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Contact Name
Dedik Budianta
Contact Email
dedik.budianta@unsri.ac.id
Phone
+628127859781
Journal Mail Official
contact@josaet.com
Editorial Address
Jl. Bukit Baru II No 44, Palembang 30131, Indonesia
Location
Kota palembang,
Sumatera selatan
INDONESIA
Journal of Smart Agriculture and Environmental Technology
ISSN : -     EISSN : 30218802     DOI : https://doi.org/10.60105/josaet
The Journal of Smart Agriculture and Environmental Technology (JOSAET) is an international, interdisciplinary publication committed to addressing current issues in agriculture and environmental sciences. Our mission is to promote sustainable and safe food production practices for the future without causing environmental harm. JOSAET covers a broad range of topics, including soil, water, and plant management, pest control, and plant cultivation. We aim to anticipate environmental changes and contribute to the development of resilient agricultural practices. Published three times a year, in April, August, and December, JOSAET maintains rigorous standards through a high-quality peer-review process. We welcome manuscripts showcasing interdisciplinary expertise in areas such as: Enhancing crop production efficiency with new technologies to support agricultural sustainability, covering topics like crop management, pest control, environmental impact, input efficiency, new variety development, and socio-economic assessment. Fostering soil and water management strategies, such as the use of organic inputs, anticipation of soil pollution, soil health, and soil fertility inputs for sustainability. Exploring technology applications in various fields including soil science, agronomy, horticulture, plantation, forestry, aquaculture, husbandry farming, bioremediation, and application of machine learning and artificial intelligence in soil science, agriculture, and environment. We also welcome contributions discussing other environmental technologies aimed at improving our environment. JOSAET publishes original papers, short communications, and reviews concerning smart agriculture and environmental technology. We are particularly interested in research that promotes agricultural practices preserving the environment, enhancing crop production and income, mitigating global warming effects, and building food security in the face of climate change. Our vision is to be a leading voice in the discourse on sustainable agriculture and environmental management, facilitating innovation and knowledge exchange for a sustainable and resilient future.
Articles 61 Documents
Machine Learning-Driven Smart Aquaculture Technology for Climate-Resilient Water Quality Monitoring Moses Abiodun; Babatunde Adesina; S. Adebukola Onashoga; Kehinde Eniola; Kehinde Adeniyi; Babatomiwa Idris Rasheed
Journal of Smart Agriculture and Environmental Technology Vol. 4 No. 1 (2026): April 2026
Publisher : Indonesian Soil Science Society of South Sumatra in Collaboration With Soil Science Department, Sriwijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60105/josaet.2026.4.1.1-6

Abstract

The aquaculture sector is among the fastest-growing food production industries, playing a critical role in global food security by supplying high-quality protein to millions.  However, climate change has introduced severe challenges, disrupting production through altered temperature regimes, unpredictable rainfall, and deteriorating water quality. Key parameters such as pH, dissolved oxygen (DO), and salinity have shown significant fluctuations, which are directly affecting fish health, growth rates, reproduction, and overall pond productivity. To address these challenges, this study  proposes an integrated IoT and machine learning (ML) framework designed for real-time water quality monitoring and adaptive management in aquaculture systems. The primary objective is to enhance climate resilience by enabling data-driven decision-making for optimal fish health and production efficiency. A comprehensive dataset was sourced from reputable offline and online repositories, and then partitioned into training (90%) and testing (10%) subsets. Water quality was classified into two categories: “good” and “bad” based on critical thresholds for aquaculture sustainability. Four supervised machine learning algorithms were evaluated for classification performance, including Random Forest (RF) with an accuracy of 100%, demonstrating superior predictive capability, and Logistic Regression (LR) with an accuracy of 57%, indicating moderate performance, Support Vector Machine (SVM) yielded an accuracy of 62%, suitable for certain nonlinear patterns, and Naive Bayes (NB) attained 89% accuracy, offering a balance between speed and reliability. This research paves the way for next-generation smart aquaculture systems, bridging the gap between environmental monitoring and AI-based decision support.