Browsing by Author "AJiboye, Johnson Adegbenga"

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Development of Draught Early Warning System (DEWS) in Nigeria: A Review of Progress, Challenges and Future Directions
(ICEC, 2025) AJiboye, Johnson Adegbenga; Ofeoshi, C. I.; Adesiji, A. R.; Saidu, M.
Drought Early Warning Systems (DEWS) are important tools for reducing the impact of drought on agriculture, water resources, and food security. This review explores drought trends in Nigeria, assessing the progress, challenges, and future directions of DEWS development. Analysis of past drought occurrences reveals that Nigeria has experienced notable drought episodes in 1914, 1924, 1935, 1943, 1951-1954, 1972-1973, and 1991-1995, with the driest decades recorded between 1970 and 1990. The increasing trend of drought events is linked to climate change, land degradation, and poor water management. Nigeria's primary DEWS, managed by the Nigerian Meteorological Agency (NiMet), employs indices such as the Standardized Precipitation Index (SPI) and the Palmer Drought Severity Index. However, these systems face significant challenges, including data gaps, limited technological integration, and inadequate community participation. An analysis of past studies shows advancements in satellite-based vegetation health indices, climate modelling, and machine learning algorithms. However, DEWS effectiveness is hindered by institutional weaknesses, data limitations, and insufficient stakeholder engagement. Key challenges include governance, coordination, funding, and capacity building. Future research should focus on intègrating local knowledge and indigenous practices, developing more complex and integrated DEWS models, improving data quality, and enhancing communication strategies. This review aims to inform policymakers, researchers, and practitioners about the need to strengthen DEWS to support drought resilience and sustainable development in Nigeria.
IoT-based Beverage Fraud Detection: A Theoretical Review
(CUJOSTECH, 2025) AJiboye, Johnson Adegbenga; Usman, A. U.; Oyewobi, S. S.; Abdulbaki, A. O.; SAlihu, B. A.; Mamman, T. A.
This theoretical review explores the foundational theories, frameworks, and models relevant to the design of an Artificial Intelligence (Al) and Internet of Things (IoT)-based system for detecting counterfeit and expired carbonated beverages in the Nigerian market. The growing incidence of beverage fraud, including dilution, mislabeling, and expiration concealment, necessitates the adoption of advanced detection mechanisms. This study explores the use of Bayesian Linear Regression (BLR) to analyze caffeine and CO: concentrations within established theories from food safety, sensor analytics, and machine learning. By comparing and critiquing traditional and modern approaches, the review highlights the strengths of integrated lot and Machine Learning (ML) technologies for scalable, real-time quality monitoring. The key finding from the review is that integrating lot-enabled sensors, BLR, and ML within the Fake Beverages Detection Systems (FaBEDs) framework offers a scalable, real-time, and interpretable approach for detecting counterfeit and expired carbonated beverages, particularly suitable for low-resource settings like Nigeria. This work contributes a critical perspective on how theoretical models can inform practical implementations for safeguarding public health and ensuring beverage supply chain integrity in developing economies.
IoT-Based Intelligent System for Real-Time Soil Nutrient Monitoring and Decision Support in Farming: Potential for Deployment in Rice Farming in Nigeria
(CUJOSTECH, 2025-04) AJiboye, Johnson Adegbenga; Usman A. U.; Salawu, N.; Gana, A. S.; Mohammed, H. K.; Ajiboye, M. A.
This paper presents a comprehensive examination of the design, potential implementation, and existing technological landscape of Internet of Things (IoT)-based intelligent soil nutrient monitoring systems for rice farming. While the paper presents a model contextualized for Niger State, its findings offer insights relevant to rice-producing regions across Nigeria. Rice farmers face significant challenges related to soil fertility management, often due to reliance on inefficient traditional methods. By reviewing recent developments in sensor integration, embedded systems, and cloud-based platforms, this paper explores how such technologies can be applied to provide continuous, real-time monitoring of essential soil parameters like nitrogen (N), phosphorus (P), potassium (K), pH, temperature, and moisture. The findings suggest that IoT-enabled systems, complemented by mobile applications and decision-support tools, offer a low-cost and scalable solution to enhance yield, optimize fertilizer use, and improve data-driven decision-making for small and medium-scale farmers. This paper aims to provide a state-of-the-art overview of current trends and contextualize the proposed system within the global and local technological ecosystem.