DEPARTMENT OF COMPUTER ENGINEERING

The increasing demand for efficiency and transparency in Nigeria’s livestock sector has highlighted the need for digital solutions that connect farmers, buyers, and agricultural experts in real time. This project, titled “Design and Development of a Web-Based Livestock Management and Sales Platform” focuses on creating a functional and user-friendly online platform that digitises livestock management, marketing, and consultancy services. The system was developed using a combination of HTML, CSS, and JavaScript for the frontend design, while Django (Python) served as the backend framework and PostgreSQL as the database management system. The platform allows farmers (administrators) to list available livestock, manage bookings, update records, and provide consultancy advice, while buyers (users) can browse livestock listings, add animals to cart, view detailed information, and request consultations. Additional modules, such as a blog for farming guides and a testimonial section, were integrated to enhance engagement and credibility. An Agile development methodology was adopted to ensure iterative design, testing, and improvement throughout the development cycle. The system was deployed on the Render cloud hosting platform, providing online accessibility and scalability for real-world application. Testing results confirmed that the system effectively supports livestock transactions, simplifies communication between stakeholders, and provides a reliable database for record management. Overall, the project demonstrates how information and communication technology (ICT) can enhance market access, transparency, and knowledge sharing in Nigeria’s livestock industry. It contributes to digital agriculture research and provides a scalable model for future integration of payment gateways, analytics, and mobile platforms. Keywords: Web-based system, Livestock management, Digital agriculture, Django, ICT, E-commerce, Render hosting.

This project aims to design and implement a hybrid clean and renewable energy system for telecommunication base stations, integrating wind and solar energy sources. The primary purpose is to enhance the sustainability, reliability, and efficiency of off-grid power systems, particularly in remote locations where traditional energy sources are costly and environmentally unsustainable. By leveraging the complementary nature of wind and solar resources, the project seeks to reduce dependence on fossil fuels, minimise carbon emissions, and improve the energy autonomy of telecommunication infrastructure. The ultimate goal is to create a resilient, ecofriendly energy framework that contributes to global efforts in combating climate change. The methodology involved an extensive research and development process. Initially, a detailed literature review was conducted to gather insights from existing studies and identify areas for improvement. The design phase focused on developing a dual-input charge controller system capable of managing power from both solar panels and wind turbines. The system architecture incorporated essential components such as voltage and current sensors, metal￾oxidesemiconductor field-effect transistor (MOSFET) drivers, and a battery storage unit. A prototype was simulated using Proteus Computer-Aided Design (CAD) software, followed by the construction of the physical model. The wind turbine was crafted from a modified fan motor, while the battery pack consisted of lithium-ion cells configured for optimal capacity. System testing was conducted under varying environmental conditions to evaluate performance and reliability. The results demonstrated that, while the solar system consistently generated higher energy outputs, the wind turbine provided supplementary power, particularly during periods of low sunlight. The hybrid system showed potential in maintaining stable power generation throughout different times and seasons. However, challenges in wind turbine fabrication affected overall efficiency. The study concludes that integrating wind and solar technologies enhances the resilience and sustainability of telecommunication base stations. Recommendations for future work include improving wind turbine fabrication, expanding testing across diverse climates, and exploring additional renewable energy sources to further bolster system autonomy and efficiency.

The efficiency and reliability of photovoltaic (PV) systems are largely determined by their ability to extract maximum power under varying environmental conditions. This project presents the implementation of an IoT-based investigative system for Maximum Power Point Tracking (MPPT) in photovoltaic arrays, focusing on the comparative performance of the MPPT and Pulse Width Modulation (PWM) charge controllers. The system integrates voltage and current sensors with an ESP32 microcontroller to measure and record PV parameters in real time. Through IoT connectivity, the collected data is transmitted to a cloud-based platform for remote monitoring, analysis, and visualization, enabling real-time tracking of PV performance. Experimental tests were conducted under different irradiance and temperature levels to evaluate the charging efficiency, dynamic response, and adaptability of both controllers. The MPPT controller dynamically adjusted the operating point of the PV module to maximize energy extraction, while the PWM controller maintained a simpler, fixed switching mechanism. Additionally, the system allowed for a detailed analysis of the relationship between light intensity, temperature, and PV output performance, with the readings interpreted from real-time graphical charts. These insights revealed how environmental variations affect energy generation and charge controller efficiency. This project develops a real-time, IoT-enabled system capable of monitoring and comparing the operational efficiency of MPPT and PWM charge controllers in photovoltaic applications. The results demonstrate that the MPPT controller achieves superior power utilization and battery charging efficiency compared to the PWM controller. Overall, the system provides a reliable, data-driven investigative platform for analyzing solar charge control strategies and
supports further optimization of PV energy systems through intelligent IoT integration.

