Optimizing Automatic Irrigation Duration for Grapevines in Greenhouses Using Multiple Linear Regression Analysis

Kharisma Monika Dian Pertiwi; Trenady  Alfarabi

doi:10.52436/1.jutif.2026.7.2.5289

Authors

Kharisma Monika Dian Pertiwi Informatics Study Program, Telkom University, Surabaya Campus, Jl. Ketintang No.156, Surabaya 60231, East Java, Indonesia
Trenady Alfarabi Informatics Study Program, Telkom University, Surabaya Campus, Jl. Ketintang No.156, Surabaya 60231, East Java, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.2.5289

Keywords:

Automatic Irrigation, Grapevines, Internet of Things, Multiple Linear Regression, Precision Agriculture, Soil Moisture

Abstract

Greenhouses offer a controllable microclimate for high‑value horticulture, yet manual irrigation and single‑sensor threshold rules remain inefficient and error‑prone for grapevine cultivation in tropical conditions. This study designs and implements an Internet‑of‑Things (IoT) automatic irrigation system that employs an interpretable multiple linear regression (MLR) model as the decision core, using air temperature and soil moisture—acquired via DHT11 and capacitive soil‑moisture sensors—to estimate irrigation duration in real time. The model is trained on greenhouse measurements and deployed for low‑latency edge inference to actuate valves with duration‑to‑volume conversion, enabling precise and adaptive water delivery. Experimental evaluation shows strong predictive performance (MSE = 0.15, MAPE = 1.44%, R² = 0.98), indicating high accuracy and reliable generalization for operational control. The primary contributions are: (i) a lightweight, explainable regression formulation tailored to tropical grapevines that outperforms single‑parameter baselines; (ii) an end‑to‑end, edge‑deployable IoT pipeline that reduces computational and energy costs while maintaining real‑time autonomy; and (iii) an engineering blueprint that is scalable and maintainable for smallholder contexts. The impact for Informatics/Computer Science lies in demonstrating a practical ML‑on‑the‑edge reference design—combining interpretable modeling, sensor fusion, and actuation—that advances sustainable computing for precision agriculture, improves resource efficiency, and supports robust, replicable deployment of smart‑irrigation systems in data and power‑constrained environments.

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References

U. Ristian, I. Ruslianto, and K. Sari, “Sistem Monitoring Smart Greenhouse pada Lahan Terbatas Berbasis Internet of Things (IoT),” Jurnal Edukasi dan Penelitian Informatika (JEPIN), vol. 8, no. 1, p. 87, 2022, doi: 10.26418/jp.v8i1.52770.

Y. Tian et al., “Passive cooling of greenhouses in extreme climates through spectral control film,” Nexus, vol. 2, no. 1, p. 100058, Mar. 2025, doi: 10.1016/j.ynexs.2025.100058.

C. Maraveas, D. Loukatos, T. Bartzanas, K. G. Arvanitis, and J. F. Uijterwaal, “Smart and Solar Greenhouse Covers: Recent Developments and Future Perspectives,” Nov. 17, 2021, Frontiers Media S.A. doi: 10.3389/fenrg.2021.783587.

S. Chen, A. Liu, F. Tang, P. Hou, Y. Lu, and P. Yuan, “A Review of Environmental Control Strategies and Models for Modern Agricultural Greenhouses,” Sensors, vol. 25, no. 5, p. 1388, Feb. 2025, doi: 10.3390/s25051388.

C. Yan, T. Na, Q. Zhen, Y. Sun, and K. Liu, “Prediction of air temperature and humidity in greenhouses via artificial neural network,” PLoS One, vol. 20, no. 6, p. e0325650, Jun. 2025, doi: 10.1371/journal.pone.0325650.

B. Guo et al., “A Critical Review of the Status of Current Greenhouse Technology in China and Development Prospects,” Applied Sciences (Switzerland), vol. 14, no. 13, 2024, doi: 10.3390/app14135952.

M. A. Nwanojuo, C. K. Anumudu, and H. Onyeaka, “Impact of Controlled Environment Agriculture (CEA) in Nigeria, a Review of the Future of Farming in Africa,” Agriculture, vol. 15, no. 2, p. 117, Jan. 2025, doi: 10.3390/agriculture15020117.

