Rule-Based IoT Decision Support System for Real-Time Chili Plant Growth Monitoring
Abstract
This study proposes a rule-based IoT decision support system for real-time chili plant growth monitoring. Its main contribution is an integrated framework for chili cultivation that combines plant growth indicators and environmental sensor data within a single transparent IF-THEN rule structure an integration not commonly addressed in prior chili-monitoring systems. The system integrates eight parameters stem height, branches, leaves, fruits, soil moisture, temperature, water volume, and soil pH collected from 100 plant samples and 704 repeated environmental sensor records over a single 14-week growing season. Classification is formalized as a maximum-severity decision over a knowledge base of eight rules: each rule carries a severity level, and a record receives the label of the most severe triggered rule (Less Optimal > Fairly Good > Optimal). Thresholds were set through a two-step calibration that combines established agronomic ranges for Capsicum annuum with empirical refinement against observed plant responses. The logic was validated against 100 expert-labeled records using a confusion matrix, achieving 96% overall accuracy (95% CI: 90.2–98.4%), a macro-average F1-score of 93.4%, and per-class F1-scores between 88.9% and 98.0%. Classification results show that 75% of records fell into the Less Optimal category, driven primarily by elevated temperature (mean 34.09°C) and acidic soil pH (mean 5.38). The system delivers interpretable, actionable decision support without complex machine learning infrastructure; its main limitation is that thresholds were calibrated on a single site and season, so external multi-site validation is required before broader deployment.
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M. Telaumbanua, F. K. Wisnu, S. E. R. Manurung, Y. Erika, B. Lanya, and A. Haryanto, “Enhancing Chili Farming through IoT-Enabled Microclimate Monitoring,” 2024, pp. 175–187. doi: 10.2991/978-94-6463-366-5_17.
E. Simanungkalit, M. Husna, J. S. Tarigan, and Suriadi, “Smart Garden on Chili Plants Based on IoT,” International Journal of Research in Vocational Studies (IJRVOCAS), vol. 3, no. 4, pp. 46–51, Jan. 2024, doi: 10.53893/ijrvocas.v3i4.146.
M. S. M. Pandi et al., “IoT Based Greenhouse Condition Monitoring System for Chili Plant Growth,” Journal of Advanced Research in Applied Sciences and Engineering Technology, vol. 41, no. 1, pp. 142–153, Nov. 2024, doi: 10.37934/araset.41.1.142153.
A. Martani, A. L. Perdana, and M. Anugrah, “Prototype Alat Monitoring Dan Kontrol Penyiraman Tanaman Cabai Berbasis Internet of Things (IoT) Dan Android,” Journal of System and Computer Engineering (JSCE), vol. 6, no. 1, pp. 72–82, Jan. 2025, doi: 10.61628/jsce.v6i1.1601.
Khairuman et al., “Irrigation control system design for paddy field by means of internet of things,” in IOP Conference Series: Earth and Environmental Science, IOP Publishing Ltd, Feb. 2021. doi: 10.1088/1755-1315/644/1/012065.
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.
L. Lindawati, A. Widianty, and M. M. Rose, “Fertigation Monitoring and Control System for Home Chili Cultivation Based on Internet of Things (IoT),” Jurnal Teknologi Informasi dan Pendidikan, vol. 18, no. 1, pp. 711–721, Mar. 2025, doi: 10.24036/jtip.v18i1.872.
R. Putri, R. Dewi, S. Rifka, S. Nita, and A. A. Dahlan, “IoT-Based Rainfall Monitoring System for Chili Farming Land,” Brilliance: Research of Artificial Intelligence, vol. 3, no. 2, pp. 510–519, Mar. 2024, doi: 10.47709/brilliance.v3i2.3649.
H. Shahab, M. Naeem, M. Iqbal, M. Aqeel, and S. S. Ullah, “IoT-driven smart agricultural technology for real-time soil and crop optimization,” Smart Agricultural Technology, vol. 10, Mar. 2025, doi: 10.1016/j.atech.2025.100847.
D. Widjaja and T. D. Sujiwa, “Data Transmission Performance on IoT Based Monitoring System of Soil Acidity and Moisture Levels in Chili Plantation.”
