http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/23 MCE Thu, 19 Feb 2026 22:41:11 GMT 2026-02-19T22:41:11Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/19003 Title: Data logging Model for Metropolitan Vehicle Movement Monitoring and Control System Authors: Adeniyi, Samuel; Olamide, Ojekunle Pa; Jack, Kufre; Salami, Taye Hassan Abstract: Abstract— The automotive industry has experienced a spectacular expansion in the last decade, with an increase in number of cars in metropolitan cities. Maintaining track of automobiles based on their plates number in order to manage vehicular traffic properly in the city has posed difficulty. This research presents an artificial intelligence-based technology technique (YOLO) for tracking vehicle movement based on the vehicle's plate number with data logging model and a centralized database structure for vehicles identification and monitoring, using other techniques such as image processing and IoT mechanism for detection and recognition accuracy. In order to create an Intelligent Plate Number Recognition (IPNR) System, this study employs artificial intelligence, computer vision (image processing), laser scanning technologies, and convolutional neural networks (CNN). This model concepts and computations underpin potential solutions to this issue, guaranteeing a range of approaches to achieving the desired outcome. This work focuses on plate number identification using the contours tracing approach, as well as edge identification and sharpening using optical character recognition algorithm based on OpenCV libraries. The vehicle is monitored on real time using the GPS technology where the vehicle plate image was captured with the Pi camera to produce high-quality images. This research employs a wide range of techniques across the board (from license plate detection to character recognition) to boost the system's speed as much as feasible with negligible overhead. The studies demonstrated how useful image processing tools were used for data logging, and character recognition when combined with vehicles in urban environments. To carry out monitoring and management of the system, a responsive web application was designed for data logging. Description: Nil Wed, 05 Apr 2023 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/19003 2023-04-05T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/18935 Title: Development of an Internet of Things Enabled Vehicle Movement Management System for Federal University of Technology Minna Niger State Authors: SALAMI, Taye Hassan Abstract: In recent years, the number of cars entering Federal University of Technology Minna has been on the increase. Vehicle Security can be managed using time-in and time-out metrics as well as license plate numbers. This research proposes a way for tracking vehicle movement based on the vehicle's license plate number with a data logging model that serves as a centralized database structure for vehicle identification, employing image processing and IoT for detection and recognition accuracy. This project aims to develop an internet of things enabled vehicle movement management system for federal university of technology Minna using AI, computer vision (image processing), laser scanning, and CNN. This challenge requires mathematical ideas and algorithms to complete the product's steps. High-quality cameras capture the image. This work focuses on plate number localization using contours tracing and optical character recognition using OpenCV modules. A vehicle equipped with a high-precision GPS and GSM module was used to test the suggested video processing technique. This study combines a variety of methods, from plate number detection to character recognition, to increase system performance with minimal effort and processing resources. These trials show the adaptability of Federal University of Technology Minna's vehicle, along with video and image processing for data logging and decision making. Also demonstrated were training data set results and video frame recognition persistence. A graphical user interface was created for data logging to enable model training continuation and system monitoring and control. It is strongly suggested that vehicle investigations benefit from the use of a central, publicly accessible database for license plate number lookup. If police had access to this database, they would have a streamlined method of locating wanted individuals by license plate number. Not only would such a system improve public safety, but it would also be a huge help to law enforcement in their day-to-day activities. Description: None Wed, 01 Mar 2023 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/18935 2023-03-01T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/18793 Title: An IoT-Based Autonomous Robot System for Maize Precision Agriculture Operations in Sub-Saharan Africa Authors: Bala, Jibril Abdullahi; Olaniyi, Olayemi Mikail; Folorunso, Taliha Abiodun; Daniya, Emmanuel Abstract: The importance of agriculture to the economic growth in sub-Saharan Africa suffers from several challenges. One major problem faced by the sector is the lack of suitable technology to optimise yield and profit to reduce the reliance of farmers on manual techniques of farming which is accompanied by drudgery, wastage, and low yields. Precision Agriculture has been applied to maximise agricultural outputs while minimising inputs. This study presents the design of an Internet of Things (IoT) based autonomous robot system that can be used for precision agricultural operations in maize crop production. The robot consists of a camera for remotely monitoring of the environment and a tank incorporated with a liquid level sensor which can be used for irrigation and herbicide application. The real-time feed from the camera as well as the output from the liquid level sensor is accessed from a cloud database via a web application. This system can be adopted for improved crop production which in turn will increase crop yield, profit, and revenue generated from agriculture. Fri, 01 Jan 2021 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/18793 2021-01-01T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/18792 Title: Weed Recognition System for Low-Land Rice Precision Farming Using Deep Learning Approach Authors: Olaniyi, Olayemi Mikail; Daniya, Emmanuel; Abdullahi, Ibrahim M.; Bala, Jibril Abdullahi; Olanrewaju, Ayobami Esther Abstract: Precision farming helps to achieve maintainable agriculture, with an objective of boosting agricultural products with minimal negative impact on the environment. This paper outlines a deep learning approach based on Single Shot multibox Detector (SSD) to classify and locate weeds in low-land rice precision farming. This approach is designed for post-emergence application of herbicide for weed control in lowland rice fields. The SSD uses VGG-16 deep learning-based network architecture to extract a feature map. The adoption of multiscale features and convolution filter enables the algorithm to have a considerable high accuracyeven at varying resolutions. Using SSD to train the weed recognition model, an entire system accuracy of 86% was recorded. The algorithm also has a system sensitivity of 93% and a precision value of 84%. The trained SSD model had an accuracy of 99% for close-up high definition images. The results of the system performance evaluation showed that the trained model could be adopted on a real rice farm to help reduce herbicide wastage and improve rice production with low chemical usage. Fri, 01 Jan 2021 00:00:00 GMT http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/18792 2021-01-01T00:00:00Z