Smart Farming Experiment: IoT-Enhanced Greenhouse Design for Rice Cultivation with Foliar and Soil Fertilization

This study introduces an IoT-enabled smart greenhouse system tailored for rice cultivation and designed as a controlled experimental platform to evaluate fertilizer application methods. Traditional greenhouse farming often struggles with unpredictable weather, pest infestations, and inefficient resource use. To overcome these challenges, the proposed system optimizes environmental conditions and enables precise monitoring and control through the Thingsboard IoT platform, which tracks temperature, humidity, and sunlight intensity in real time. The cultivation process involved Inceptisol soil preparation, slurrying, fertilization, seeding, transplantation, and continuous monitoring. The novelty lies in its dual-purpose design, enabling both cultivation and structured agronomic experimentation under identical environmental conditions. The system enables both rice cultivation and comparative testing of nano-silica fertilizer applied via root (soil) and foliar (leaf) methods. Automated temperature control (maintaining 20–36.5 °C) and humidity regulation (10–85% RH) with a mist blower response time under 5 s ensured consistent conditions. Sensor accuracy was validated with deviations of 0.4% (±0.11 °C) and 0.77% (±0.5% RH). Compared to conventional setups, this system achieved superior environmental stability and control precision, improving experimental reproducibility. Its integration potential with machine learning models opens new possibilities for forecasting plant responses based on historical data. Overall, the study demonstrates how advanced technology can enhance agricultural precision, sustainability, and research reliability.

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Publication Details

Journal
AgriEngineering
Published
2025-11-10
DOI
https://doi.org/10.3390/agriengineering7110380
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
0.00

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article

Smart Farming Experiment: IoT-Enhanced Greenhouse Design for Rice Cultivation with Foliar and Soil Fertilization

Camellia Panatarani, Aswaldi Anwar, Mimien Hariyanti, Oviyanti Mulyani et al.
AgriEngineering
Smart Agriculture and AI
article

Smart Farming Experiment: IoT-Enhanced Greenhouse Design for Rice Cultivation with Foliar and Soil Fertilization

Camellia Panatarani, Aswaldi Anwar, Mimien Hariyanti, Oviyanti Mulyani, Ni Nyoman Rupiasih, Sparisoma Viridi, Dwindra Wilham Maulana, Ni Luh Watiniasih, Pramujo Widiatmoko, Ferry Faizal, M P Khairunnisa
article en

Abstract

This study introduces an IoT-enabled smart greenhouse system tailored for rice cultivation and designed as a controlled experimental platform to evaluate fertilizer application methods. Traditional greenhouse farming often struggles with unpredictable weather, pest infestations, and inefficient resource use. To overcome these challenges, the proposed system optimizes environmental conditions and enables precise monitoring and control through the Thingsboard IoT platform, which tracks temperature, humidity, and sunlight intensity in real time. The cultivation process involved Inceptisol soil preparation, slurrying, fertilization, seeding, transplantation, and continuous monitoring. The novelty lies in its dual-purpose design, enabling both cultivation and structured agronomic experimentation under identical environmental conditions. The system enables both rice cultivation and comparative testing of nano-silica fertilizer applied via root (soil) and foliar (leaf) methods. Automated temperature control (maintaining 20–36.5 °C) and humidity regulation (10–85% RH) with a mist blower response time under 5 s ensured consistent conditions. Sensor accuracy was validated with deviations of 0.4% (±0.11 °C) and 0.77% (±0.5% RH). Compared to conventional setups, this system achieved superior environmental stability and control precision, improving experimental reproducibility. Its integration potential with machine learning models opens new possibilities for forecasting plant responses based on historical data. Overall, the study demonstrates how advanced technology can enhance agricultural precision, sustainability, and research reliability.

AgriEngineeringVol. 7(11)
Udayana University (ID), Bandung Institute of Technology (ID), University of Kuala Lumpur (MY), Andalas University (ID), Padjadjaran University (ID)
Universitas Padjadjaran
Openalex Percentile: Top 75%
Smart Agriculture and AI
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