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p-Index From 2021 - 2026
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All Journal Journal of Information Systems and Informatics International Journal of Electrical, Energy and Power System Engineering (IJEEPSE)
Sumbwanyambe, Mbuyu
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

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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.
Impact of Periodic Cycling on Lithium Battery Performance in Standby UPS Systems: A Case Study of a Vertiv 100 kW Sithole, Tshepo; Sumbwanyambe, Mbuyu
International Journal of Electrical, Energy and Power System Engineering Vol. 9 No. 1 (2026): The International Journal of Electrical, Energy and Power System Engineering (I
Publisher : Electrical Engineering Department, Faculty of Engineering, Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/ijeepse.9.1.39-47

Abstract

Lithium Iron Phosphate batteries in standby uninterruptible power supply (UPS) applications are susceptible to calendar aging when infrequently cycled, yet empirical field data on performance recovery strategies for large-scale industrial systems remain scarce. This study investigates the performance degradation of a Vertiv 100 kW three-phase UPS paired with two strings of 8 Vision Lithium batteries (100 Ah, 52.4 Vdc each), installed in 2021 at a commercial facility with a stable 45 kW load. After three years of minimal cycling, clients reported reduced backup duration, intermittent battery alarms, and DC breaker trips, attributed to calendar aging effects including elevated internal resistance and capacity fade under prolonged high state-of-charge conditions. A manual discharge/recharge protocol was implemented twice weekly using the site load, with discharge duration recorded across successive cycles. Battery performance recovered progressively, with discharge time increasing from 3 minutes to 18 minutes over 10 cycles, corresponding to a State of Health improvement from 2.68% to 16.10%. Following this recovery, the system was programmed for automatic periodic discharges. These findings demonstrate that periodic cycling effectively mitigates calendar aging in standby LFP systems and provide a practical framework for preventive battery management in low-outage industrial UPS environments.
Co-Authors Kgopa, Alfred Thaga Monchusi, Baakanyang Bessie sibiya, Malusi Sithole, Tshepo