E. Barus, K. Kunci, P. Tanaman, and T. Cabai, “Sistem Monitoring Pertumbuhan Tanaman Berbasis Internet of Things,” Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI), vol. 6, no. 1, pp. 1–8, 2023.
S. Mansoor, S. Iqbal, S. M. Popescu, S. L. Kim, Y. S. Chung, and J. H. Baek, “Integration of smart sensors and IOT in precision agriculture: trends, challenges and future prospectives,” 2025, Frontiers Media SA. doi: 10.3389/fpls.2025.1587869.
V. H. U. Eze, E. C. Eze, G. U. Alaneme, P. E. BUBU, E. O. E. Nnadi, and M. Ben Okon, “Integrating IoT sensors and machine learning for sustainable precision agroecology: enhancing crop resilience and resource efficiency through data-driven strategies, challenges, and future prospects,” Discover Agriculture, vol. 3, no. 1, May 2025, doi: 10.1007/s44279-025-00247-y.
A. S. Ibrahim et al., “AI-IoT based smart agriculture pivot for plant diseases detection and treatment,” Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-98454-6.
N. Azman, D. M. Ali, and Y. Yusuf, “Smart Agricultural Monitoring System using IoT Application for Chili Plants,” Journal of Advanced Research in Applied Sciences and Engineering Technology, vol. 33, no. 1, pp. 53–66, Dec. 2023, doi: 10.37934/araset.33.1.5366.
L. HARTAWAN et al., “Design of IoT-based Greenhouse Temperature and Humidity Monitoring System,” ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, vol. 12, no. 4, p. 1051, Dec. 2024, doi: 10.26760/elkomika.v12i4.1051.
E. S. Barus, M. Zarlis, Z. Nasution, and Sutarman, “DEVELOPMENT OF MACHINE LEARNING FOR FORECASTING OPTIMIZATION IMPLEMENTED IN MORPHOLOGY PLANT GROWTH,” Eastern-European Journal of Enterprise Technologies, vol. 3, no. 2, pp. 42–53, 2025, doi: 10.15587/1729-4061.2025.331745.
P. Pertumbuhan, T. Barus, and E. Sabarita, “Optimisasi Berkelanjutan Menggunakan Machine Learning untuk Forecasting.” [Online]. Available: https://repositori.usu.ac.id/handle/123456789/107831
G. Kostopoulos, G. Davrazos, and S. Kotsiantis, “Explainable Artificial Intelligence-Based Decision Support Systems: A Recent Review,” Jul. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/electronics13142842.
D. Viondra Sihar Matondang, D. Saogo, R. F. Samuel Putra Sianturi, S. Dapit, E. Sabarita Barus, and F. Sains dan Teknologi, “ANALISIS PEMBERIAN NUTRISI MENGGUNAKAN METODE FUZZY LOGIC STUDI KASUS TANAMAN CABAI,” Jurnal TEKINKOM, vol. 6, no. 2, p. 2023, doi: 10.37600/tekinkom.v6i2.929.
E. Simangunsong, J. Medi Situmeang, and E. Sabarita Barus, “ANALYSIS OF ANTHRACNOSE DISEASE IN CURLY CHILI USING FUZZY LOGIC METHOD ANALISIS PENYAKIT ANTRAKNOSA PADA CABAI KERITING MENGGUNAKAN METODE FUZZY LOGIC,” vol. 10, no. 2, p. 2025.
S. Sahputra, D. C. Sembiring, I. H. Sipayung, and E. S. Barus, “ANALISIS PREDIKSI HASIL PRODUKSI TANAMAN CABAI MENGGUNAKAN METODE MULTI LINIER REGRESI,” Jurnal Teknik Informasi dan Komputer (Tekinkom), vol. 7, no. 2, p. 619, Dec. 2024, doi: 10.37600/tekinkom.v7i2.1512.
L. Aritonang, B. Aryowindo, R. Syarif, and E. Sabarita Barus, “FORECASTING RED CHILLI PLANT GROWTH USING TIME SERIES METHOD WITH LONG SHORT-TERM MEMORY MODEL FORECASTING PERTUMBUHAN TANAMAN CABAI MERAH MENGGUNAKAN METODE TIME SERIES DENGAN MODEL LONG SHORT-TERM MEMORY,” vol. 10, no. 2, p. 2025.
DOI: http://dx.doi.org/10.30829/zero.v10i2.30236
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