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All Journal International Journal of Electrical and Computer Engineering Journal of Information Systems and Informatics International Journal on Food, Agriculture and Natural Resources (IJFANRES)
Kgopa, Alfred Thaga
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Agriculture, Biological Sciences & Forestry Computer Science & IT Electrical & Electronics Engineering

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Robotic Process Automation Readiness Barriers and Enablers in South Africa’s Energy Supply Chain Motsoeneng, Mariah Thokozile; Segooa, Mmatshuene Anna; Motjolopane, Ignitia; Kgopa, Alfred Thaga
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1281

Abstract

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.
Trends of unmanned aerial vehicles in smart farming: a bibliometric analysis Kgopa, Alfred Thaga; Manyela, Sikhosonke; Monchusi, Bessie Baakanyang
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5746-5758

Abstract

This paper presents a review of the trends of unmanned aerial vehicles (UAV) in agriculture using a bibliometric analysis. This bibliometric analysis shows that 1676 articles were accessed from the Elsevier Scopus database between 2013 and 2023. Our findings indicate research related to UAVs in agriculture has surged over the years, but the adoption and acceptance of smart farming technology in sub-Saharan Africa remains inert. This study employed VosViewer in data analysis and bibliometrics. Our findings show that China leads all countries and followed by the United States on UAV publications in smart farming research foci. Our findings indicate that UAVs are impactful in improving crop growth, crop health monitoring, and may be beneficial to small-holder farmers with increased yields. We recommend that sub-Saharan Africa nations accelerate collaboration with China and United States in advancing climate smart agriculture practices to mitigate food insecurity risks.
Machine Learning Algorithms for Integrating IoT Sensor into a Smart Irrigation system Kgopa, Alfred Thaga; Monchusi, Baakanyang Bessie
International Journal on Food, Agriculture and Natural Resources Vol 6, No 4 (2025): IJ-FANRES
Publisher : Food, Agriculture and Natural Resources - NETWORKS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46676/ij-fanres.v6i4.511

Abstract

Water management is a critical challenge in agriculture, particularly for small-scale farms that face resource limitations and unpredictable environmental conditions. Smart irrigation technologies that integrate the Internet of Things (IoT) and machine learning offer significant solutions in enhancing water efficiency and boosting crop production. This study investigates the synergistic application of IoT-enabled sensors alongside machine learning methodologies, specifically Decision Trees (DT) and Support Vector Machines (SVM), to augment irrigation effectiveness. Real-time sensor data collection, featuring elements like soil moisture, temperature, and humidity, serves to direct irrigation techniques. The proposed utilizes solution supervised learning techniques to establish optimal irrigation timetable and reinforcement learning to modify decisions based on real-world performance. Preliminary findings suggest that SVM outperforms DT in reducing false positives and negatives, leading to more precise irrigation control. The study underlines the benefits of AI-driven irrigation system, such as enhanced water conservation, higher crop yields, and increased sustainability. Furthermore, the difficulties of establishing IoT-based irrigation systems, such as data security, connectivity constraints, and cost considerations, are addressed. The findings add to the literature of precision agriculture and provide useful insights for small-scale farmers who are willing to implement smart irrigation solutions. The study's goal is to enhance efficient water use, strengthen food security, and support sustainable farming methods by combining IoT and AI. To get the most out of AI-powered irrigation systems, future research should focus on enhancing algorithm accuracy, expanding real-world trials, and tackling scalability challenges.
Machine Learning and Deep Learning for Plant Disease Detection: A Review of Techniques and Trends Kgopa, Alfred Thaga; sibiya, Malusi; Sumbwanyambe, Mbuyu; Monchusi, Baakanyang Bessie
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1300

Abstract

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.
Co-Authors Manyela, Sikhosonke Monchusi, Baakanyang Bessie Monchusi, Bessie Baakanyang Motjolopane, Ignitia Motsoeneng, Mariah Thokozile Segooa, Mmatshuene Anna sibiya, Malusi Sumbwanyambe, Mbuyu