C. Maraveas, D. Piromalis, K. G. Arvanitis, T. Bartzanas, and D. Loukatos, “Applications of IoT for optimized greenhouse environment and resources management,” Comput Electron Agric, vol. 198, no. April, p. 106993, 2022, doi: 10.1016/j.compag.2022.106993.

M. A. Tawfeek, S. Alanazi, and A. A. A. El-Aziz, “Smart Greenhouse Based on ANN and IOT,” Processes, vol. 10, no. 11, pp. 1–17, 2022, doi: 10.3390/pr10112402.

H. Luo et al., “Transparent solar photovoltaic windows provide a strong potential for self-sustainable food production in forward-looking greenhouse farming architectures,” Clean Eng Technol, vol. 24, p. 100895, Feb. 2025, doi: 10.1016/j.clet.2025.100895.

M. Gholami, A. Arefi, A. Hasan, C. Li, and S. M. Muyeen, “Enhancing energy autonomy of greenhouses with semi-transparent photovoltaic systems through a comparative study of battery storage systems,” Sci Rep, vol. 15, no. 1, p. 2213, Jan. 2025, doi: 10.1038/s41598-025-85418-z.

J. Xu, B. Gu, and G. Tian, “Review of agricultural IoT technology,” Artificial Intelligence in Agriculture, vol. 6, pp. 10–22, 2022, doi: 10.1016/j.aiia.2022.01.001.

C. Li, J. Wang, S. Wang‎, and Y. Zhang, “A review of IoT applications in healthcare,” Neurocomputing, vol. 565, no. May 2023, p. 127017, 2024, doi: 10.1016/j.neucom.2023.127017.

Y. Bin Zikria, R. Ali, M. K. Afzal, and S. W. Kim, “Next-generation internet of things (IoT): Opportunities, challenges, and solutions,” Feb. 02, 2021, MDPI AG. doi: doi.org/10.3390/s21041174.

K. Maros, “Peningkatan Keberdayaan Usaha Budidaya Jamur Tiram Melalui Implementasi Penyiraman Otomatis Berbasis IoT,” vol. 4, no. 5, 2024, doi: 10.59818/jpm.v4i5.881.

B. Pradhan, S. Bhattacharyya, and K. Pal, “IoT-Based Applications in Healthcare Devices,” J Healthc Eng, vol. 2021, 2021, doi: 10.1155/2021/6632599.

A. Chatterjee and B. S. Ahmed, “IoT anomaly detection methods and applications: A survey,” Internet of Things (Netherlands), vol. 19, no. June, p. 100568, 2022, doi: 10.1016/j.iot.2022.100568.

J. Contreras-Castillo, J. A. Guerrero-Ibañez, P. C. Santana-Mancilla, and L. Anido-Rifón, “SAgric-IoT: An IoT-Based Platform and Deep Learning for Greenhouse Monitoring,” Applied Sciences (Switzerland), vol. 13, no. 3, 2023, doi: 10.3390/app13031961.

Ircham Ali, S. Warisma, and A. Aljabar, “Arduino Microcontroller-Based Automatic Irrigation System for Grape Cultivation,” Journal of Artificial Intelligence and Engineering Applications (JAIEA), vol. 5, no. 1, pp. 816–821, Oct. 2025, doi: 10.59934/jaiea.v5i1.1477.

L. I. Haryanto, “Farmer Perspectives On Livelihoods Within Grape Community In South Tangerang City,” Jambura Agribusiness Journal, vol. 4, no. 1, pp. 23–32, Jul. 2022, doi: 10.37046/jaj.v4i1.14361.

Luh Putu Yuni Widyastuti and Ni Kadek Ema Sustia Dewi, “Productivity and Brix value of Green Grapes (Vitis vinifera L var. Muscat Saint Vallier) at Different Location and Pruning Time in Buleleng Bali,” SEAS (Sustainable Environment Agricultural Science), vol. 7, no. 2, pp. 139–144, Oct. 2023, doi: 10.22225/seas.7.2.8222.139-144.

Y. Yunita, M. A. I. Yunus, K. Mirbah, M. A. U. N, and S. Sukmawati, “Menguak Potensi Tersembunyi Kebun Anggur: Analisis Pengelolaan dan SOP Agro Secino dalam Memenuhi Kebutuhan Pasar,” Jurnal Ekonomi Pertanian dan Agribisnis, vol. 2, no. 2, pp. 88–95, Nov. 2024, doi: 10.62379/jepag.v2i1.2246.