State of Charge (SoC) estimation plays a crucial role in battery management systems (BMS), directly impacting the performance, safety, and longevity of lithium-ion batteries. This study presents a comparative review of three major categories of SoC estimation techniques: model-based, data-driven, and hybrid methods. The review is driven by the need to evaluate the accuracy, robustness, and practical applicability of these methods across various real-world conditions, including different temperature profiles, battery chemistries, and aging states. The research methodology involved a structured literature search, selection of 45 peerreviewed studies published between 2018 and 2025, and systematic data extraction. Model-based approaches, particularly those using Kalman filters and equivalent circuit models, demonstrated computational efficiency but showed sensitivity to parameter drift and aging. Data-driven techniques, including LSTM networks, Gaussian Process Regression (GPR), and Random Forests, offered high accuracy—often achieving <2% RMSE—but required large, diverse datasets. Hybrid methods, such as AEKF-LSTM and UKF-PSO-LSTM models, consistently achieved the highest accuracy (RMSE <1%) while balancing robustness and adaptability. The findings suggest that while model-based methods are suitable for resourceconstrained systems, hybrid approaches offer the most promising results in terms of overall performance and reliability. These insights can guide future BMS development
and inform system-level design choices in electric vehicle and energy storage applications.

This project focuses on the design and implementation of a controlled environment vertical farming system for tomato production in Benin City. The system integrates climate control, automated irrigation, and hydroponic nutrient delivery to optimize plant growth and resource efficiency. Key components include temperature and humidity sensors, an automated irrigation system, and a microcontroller-based control unit for real-time monitoring and adjustments. The vertical farming setup was designed to maximize space utilization while reducing water consumption and dependency on
chemical fertilizers. The implementation process involved system calibration, sensor integration, and performance evaluation to assess its impact on crop yield and sustainability. Results indicate that the controlled environment significantly enhanced tomato growth, minimized pest infestations, and improved overall yield compared to conventional soil- based farming methods. However, challenges such as high initial investment costs and power dependency were noted, necessitating the integration of renewable energy sources for long-term viability. This study demonstrates the potential of vertical farming as a sustainable and scalable solution for urban agriculture, addressing food security concerns while promoting resource-efficient farming practices. The findings suggest that further research into automation, AI-driven climate control, and localized material sourcing could enhance system performance and accessibility for wider adoption..

Although centralized institutions, like governments, can use blockchain technology to increase the safety and security of sensitive data, this technology also permits the emergence of decentralized business models. The proposal to create a car registration system in Nigeria that can enhance interoperability between governmental agencies and might be expanded to a cross-borders system is described in this project work and is based on the Ethereum blockchain network. The suggested system takes care of all car registration-related procedures, including changing a vehicle's ownership status and registering it. As the car registration information is supplied to each government agency in a single decentralized system, this approach can facilitate information interchange among several states

Precision agriculture has emerged as a vital approach for improving agricultural efficiency and sustainability by leveraging advanced technologies. This project focuses on developing a machine learning model that utilizes historical data for precision agriculture applications. The system aims to assist farmers in making data-driven decisions by predicting suitable crops, forecasting crop yields, and detecting potential crop diseases. The proposed solution integrates wireless sensor networks to collect environmental parameters such as rainfall, temperature, humidity, Potassium, Nitrogen and soil pH, which are combined with historical agricultural data. Machine learning models, including Voting Classifier (Support Vector Machines, Gaussian Naïve Bayes, Random Forest) and Linear regression model, are trained and deployed to analyze the data for actionable insights. The Voting classifier model achieved a 99.3% accuracy in predicting suitable crops, while the Linear regression model provided yield forecasts with an R² score of 0.91. The Convolutional Neural Network (CNN) detected crop diseases with an accuracy of 93.8%. The system’s implementation demonstrates the effectiveness of combining historical data with machine learning techniques to enhance precision agriculture practices. By providing accurate and timely information, this solution helps optimize crop selection, improve resource allocation, and mitigate the risk of crop diseases. Recommendations for integration with mobile applications, weather data integration and farmer education and training are proposed to further enhance the system’s usability and impact. This project offers a promising step toward sustainable and smart farming practices, contributing to food security and agricultural productivity.

