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All Journal Journal of Degraded and Mining Lands Management International Journal of Advances in Intelligent Informatics Journal of Robotics and Control (JRC)
Oakley, Simon
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Development of Microclimate Data Recorder on Coffee-Pine Agroforestry Using LoRaWAN and IoT Technology Nurwarsito, Heru; Suprayogo, Didik; Sakti, Setyawan P.; Prayogo, Cahyo; Oakley, Simon; Wibawa, Aji Prasetya; Adaby, Resnu Wahyu
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.20991

Abstract

Microclimate monitoring in agroforestry is very important to understand the complex interactions between vegetation, soil, and the environment. Microclimate parameters include air and soil temperature, air humidity, soil moisture, and light intensity. This research aims to develop a new microclimate data recording system for coffee-pine agroforestry, utilizing LoRaWAN and IoT technology to capture real-time microclimate parameters. Unlike traditional data loggers that require manual download on-site, this innovative system enables instant data download from IoT servers, thereby increasing data efficiency and accessibility. The system proved effective, significantly improving the precision of air temperature and humidity, as well as soil temperature measurements, with an average accuracy of 100%. However, soil moisture and light intensity recorded lower accuracies of 81.23% and 82.56%, respectively, indicating potential areas for future research and system refinement. The system maintains a 15-minute sampling period, aligning with conventional datalogger intervals. This represents an advancement in precision agriculture for microclimate monitoring, enabling the data to be utilized in decision-making for agroforestry management, which involves complex interactions between the local microclimate and the broader ecological system. It underscores the significance of sustainable land use as a response to global climate change.
Optimizing coffee yields in agroforestry systems using WaNuLCAS model: A case study in Malang, Indonesia Fitra, Ahmad Ali Yuddin; Oakley, Simon; Prayogo, Cahyo; Ratna Sari, Rika; Saputra, Danny Dwi; Ishaq, Rizqi Maulana; Suprayogo, Didik
Journal of Degraded and Mining Lands Management Vol. 11 No. 4 (2024)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2024.114.6337

Abstract

Agroforestry systems have significant potential for development in increasing coffee production in Indonesia. Besides providing economic benefits, agroforestry can also have ecological impacts, such as improving soil structure, reducing erosion, and other environmental services. There is a complex interaction between trees, soil, and crops in agroforestry systems, making modeling a valuable approach to unraveling these processes. We utilized the spatial and temporal explicit model WaNuLCAS to (i) evaluate the model's performance in depicting actual events (through coffee production and soil water content), (ii) assess the dynamic processes influencing coffee production and the environmental impact of management patterns, (iii) formulate and simulate optimal scenarios for coffee production optimization. Data from a one-year period involving five agroforestry management patterns for coffee-pine in UB Forest were used as input for the model. The model validation results showed satisfactory and acceptable outcomes for coffee production and groundwater dynamics. WaNuLCAS simulation results indicated that pruning and thinning management are crucial factors in increasing coffee production and are related to creating optimal conditions for coffee plants (light, humidity, and inter-plant competition). Additionally, fertilization management can be combined as a supporting factor to meet the nutritional needs of coffee plants. WaNuLCAS simulation results also suggested that pruning and thinning can improve soil physical properties, but thinning increases surface runoff within the system. This research provides insights into how modeling can be used as a decision-making tool.
Imputation of missing microclimate data of coffee-pine agroforestry with machine learning Nurwarsito, Heru; Suprayogo, Didik; Sakti, Setyawan Purnomo; Prayogo, Cahyo; Yudistira, Novanto; Fauzi, Muhammad Rifqi; Oakley, Simon; Mahmudy, Wayan Firdaus
International Journal of Advances in Intelligent Informatics Vol 10, No 1 (2024): February 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i1.1439

Abstract

This research presents a comprehensive analysis of various imputation methods for addressing missing microclimate data in the context of coffee-pine agroforestry land in UB Forest. Utilizing Big data and Machine learning methods, the research evaluates the effectiveness of imputation missing microclimate data with Interpolation, Shifted Interpolation, K-Nearest Neighbors (KNN), and Linear Regression methods across multiple time frames - 6 hours, daily, weekly, and monthly. The performance of these methods is meticulously assessed using four key evaluation metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that Linear Regression consistently outperforms other methods across all time frames, demonstrating the lowest error rates in terms of MAE, MSE, RMSE, and MAPE. This finding underscores the robustness and precision of Linear Regression in handling the variability inherent in microclimate data within agroforestry systems. The research highlights the critical role of accurate data imputation in agroforestry research and points towards the potential of machine learning techniques in advancing environmental data analysis. The insights gained from this research contribute significantly to the field of environmental science, offering a reliable methodological approach for enhancing the accuracy of microclimate models in agroforestry, thereby facilitating informed decision-making for sustainable ecosystem management.
Co-Authors Adaby, Resnu Wahyu Aji Prasetya Wibawa Cahyo Prayogo, Cahyo Didik Suprayogo Fauzi, Muhammad Rifqi Fitra, Ahmad Ali Yuddin Heru Nurwarsito Ishaq, Rizqi Maulana Novanto Yudistira RATNA SARI, RIKA Saputra, Danny Dwi Setyawan P. Sakti Setyawan Purnomo Sakti Wayan Firdaus Mahmudy