J. Martínez-Lüscher, J. T. Matus, E. Gomès, and I. Pascual, “Toward understanding grapevine responses to climate change: a multi-stress and holistic approach,” J Exp Bot, vol. 76, no. 11, pp. 2949–2969, Aug. 2025, doi: 10.1093/jxb/erae482.

S. Firdaus, T. Rismawan, and U. Ristian, “Sistem Manajemen Pengairan Pada Budidaya Tanaman Anggur Berbasis Internet of Things (Iot),” Jurnal Informatika dan Teknik Elektro Terapan, vol. 11, no. 3s1, pp. 907–916, 2023, doi: 10.23960/jitet.v11i3s1.3389.

A. R. Putri, A. Syakur, and Muhardi, “The Impact of Climate Change on Grape Production in Indonesia,” 2023, pp. 54–60. doi: 10.2991/978-94-6463-144-9_6.

L. A. Arias, F. Berli, A. Fontana, R. Bottini, and P. Piccoli, “Climate Change Effects on Grapevine Physiology and Biochemistry: Benefits and Challenges of High Altitude as an Adaptation Strategy,” Front Plant Sci, vol. 13, May 2022, doi: 10.3389/fpls.2022.835425.

A. Syahri and R. Ulansari, “Penyiraman Otomatis dengan NodeMcu Berbasis Iot Untuk Tanaman Cabai,” Jurnal Teknologi Informasi, vol. 9, no. 1, pp. 38–44, 2023, doi: 10.52643/jti.v9i1.3173.

E. Schiller, J. Ajayi, S. Weber, T. Braun, and B. Stiller, “Toward a Live BBU Container Migration in Wireless Networks,” IEEE Open Journal of the Communications Society, vol. 3, pp. 301–321, 2022, doi: 10.1109/OJCOMS.2022.3149965.

G. Custódio and R. C. Prati, “Comparing modern and traditional modeling methods for predicting soil moisture in IoT-based irrigation systems,” Smart Agricultural Technology, vol. 7, no. August 2023, p. 100397, 2024, doi: 10.1016/j.atech.2024.100397.

R. Andrianto and F. Irawan, “Implementasi Metode Regresi Linear Berganda Pada Sistem Prediksi Jumlah Tonase Kelapa Sawit di PT . Paluta Inti Sawit,” Jurnal Pendidikan Tambusai, vol. 7, no. 1, pp. 2926–2934, 2023.

F. Wadly, “Integrating the NodeMCU ESP8266 Microcontroller with the DHT11 and MQ-135 Sensors Enables IoT-Based Air Quality Monitoring,” vol. 2, no. 1, 2025.

A. R. A. S. E. S. Mudofar Baehaqi, “5-Pengujian Performa Sensor DHT11 dan DS18B20 Sebagai Sensor Suhu Ruang Server,” Mestro Jurnal Ilmiah, vol. 2, no. 02, pp. 6–12, 2023.

A. A. A. Halim, R. Mohamad, F. Y. A. Rahman, H. Harun, and N. M. Anas, “IoT based smart irrigation, control, and monitoring system for chilli plants using NodeMCU-ESP8266,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 5, pp. 3053–3060, 2023, doi: 10.11591/eei.v12i5.5266.

A. Herlina, M. I. Syahbana, M. A. Gunawan, and M. M. Rizqi, “Sistem Kendali Lampu Berbasis Iot Menggunakan Aplikasi Blynk 2.0 Dengan Modul Nodemcu Esp8266,” INSANtek, vol. 3, no. 2, pp. 61–66, 2022, doi: 10.31294/instk.v3i2.1532.

A. Wardhana, “OTOMATIS TANAMAN ANGGUR PADA GREENHOUSE Program Studi Sarjana Teknologi Informasi ( Kampus Kota Surabaya ) Fakultas Informatika Universitas Telkom Surabaya,” 2024.

R. F. Osemeke, J. N. Igabari, and N. D. Christian, “Detection and Correction of Violations of Linear Model Assumptions by Means of Residuals,” Journal of Science Innovation and Technology Research (JSITR), vol. 3, no. 9, pp. 1–15, 2024.

G. Mardiatmoko, “The Application of the Classical Assumption Test in Multiple Linear Regression Analysis (a Case Study of the Preparation of the Allometric Equations of Young Makila),” JTAM (Jurnal Teori dan Aplikasi Matematika), vol. 8, no. 3, p. 724, 2024, doi: 10.31764/jtam.v8i3.22179.

