Water management is a major issue in agriculture, particularly for small-scale farms that frequently confront resource limits and changing environmental circumstances. Implementing a smart irrigation system using IoT devices has various advantages but also presents some problems. The purpose of the study was to create a smart irrigation system that uses IoT sensors and machine learning algorithms to optimize water usage in small-scale farms, decreasing waste while increasing crop output. The suggested system uses IoT sensors to monitor soil moisture and meteorological conditions, with data processed in real time via edge computing. Machine learning methods, notably Decision Trees and Support Vector Machines (SVMs), are trained to anticipate optimal irrigation schedules using gathered data. The system design incorporates connection infrastructure that enables seamless data transmission and real-time decision-making. Preliminary field studies on a small-scale tomato farm showed a 25% boost in crop output and a 35% reduction in water usage while using the smart irrigation system. The results illustrate the efficacy of merging the Internet of Things and machine learning. The smart irrigation system developed in this study efficiently optimizes water utilization in agriculture, making it a feasible alternative for small-scale farmers confronting water constraint. This study advances the field of precision agriculture by proving the practical use of IoT and machine learning in smart irrigation systems as a main contribution.

Background: South Africa’s agricultural issues include water scarcity, unpredictable climate conditions and the degradation of soil. Considering the crucial importance of food and water as commodities, farmers must carefully conserve water while maintaining food security. Objectives: This study aims to determine the elements that influence farmers’ readiness to use smart irrigation technology and to evaluate their perception regarding their adoption. Method: Data were gathered through semi-structured interviews with 53 South African small-scale farmers from Gauteng and North-West provinces and analysed using interpretive approaches. Results: The findings reveal a cautious, but hopeful assessment of smart irrigation technologies, with significant perceived benefits such as water conservation, increased agricultural production and cost savings. Challenges such as high start-up costs, technological awareness, maintenance concerns and Internet connectivity prevent the adoption. Conclusion: Financial subsidies, extensive training programmes, farming community engagement projects and collaborations with technology suppliers are all suggested as ways to encourage the adoption of smart irrigation technologies. Contribution: The study adds to the literature on technology adoption by increasing understanding of the elements that influence farmers’ readiness for adopting smart irrigation technologies and by highlighting socio-economic and technological challenges.

Background: Organisational readiness for cloud-computing technology installation is crucial for improving healthcare service delivery. However, it is mostly unexplored in Limpopo provincial hospitals. Objectives: This study fills this gap by creating a framework for assessing organisational readiness that is uniquely adapted to the healthcare sector in Limpopo province, South Africa. The study incorporates the Human, Organisation, and Technology-Fit (HOT-fit) model and the Information Systems (IS) success model to emphasise the alignment of human, organisational and technological elements in assessing the organisational readiness of cloud-computing implementations. Method: The study employed a quantitative research approach, collecting data from healthcare professionals and information technology (IT) administrators using survey questionnaires. Statistical analyses, such as regression analysis and correlation tests, are used to validate the suggested framework and determine the correlations between key readiness criteria and cloud-computing implementation. Results: The findings show that organisational structure, system utilisation, environmental conditions and service quality all have a substantial impact on cloud-computing readiness. Conclusion: By addressing these readiness elements, Limpopo provincial hospitals can strategically use cloud-computing to improve healthcare service delivery and operational efficiency in resource-constrained environments. Contribution: The study fills the gap in the existing literature by presenting new factors validated as a theoretical framework to assess the readiness for cloud-computing and contributes to the advancement of this field. Keywords: cloud-computing; cloud-computing readiness; cloud-computing framework; Limpopo hospitals; South African public hospitals; healthcare professionals.

Background: A systematic review of the literature uncovered little explanation as to why academics as individuals switch mobile applications or continue to use the same mobile applications. Knowing and being able to explain the personal behaviour are important and could be of value to practitioners and researchers, especially when it comes to predicting the future behaviour. Objectives: The main objective of this study was to explain and to model switching and continued use of mobile computing applications by South African academics. Method: A survey was carried out within South African academics as participants to test 14 hypotheses and explain switching and continued use of mobile computing applications. The adopted positivist study was associated with the quantitative research approach to test hypotheses so that the truth about objectives are obtainable. Results: The results of the study contributed to the body of knowledge by explaining and modelling the determinants influencing and significant to mobile computing application switching and continued use behaviour. Conclusion: The developed model may help to inform practitioners and researchers on what actually satisfies users to switch or continue to use mobile computing applications.

Plant diseases pose a significant threat to global agricultural productivity, making early and accurate detection critical for yield protection and food security. This study evaluates the evolution, effectiveness, and practical applicability of Machine Learning (ML) and Deep Learning (DL) models for plant disease detection while analyzing research trends to identify leading models, data limitations, and implementation challenges. A systematic literature review and bibliometric analysis were conducted using the PRISMA framework, examining 625 peer-reviewed articles published between 2017 and 2025 from major databases. The analysis highlights the most influential studies, commonly used datasets, and top-performing ML/DL models, assessed in terms of accuracy, methodology, dataset type, and real-time deployment potential. Results show that models such as YOLOv4, VGG19, ResNet50, and MobileNetV2 achieved accuracy levels between 98% and 99.99%, with most trained on the PlantVillage dataset or custom annotated datasets. Several studies demonstrated successful real-time deployment via mobile and edge-device applications. However, key challenges remain, including limited dataset diversity, poor model generalization across environments, and reduced performance under real-field conditions. This study provides a comprehensive overview of progress in AI-based plant disease detection, emphasizing the need for lightweight, adaptable, and field-ready models to support scalable real-world deployment.

South Africa’s energy industry faces ongoing challenges including power shortages, ageing infrastructure, and supply chain inefficiencies, while, limited empirical evidence exists on how organisations in this industry prepare for Robotics Process Automation (RPA) adoption. This study examines the RPA readiness barriers and enablers within the supply chain of South Africa’s energy industry. The research adopts a qualitative design grounded in the Technology-Organisation-Environment (TOE) framework and the Awareness, Desire, Knowledge, Ability, Reinforcement (ADKAR) change management model to connect technological capability with individual and organisational readiness for change. Data were gathered through semi-structured interviews with 18 professionals representing eight stakeholder groups, including supply chain managers, IT specialists, process improvement leads, and employees affected by automation. Four key readiness barriers emerged: readiness gaps (61 mentions), organisational misalignment (158), infrastructure strain (83), and job security and resistance (60). Corresponding enablers included leadership accountability, RPA governance and alignment frameworks, readiness checklists, structured communication protocols, KPI frameworks, capability audits, investment planning, psychological safety, and regulatory alignment mechanisms. The integration of TOE and ADKAR offers a novel dual-lens perspective that extends existing knowledge. The findings provide practical guidance for managers and policymakers seeking to strengthen organisational systems and structures with human readiness factors in emerging economies.