Smart Greenhouse Data Integration Using Machine Learning Approaches

Shafa  Auliya Arfiyani; Eni  Dwi Wardihani; Helmy

doi:10.22146/jnteti.v15i2.21951

Shafa Auliya Arfiyani Telecommunication Engineering Study Program, Department of Electrical Engineering, Politeknik Negeri Semarang, Semarang, Jawa Tengah 50275, Indonesia
Eni Dwi Wardihani Telecommunication Engineering Study Program, Department of Electrical Engineering, Politeknik Negeri Semarang, Semarang, Jawa Tengah 50275, Indonesia
Helmy Telecommunication Engineering Study Program, Department of Electrical Engineering, Politeknik Negeri Semarang, Semarang, Jawa Tengah 50275, Indonesia

DOI: https://doi.org/10.22146/jnteti.v15i2.21951

Keywords: Machine Learning, Smart Greenhouse, Decision Tree, Random Forest, Naïve Bayes

Abstract

In modern agriculture, smart greenhouse systems play an important role in improving agricultural productivity by integrating internet of things (IoT) technology and data-driven decision making. This approach is particularly relevant in hydroponic lettuce cultivation, where rapidly changing greenhouse conditions directly influence plant growth and yield, making accurate yield prediction a challenging task. This study aimed to compare the performance of three machine learning algorithms, namely random forest, decision tree, and naïve Bayes, in predicting hydroponic lettuce yield using data obtained from an IoT-based smart greenhouse system. A total of 15,492 data samples were collected from two greenhouse locations using sensors that measured air temperature, water temperature, humidity, light intensity, pH, and nutrient levels. Prior to model development, the dataset was preprocessed through data cleaning, normalization, and integration stages, and then divided into training and testing sets using an 80:20 ratio. Model performance was evaluated using mean absolute error (MAE) and the coefficient of determination (R²). The experimental results showed that the random forest model achieved the best performance with an MAE of 3.51 and an R² value of 0.8511, followed by the decision tree model with an R² of 0.851 and the naïve Bayes model with an R² of 0.7245. These findings indicate that integrating IoT-based smart greenhouse monitoring with machine learning models, particularly random forest, enables accurate crop yield prediction and supports effective decision making for sustainable hydroponic agriculture.

References

M. Ghiasi, Z. Wang, M. Mehrandezh, and R. Paranjape, “A systematic review of optimal and practical methods in design, construction, control, energy management and operation of smart greenhouses,” IEEE Access, vol. 12, pp. 2830–2853, Jan. 2024, doi: 10.1109/ACCESS.2023.3346436.

H. Andrianto, Suhardi, and A. Faizal, “Development of smart greenhouse system for hydroponic agriculture,” in 2020 Int. Conf. Inf. Technol. Syst. Innov. (ICITSI), 2020, pp. 335–340, doi: 10.1109/ICITSI50517.2020.9264917.

A. Rifin, “Marketed surplus of Indonesian rice production,” Cogent Econ. Finance, vol. 10, no. 1, pp. 1–14, Sep. 2022, doi: 10.1080/23322039.2022.2119694.

V. Mamatha and J.C. Kavitha, “Machine learning based crop growth management in greenhouse environment using hydroponics farming techniques,” Meas., Sens., vol. 25, pp. 1–7, Feb. 2023, doi: 10.1016/j.measen.2023.100665.

G.R.E. Bradley and P.A. Kyriacou, “Evaluating the effectiveness of non-invasive intracranial pressure monitoring via near-infrared photoplethysmography using classical machine learning methods,” Biomed. Signal Process. Control, vol. 96, pp. 1–11, Oct. 2024, doi: 10.1016/j.bspc.2024.106517.

B.A. Muthohar and L. Zahrotun, “Sistem pendukung keputusan pemilihan pupuk pada bawang merah dengan metode analytical hierarchy process,” INFOTECH, J. Inform. Teknol., vol. 4, no. 1, pp. 71–84, Jun. 2023, doi: 10.37373/infotech.v4i1.560.