The growing global demand for renewable energy has driven significant advancements in solar energy technology, particularly in photovoltaic (PV) systems and inverters, which convert solargenerated DC into usable AC. Despite progress, traditional inverters face challenges such as inefficiency, high harmonic distortion, and limited adaptability to dynamic environmental conditions.This project aims to design a microcontroller-based solar inverter that integrates
advanced control algorithms like Maximum Power Point Tracking (MPPT) and Pulse-Width Modulation (PWM) to enhance efficiency, reliability, and adaptability. By leveraging modern microcontroller technology, the project seeks to improve energy conversion, reduce costs, and address the limitations of conventional designs, contributing to the broader adoption of solar energy systems. The process begins with modeling the photovoltaic (PV) array using Simulink’s Simscape Electrical library, incorporating real-world parameters such as irradiance and temperature to simulate I-V and P-V curves. The MPPT algorithm, specifically the Perturb and Observe (P&O) method, is implemented to optimize power extraction under varying conditions. PWM is generated using a PID controller to regulate the DC-DC boost converter, which steps up the PV voltage. An H-Bridge inverter, controlled by Sinusoidal PWM (SPWM), converts the boosted DC into a clean AC waveform. The complete system integrates the PV array, MPPT, boost converter, and inverter, with simulations conducted to validate performance under diverse environmental and load conditions. This project successfully designed and simulated a microcontroller-based solar inverter system. The PV array, modeled under varying irradiance and temperature conditions, consistently generated around 5300W, operating near its maximum power point. The boost converter efficiently stepped up the PV voltage to 275.1V with over 90% efficiency, while the H-bridge inverter produced a clean 220V AC output with minimal harmonic distortion. System integration demonstrated robust performance under diverse environmental and load conditions, achieving an overall efficiency exceeding 90%.

The increasing adoption of smart home technologies and the demand for robust home security systems have driven the need for innovative solutions that integrate various components seamlessly. This project presents a comprehensive smart home security solution that integrates IoT technology with remote camera control capabilities, leveraging the cost-effective ESP32-
CAM microcontroller. The system comprises two key components: a responsive web application and a Telegram bot interface, designed to provide homeowners with real-time surveillance and control functionality. The ESP32-CAM module, equipped with Wi-Fi connectivity and an integrated camera sensor, is the core hardware, capturing high-quality images and video streams
that can be accessed remotely through either interface. The web application offers an intuitive dashboard for monitoring live video feeds, viewing captured images, controlling camera parameters, and receiving motion-triggered alerts. Complementing this, the Telegram bot provides similar functionality through a conversational interface, allowing users to request images, view live streams, and receive instant notifications on their mobile devices. This dual-interface approach ensures accessibility across various devices and user preferences. The implementation utilizes a decentralized architecture where the ESP32-CAM operates as both a camera and a lightweight server, communicating directly with client applications without relying on cloud infrastructure. This edge computing approach prioritizes data privacy and reduces dependency on external services. The Telegram bot integration leverages the Telegram API for secure notifications while maintaining the local processing paradigm. Experimental
results demonstrate the system's effectiveness in providing reliable surveillance with minimal latency while maintaining reasonable power consumption for extended operation. This solution offers an affordable yet robust alternative to commercial smart security systems, making home security technology more accessible to a broader range of users

This project focuses on developing an innovative web-based monitoring system tailored for deep cycle batteries. Serving as a repository of vital information, the system seamlessly amalgamates data from battery-connected sensors, securely storing it within a cloud-based database. Accessible via an intuitively designed web interface, users can effortlessly access essential battery insights, without the need for real-time updates. The system's ingenuity lies in its capacity to translate raw data into actionable insights. Extracted patterns and correlations inform the optimization of battery performance and the extension of its lifespan. The system's intelligence empowers informed decision-making, offering suggestions for adjustments to charging rates, discharge patterns, and operational strategies. These recommendations hold the potential to substantially enhance deep cycle battery longevity, mitigate maintenance costs, and elevate overall system efficiency. Furthermore, the system acts as a trusted guide in selecting deep cycle batteries tailored to specific needs. Conducting meticulous comparative analyses of battery performances and considering pivotal selection factors empowers users to make confident, well-informed decisions, even in the absence of visual aids Spanning applications across the renewable energy, marine, and automotive sectors, this allencompassing monitoring system revolutionizes deep cycle battery management. By prioritizing pertinent data and actionable insights over real-time updates, the system lays the groundwork for efficient, cost-effective, and well-informed battery systems, thus contributing to a sustainable energy landscape