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This Author published in this journals
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
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Electrical & Electronics Engineering

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Journal : Journal of Information Systems and Informatics

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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.
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