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All Journal Journal of Information Systems and Informatics International Journal on Food, Agriculture and Natural Resources (IJFANRES)
Monchusi, Baakanyang Bessie
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Agriculture, Biological Sciences & Forestry Computer Science & IT

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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 Kgopa, Alfred Thaga sibiya, Malusi Sumbwanyambe, Mbuyu