Agriculture is undergoing a digital transformation driven by Machine Learning (ML) and the Internet of Things (IoT), enabling sustainable food production, efficient resource utilization, and climate-resilient farming. Traditional agricultural practices are increasingly challenged by climate change, soil degradation, water scarcity, pest outbreaks, and the growing global demand for food. Integrating IoT sensors with ML algorithms provides real-time monitoring, predictive analytics, and intelligent decision-making for precision agriculture. This paper reviews recent developments in ML and IoT applications for sustainable agriculture, focusing on crop monitoring, smart irrigation, disease detection, yield prediction, livestock management, and resource optimization. The proposed framework integrates IoT sensing devices, wireless communication, cloud edge computing, machine learning analytics, and farmer decision support systems to improve productivity while minimizing environmental impact. The study further discusses current challenges, including cyber security, interoperability, infrastructure cost, and limited digital literacy among farmers. Recent studies demonstrate that AI enabled IoT systems significantly improve irrigation efficiency, reduce fertilizer consumption, enhance crop productivity, and support climate-smart agriculture. The paper concludes that combining ML and IoT is fundamental to achieving sustainable agricultural development and the United Nations Sustainable Development Goals (SDGs), particularly Zero Hunger (SDG 2), Clean Water (SDG 6), Responsible Consumption and Production (SDG 12), and Climate Action (SDG 13). Finally, future research directions involving edge intelligence, federated learning, block chain integration, and digital twins are highlighted.
Mustapha Malami Idina, Mubarak Jibril Yeldu, A. Gulumbe· International Journal of Mul...· 0 citations
- Rapid urbanization, increasing vehicle ownership, and the growing complexity of urban transportation networks have intensified challenges related to traffic congestion, travel delays, fuel consumption, environmental pollution, and road traffic accidents. Conventional traffic management systems, which primarily rely on fixed-time traffic signal control and manual monitoring, often fail to respond effectively to dynamic traffic conditions and accident-prone situations. The integration of the Internet of Things (IoT) with deep learning has emerged as a promising approach for developing intelligent transportation systems capable of real-time monitoring, adaptive traffic control, and proactive accident prediction. This paper proposes an Intelligent Traffic Management and Accident Prediction Framework Using IoT and Deep Learning that integrates heterogeneous IoT devices, edge computing, cloud computing, and hybrid deep learning models to improve traffic efficiency and road safety. The proposed framework employs smart cameras, Global Positioning System (GPS) devices, roadside units, Radio Frequency Identification (RFID) sensors, connected vehicles, and environmental sensors to collect real-time traffic data. Data are preprocessed at the edge to reduce latency before being transmitted to cloud platforms for large-scale storage and deep learning analysis. A hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture is adopted to capture both spatial and temporal traffic characteristics for congestion forecasting and accident risk prediction. The framework supports adaptive traffic signal control, intelligent route optimization, and early warning generation for traffic management authorities and road users. A comprehensive review of recent studies demonstrates that integrating IoT with deep learning significantly improves prediction accuracy, decision-making speed, and transportation efficiency compared with conventional approaches. The research also identifies key implementation challenges, including data heterogeneity, cyber security, privacy preservation, model explain ability, and scalability. The proposed research framework provides a scalable and intelligent solution for next-generation smart transportation systems and offers a foundation for future research on explainable artificial intelligence, federated learning, digital twins, and autonomous connected vehicles within intelligent transportation environments.
Mubarak Jibril Yeldu, Abubakar Jibo Magayaki, A. Gulumbe et al.· Iconic research and engineer...· 0 citations
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