revolutionary collaborative ecosystem designed to democratise access to cutting-edge stomatal analysis methodologies through the integration of artificial intelligence and open science principles. This comprehensive platform, architected and maintained by Dr. Jonathon Gibbs, serves as a centralised repository for meticulously annotated stomatal datasets, peer-reviewed research publications, and state-of-the-art machine learning applications. The platform features an advanced web-based stomatal detection system currently under active development, incorporating the latest advances in computer vision and deep learning architectures. By fostering interdisciplinary collaboration between plant biologists, computer scientists, and agricultural researchers, StomataHub accelerates the pace of discovery in plant phenotyping research. The platform's expanding collection of publicly accessible datasets and sophisticated analytical tools empowers researchers worldwide to advance our understanding of stomatal traits, ultimately contributing to enhanced crop breeding programs and improved agricultural sustainability in the face of climate change.

This groundbreaking research introduces a novel deep learning methodology that revolutionises automated stomatal detection and morphometric analysis in high-resolution microscopy imagery. The investigation presents a custom-designed convolutional neural network architecture that incorporates advanced feature extraction mechanisms, achieving unprecedented accuracy rates exceeding 98% across diverse microscopic imaging modalities and plant species. The framework enables comprehensive high-throughput quantitative analysis of critical stomatal parameters including density distributions, morphological dimensions, and complex spatial patterning characteristics. These capabilities provide invaluable insights into fundamental plant physiological processes, particularly water-use efficiency mechanisms and environmental adaptation strategies. The research contribution includes the development of an extensively annotated dataset comprising thousands of stomatal images, establishing a new benchmark for computational plant biology research. This work bridges the gap between traditional botanical analysis and modern artificial intelligence, offering researchers unprecedented tools for understanding plant responses to environmental stressors and climate variability.

This comprehensive research program advances the field of autonomous plant phenotyping through the development of sophisticated robotic vision systems that seamlessly integrate precision mechanical engineering, advanced computer vision algorithms, and machine learning methodologies. The investigation presents novel path-planning algorithms specifically optimised for agricultural applications, incorporating multi-objective optimisation strategies that maximise information acquisition while minimising mechanical disturbance to delicate plant structures. The system architecture demonstrates remarkable innovation through its hybrid approach, combining volumetric reconstruction techniques with high-resolution point cloud processing methodologies to achieve sub-millimetre accuracy in complex plant architecture modelling. This technological advancement enables unprecedented precision in phenotypic measurements, facilitating detailed analysis of growth patterns, structural biomechanics, and morphological adaptations. The research incorporates GPU-accelerated processing pipelines utilising CUDA frameworks, ensuring real-time performance suitable for high-throughput agricultural research applications. These contributions establish new standards for non-invasive plant monitoring and provide essential tools for advancing crop improvement programs and agricultural sustainability research.