D.W. Triscowati and A.W. Wijayanto, “Peluang dan tantangan dalam pemanfaatan teknologi penginderaan jauh dan machine learning untuk prediksi data tanaman pangan yang lebih akurat,” in Pros. Semin. Nas. Off. Stat., 2019, 2019, pp. 177–187, doi: 10.34123/semnasoffstat.v2019i1.230.

A. Ambarwari, D.K. Widyawati, and A. Wahyudi, “Sistem pemantau kondisi lingkungan pertanian tanaman pangan dengan NodeMCU ESP8266 dan Raspberry Pi berbasis IoT,” J. RESTI (Rekayasa Sist. Teknol. Inf.), vol. 5, no. 3, pp. 496–503, Jun. 2021, doi: 10.29207/resti.v5i3.3037.

M. Guesmiet et al., “Gas liquid flow pattern prediction in horizontal and slightly inclined pipes: From mechanistic modelling to machine learning,” Appl. Math. Model., vol. 138, pp. 1–17, Feb. 2025, doi: 10.1016/j.apm.2024.115748.

G. Liu et al., “A state of art review on time series forecasting with machine learning for environmental parameters in agricultural greenhouses,” Inf. Process. Agric., vol. 11, no. 2, pp. 143–162, Jun. 2024, doi: 10.1016/j.inpa.2022.10.005.

M.S. Farooq, R. Javid, S. Riaz, and Z. Atal, “IoT based smart greenhouse framework and control strategies for sustainable agriculture,” IEEE Access, vol. 10, pp. 99394–99420, Sep. 2022, doi: 10.1109/ACCESS.2022.3204066.

I. Ullah, M. Fayaz, M. Aman, and D. Kim, “An optimization scheme for IoT based smart greenhouse climate control with efficient energy consumption,” Computing, vol. 104, no. 2, pp. 433–457, Feb. 2022, doi: 10.1007/s00607-021-00963-5.

A. Castañeda-Miranda and V.M. Castaño-Meneses, “Internet of things for smart farming and frost intelligent control in greenhouses,” Comput. Electron. Agric., vol. 176, pp. 1–23, Sep. 2020, doi: 10.1016/j.compag.2020.105614.

A. Elvanidi and N. Katsoulas, “Machine learning-based crop stress detection in greenhouses,” Plants, vol. 12, no. 1, pp. 1–20, Jan. 2023, doi: 10.3390/plants12010052.

P.K. Tripathy, A.K. Tripathy, A. Agarwal, and S.P. Mohanty, “MyGreen: An IoT-enabled smart greenhouse for sustainable agriculture,” IEEE Consum. Electron. Mag., vol. 10, no. 4, pp. 57–62, Jul. 2021, doi: 10.1109/MCE.2021.3055930.

Helmy et al., “Pemantauan dan pengendalian parameter akuaponik menggunakan representational state transfer application programming interface,” J. Nas. Tek. Elekt. Teknol. Inf., vol. 9, no. 4, pp. 394–402, Nov. 2020, doi: 10.22146/jnteti.v9i4.594.

C.M. Angelopoulos, G. Filios, S. Nikoletseas, and T.P. Raptis, “Keeping data at the edge of smart irrigation networks: A case study in strawberry greenhouses,” Comput. Netw., vol. 167, pp. pp. 1–10, Feb. 2020, doi: 10.1016/j.comnet.2019.107039.

T. van Klompenburg, A. Kassahun, and C. Catal, “Crop yield prediction using machine learning: A systematic literature review,” Comput. Electron. Agric., vol. 177, pp. 1–18, Oct. 2020, doi: 10.1016/j.compag.2020.105709.

H.W. Herwanto, T. Widiyaningtyas, and P. Indriana, “Penerapan algoritme linear regression untuk prediksi hasil panen tanaman padi,” J. Nas. Tek. Elekt. Teknol. Inf., vol. 8, no. 4, pp. 364–370, Nov. 2019, doi: 10.22146/jnteti.v8i4.537.

Frira Sesilia, V.H. Pranatawijaya, and R. Priskila, “Machine learning untuk memprediksi jumlah penjualan, stok dan jumlah tanam hasil pertanian hidroponik,” Konstelasi, Konvergensi Teknol. Sist. Inf., vol. 4, no. 1, pp. 222–233, Jun. 2024, doi: 10.24002/konstelasi.v4i1.9055.