Design and architecture of an IoT-enabled bamboo resource management system: Data-driven approach for sustainable agriculture
Paper Details
Design and architecture of an IoT-enabled bamboo resource management system: Data-driven approach for sustainable agriculture
Abstract
Bamboo is an important renewable resource with significant ecological and economic value, particularly in regions where it contributes to rural livelihoods and sustainable agriculture. However, traditional bamboo farming practices often rely on manual monitoring and limited data, which can reduce efficiency in resource management and crop productivity. This study proposes the design and architecture of an Internet of Things (IoT)-enabled Bamboo Resource Management System (BRMS) aimed at supporting sustainable bamboo cultivation in Northeastern Cagayan, Philippines. The system was developed using a design-oriented research approach that integrates stakeholder feedback, field observations, and analysis of existing bamboo farming challenges. The proposed framework consists of four main stages: requirement identification, system architecture design, hardware and software selection, and development of a comprehensive data model. The system utilizes IoT sensors and GPS technology to collect real-time environmental and spatial data from bamboo plantations. These data are transmitted through cellular and Wi-Fi networks and stored in a cloud-based MySQL database. Data analysis is conducted using Python-based analytical libraries to generate actionable insights for resource management. A web-based dashboard developed using PHP, JavaScript, and Bootstrap enables users to monitor plantation conditions and access decision-support information through an interactive interface. The proposed BRMS architecture demonstrates how IoT technology and data analytics can improve monitoring, resource allocation, and decision-making in bamboo farming. The system provides a scalable framework for integrating digital technologies into sustainable agricultural practices and may serve as a model for smart farming applications in other crop production systems.
Abbasi R, Martinez P, Ahmad R. 2022. The digitization of agricultural industry: A systematic literature review on agriculture 4.0. Smart Agricultural Technology 2, 100042. https://doi.org/10.1016/j.atech.2022.100042
Alam MFB, Tushar SR, Zaman SM, Gonzalez EDRS, Bari ABMM, Karmaker CL. 2023. Analysis of the drivers of agriculture 4.0 implementation in emerging economies: Implications towards sustainability and food security. Green Technologies and Sustainability 1(2), 100021. https://doi.org/10.1016/j.grets.2023.100021
Binfield L, Britton TL, Dai C, Innes J. 2022. Evidence on the social, economic, and environmental impact of interventions that facilitate bamboo industry development for sustainable livelihoods: A systematic map protocol. Environmental Evidence 11(1), 1–12. https://doi.org/10.1186/s13750-022-00286-8
Cesco S, Sambo P, Borin M, Basso B, Orzes G, Mazzetto F. 2023. Smart agriculture and digital twins: applications and challenges in a vision of sustainability. European Journal of Agronomy 146, 126809. https://doi.org/10.1016/j.eja.2023.126809
Chamara N, Bai G, Ge Y. 2023. AICropCAM: Deploying classification, segmentation, detection and counting deep-learning models for crop monitoring on the edge. Computers and Electronics in Agriculture 215, 108420.https://doi.org/10.1016/j.compag.2023.108420
Dhanaraju M, Chenniappan P, Ramalingam K, Pazhanivelan S, Kaliaperumal R. 2022. Smart farming: Internet of Things (IoT)-based sustainable agriculture. Agriculture 12(10), 1745. https://doi.org/10.3390/agriculture12101745
Gupta A, Kumar S, Singh P, Sharma V. 2023. IoT-based smart agriculture monitoring system using machine learning and sensor networks. Computers and Electronics in Agriculture 206, 107637.
Kamilaris A, Prenafeta-Boldú FX. 2018. Deep learning in agriculture: A survey. Computers and Electronics in Agriculture 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016
Mohamed Ismail Ahmed DS. 2021. The role of technology in small agricultural projects. International Journal of Modern Agriculture and Environment 1(1), 1–21. https://doi.org/10.21608/ijmae.2023.215955.1015
Nalini T, Rama A. 2022. Impact of temperature condition in crop disease analyzing using machine learning algorithm. Measurement: Sensors 24, 100408. https://doi.org/10.1016/j.measen.2022.100408
Pathmudi VR, Khatri N, Kumar S, Abdul-Qawy ASH, Vyas AK. 2023. A systematic review of IoT technologies and their constituents for smart and sustainable agriculture applications. Scientific African 19, e01577. https://doi.org/10.1016/j.sciaf.2023.e01577
Spandana K, Pabboju S. 2023. IoT enabled smart agriculture using digital dashboard. Indian Journal of Science and Technology 16(1), 1–11. https://doi.org/10.17485/ijst/v16i1.1680
Thilakarathne NN, Bakar MSA, Abas PE, Yassin H. 2022. A cloud enabled crop recommendation platform for machine learning-driven precision farming. Sensors 22(16), 6299. https://doi.org/10.3390/s22166299
Charlot L. Maramag*, Dorothy M. Ayuyang, Richard R. Ayuyang, 2026. Design and architecture of an IoT-enabled bamboo resource management system: Data-driven approach for sustainable agriculture. Int. J. Biosci., 28(4), 107-115.
Copyright © 2026 by the Authors. This article is an open access article and distributed under the terms and conditions of the Creative Commons Attribution 4.0 (CC BY 4.0) license.