P. Serafin, “Modern web technology – frameworks, advantages, disadvantages and optimal applications,” Computer Science and Mathematical Modelling, vol. 0, no. 19/2024, pp. 25–34, 2024, doi: 10.5604/01.3001.0055.0854.

N. A. Fitri, R. Z. Emba, M. R. Mufid, A. Fiyanto, W. Wajib, and A. Shofyan, “Kediri City Tourism Object Application Using Firebase Realtime Database Technology,” Proceedings of the International Conference on Applied Science and Technology on Social Science 2021 (iCAST-SS 2021), vol. 647, pp. 892–897, 2022, doi: 10.2991/assehr.k.220301.147.

M. Shanmugavalli and K. M. J. Ignatia, “Comparative Study among MAPE, RMSE and R Square over the Treatment Techniques Undergone for PCOS Influenced Women,” Recent Patents on Engineering, vol. 19, no. 1, 2025, doi: 10.2174/0118722121269786231120122435.

D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput Sci, vol. 7, pp. 1–24, 2021, doi: 10.7717/PEERJ-CS.623.

A. A. A. Halim, R. Mohamad, F. Y. A. Rahman, H. Harun, and N. M. Anas, “IoT based smart irrigation, control, and monitoring system for chilli plants using NodeMCU-ESP8266,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 5, pp. 3053–3060, Oct. 2023, doi: 10.11591/eei.v12i5.5266.

… | Wahjuni, S. Wulandari, and M. Kholili, “Development of Fuzzy-Based Smart Drip Irrigation System for Chili Cultivation,” 2022.

J. Martínez-Lüscher, J. T. Matus, E. Gomès, and I. Pascual, “Toward understanding grapevine responses to climate change: a multi-stress and holistic approach,” J Exp Bot, vol. 76, no. 11, pp. 2949–2969, Aug. 2025, doi: 10.1093/jxb/erae482.

L. P. Challa, C. D. Singh, K. V. R. Rao, A. Subeesh, and M. Srilakshmi, “Prediction of soil moisture using machine learning techniques: A case study of an IoT‐based irrigation system in a naturally ventilated polyhouse,” Irrigation and Drainage, vol. 73, no. 3, pp. 1138–1150, Jul. 2024, doi: 10.1002/ird.2933.

S. Gupta et al., “Smart agriculture using IoT for automated irrigation, water and energy efficiency,” Smart Agricultural Technology, vol. 12, p. 101081, Dec. 2025, doi: 10.1016/j.atech.2025.101081.

G. D. Shimizu and L. S. A. Gonçalves, “AgroReg: main regression models in agricultural sciences implemented as an R Package,” Sci Agric, vol. 80, 2023, doi: 10.1590/1678-992x-2022-0041.

A. A. A. Halim, R. Mohamad, F. Y. A. Rahman, H. Harun, and N. M. Anas, “IoT based smart irrigation, control, and monitoring system for chilli plants using NodeMCU-ESP8266,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 5, pp. 3053–3060, Oct. 2023, doi: 10.11591/eei.v12i5.5266.

E. K. Pramartaningthyas, S. Ma’shumah, and A. Al Hannan, “Design and Implementation of an IoT-Based Automatic Irrigation and Monitoring System for Bird’s Eye Chili Plants with Telegram and Blynk Platform Integration,” G-Tech: Jurnal Teknologi Terapan, vol. 9, no. 3, pp. 1248–1257, Jul. 2025, doi: 10.70609/g-tech.v9i3.7160.

G. D. Shimizu and L. S. A. Gonçalves, “AgroReg: main regression models in agricultural sciences implemented as an R Package,” Sci Agric, vol. 80, 2023, doi: 10.1590/1678-992x-2022-0041.

M. A. Mattar, D. K. Roy, H. M. Al-Ghobari, and A. Z. Dewidar, “Machine learning and regression-based techniques for predicting sprinkler irrigation’s wind drift and evaporation losses,” Agric Water Manag, vol. 265, p. 107529, May 2022, doi: 10.1016/j.agwat.2022.107529.

R. A. Syahputra and D. Andriani, “A Predictive Model For Crop Irrigation Schedulling Using Machine Learning and IoT-Generated Environmental Data,” 2025. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC