Smart Nutrition Monitoring: IoT-Based Rice Soil Health Classification Using Naive Bayes and Fuzzy Expert System
DOI:
https://doi.org/10.17509/chcry728Keywords:
Machine Learning, Naive Bayes, Expert Systems, Fuzzy Logic, Internet of Things, Rice PlantsAbstract
The implementation of Machine Learning (ML) in precision agriculture often stops at probabilistic outputs without producing directly executable (actionable) agronomic guidance. In response to this functional gap, this study created an IoT proof of Concept (PoC) system architecture design that integrates Machine Learning with the Mamdani Fuzzy Expert System. To validate the software before entering the implementation phase, the system was trained and tested using 5000 synthetic datasets. This synthetic data was generated using a Multimodal Gaussian distribution to represent real-world conditions in the field (TDS, pH, humidity, and temperature). The evaluation results obtained proved that the Naive Bayes classification was very efficient for use in system architectures with minimal resources, with an accuracy of 96.65% and a latency of 0.054. In the final stage of the system, the Fuzzy Logic integration successfully produced output in the form of solutions and recommendations based on the values generated by the Machine Learning system. The integration of these three systems successfully became a unified, complete architectural system. This system has proven to be able to bridge the gap between the complexity of the system and the needs of farmers, and is ready to be implemented in the field using real-time data.
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[1] W. I. Susanti, S. N. Cholidah, and F. Agus, “Agroecological Nutrient Management Strategy for Attaining Sustainable Rice Self-Sufficiency in Indonesia,” Sustainability, vol. 16, no. 2, pp. 1–29, 2024, doi: 10.3390/su16020845.
[2] C. C. Daulay and S. Arka, “Determinants of Indonesia’s Food Security 2019–2023: A Panel Data Analysis,” Lit. Int. Sci. Journals Soc. Educ. Humanit., vol. 4, no. 2, pp. 121–137, 2025, [Online]. Available: https://doi.org/10.56910/literacy.v4i2.2313
[3] I. K. Rahakbauw and P. Lindiasari Samputra, “Analisis Tantangan dan Strategi Ketahanan Pangan di Indonesia,” J. AGRICA, vol. 18, no. 1, pp. 1–17, 2024.
[4] F. W. D. Fika, M. Maulana, R. A. Wigunawan, and R. M. Putri, “Analisis Pengaruh Harga Gabah , Impor , dan Produksi terhadap Kesejahteraan Petani ( Studi Kasus : NTP di Indonesia ),” J. Ekon. dan Pembang. Indones., vol. 3, no. 3, pp. 85–100, 2025, doi: 10.61132/jepi.v3i3.1523.
[5] R. Aliya and N. P. Citra, “Analisis pengaruh kebijakan impor beras terhadap permintaan dalam negeri,” J. Ilm. Ekon. Dan Manaj., vol. 3, no. 2, pp. 73–83, 2025, [Online]. Available: https://doi.org/10.61722/jiem.v3i2.3786
[6] T. W. Saleh et al., “The Impact of Rice Import Policy on Grain and Rice Prices in Indonesia ( Case Study of Rice Import Data 2017-2023 ),” IOP Conf. Ser. Earth Environ. Sci., vol. 1482, no. 1, 2025, doi: 10.1088/1755-1315/1482/1/012035.
[7] H. Hamadani, S. M. Rashid, J. Parrah, and A. Khan, “Traditional Farming Practices and Its Consequences,” Microbiota and Biofertilizers, vol. 2, pp. 119–128, 2021, doi: 10.1007/978-3-030-61010-4.
[8] P. R. Bhokare and R. R. Wankhade, “Impacts of chemical fertilizer on agricultural soil of digras region, yavatmal district, maharashtra (india): a case study,” Int. Educ. Res. J., vol. 10, no. 5, pp. 40–42, 2024.
[9] A. H. A. Hussein, K. A. Jabbar, A. Mohammed, and H. M. Al-Jawahry, “AI and IoT in Farming : A Sustainable Approach,” EDP Sci., vol. 491, 2024, no. International Conference on Environmental Development Using Computer Science, pp. 1–10, 2024.
[10] J. Li, Z. Wang, and M. Liu, “Why the same economic reform leads to different urbanization route? --The comparative study of China and India,” Elsevier, vol. 129, no. Land use policy, p. 106642, 2023, doi: 10.1016/j.landusepol.2023.106642.
[11] I. B.-R. Héctor, de la L. X. N.-R. María, P. G.-V. Francisco, Á.-B. Dioselina, and C.-B. Eloy, “Analyzing the Impact of Intensive Agriculture on Soil Quality : A Systematic Review and Global Meta-Analysis of Quality Indexes,” Agronomy, vol. 13, no. 8, p. 2166, 2023.
[12] P. Khaliq and F Budiman, “Optimization Accuracy Value of Agricultural Land Fertility Classification Using Soft Voting Method,” Sink. J. dan Penelit. Tek. Inform. Vol., vol. 8, no. 1, pp. 152–164, 2024.
[13] M. Sujatha and C. D. Jaidhar, “Machine learning-based approaches to enhance the soil fertility — A review,” Expert Syst. Appl., vol. 240, no. 122557, 2024, doi: 10.1016/j.eswa.2023.122557.
[14] R. Rahman and K. N. Das, “Artificial Intelligence and Machine Learning in Soil Analysis for Precision Agriculture : A Review,” J. Exp. Agric. Int., vol. 47, no. 5, pp. 511–524, 2025.
[15] Nurdin, S. Abadi, and Y. Afrillia, “Classification System for Soil Types Suitable for Food Crops using Naïve Bayes Method,” Sist. J. Sist. Inf., vol. 13, no. 3, pp. 1102–1113, 2024.
[16] A. Morchid, Z. Said, and H. Tairi, “Smart Agricultural Technology Innovative applications of internet of things and machine learning in sustainable agricultural irrigation management : Benefits and challenges,” Smart Agric. Technol., vol. 13, no. 101661, 2026, doi: 10.1016/j.atech.2025.101661.
[17] M. Walid, A. Susanto, and L. Farihin, “Sistem Smart Plant Care berbasis IoT untuk meningkatkan produktivitas pada Tanaman Padi,” STORAGE J. Ilm. Tek. dan Ilmu Komput., vol. 4, no. 3, pp. 166–174, 2025.
[18] N. Widanti, A. Alamsyah, A. Albus, A. N. Ikhsan, and S. W. Lestari, “Design Smart Farming in Rice Field for Monitoring Soil Fertility and Pest Rate Using Internet of Things,” J. Penelit. Pendidik. IPA, vol. 10, no. 8, pp. 5782–5788, 2024, doi: 10.29303/jppipa.v10i8.8288.
[19] S. Mansoor, S. Iqbal, and S. M. Popescu, “Integration of smart sensors and IOT in precision agriculture: trends, challenges and future prospectives,” Front. Plant Sci., vol. 16, no. 1587869, pp. 1–21, 2025, doi: 10.3389/fpls.2025.1587869.
[20] S. Dwivedy, A. K. Mahto, A. K. Sahoo, and C. Pradhan, “Soil Testing using IOT and Machine Learning Classifiers,” IEEE, vol. 4th Intern, no. Communication Control and Networking (ICAC3N), pp. 359–363, 2022, [Online]. Available: https://api.semanticscholar.org/CorpusID:257809570
[21] E. D. L. Chilito, J. F. C. Olaya, J. C. Corrales, and C. Figueroa, “Sustainability-driven fertilizer recommender system for co ee crops using case-based reasoning approach,” Front. Sustain. Food Syst., vol. 8, no. 1445795, 2025, doi: 10.3389/fsufs.2024.1445795.
[22] W. M. Vandana and B. Kavya, “Soil Fertility Assessment and Crop Recommendation for Sustainable Farming using Machine Learning and Deep Learning,” IEEE, vol. 2024 4th I, pp. 1–3, 2024, [Online]. Available: https://api.semanticscholar.org/CorpusID:269307388
[23] I. W. Widayat, A. A. Arsyad, A. J. Mantau, Y. Adhitya, and M. Köppen, “Fuzzy Methods in Smart Farming : A Systematic Review,” Informatica, vol. 36, no. 2, pp. 453–489, 2024.
[24] Y. Jang, S. Raza, M. Kim, and H. Choo, “Proactive Handover Decision for UAVs with Deep Reinforcement Learning,” Sensors, vol. 22, no. 3, p. 1200, 2022, [Online]. Available: https://doi.org/10.3390/s22031200
[25] X. Yan, R. Fu, T. Cheng, and S. Li, “A highly sensitive refractive index sensor based on a V-shaped photonic crystal fiber with a high refractive index range,” Sensors, vol. 21, no. 11, p. 3782, 2021.
[26] Y. Ma, H. Wang, J. Liu, R. Wang, and Z. Che, “Effects of Root Trace Nitrogen Reduction in Arid Areas on Sucrose Starch Metabolism and Yield of Flag Leaves and Grains of Spring Wheat under Drip Irrigation,” 2024, doi: 10.20944/preprints202401.0829.v1.
[27] L. Fan et al., “Surface plasmon resonance sensor based on core-shell Fe3O4@ SiO2@ Au nanoparticles amplification effect for detection of T-2 toxin,” Sensors, vol. 23, no. 6, p. 3078, 2023.
[28] A. Seth, “Attack and Anomaly Detection in IoT Sensors Using Machine Learning Approaches,” J. Recent Innov. Comput. Sci. Technol., vol. 2, no. 1, pp. 16–27, 2025, doi: 10.70454/JRICST.2025.20108.
[29] D. R. Whitman, S. L. Bennett, C. J. Wallace, and M. T. Richardson, “Synthetic Data Generation for Enhancing Anomaly Detection in Infrastructure Networks,” 2026.
[30] H. Delibašić-Marković, V. Petrović, and I. Petrović, “Implementation of a passive RC low-pass filter for embedded iot systems with open-source simulation support,” Orig. Sci. Pap., vol. Conference, pp. 505–513, 2025, doi: 10.63395/STEDConf14022025944D505.
[31] V. Kovtun, “Nonparametric data recovery in IoT sensor networks based on a priori density⋆,” 2025.
[32] S. F. Batubara et al., “Evaluasi status hara makro nitrogen, fosfor dan kalium di lahan sawah irigasi Kabupaten Deli Serdang, Sumatera Utara,” Agrikultura, vol. 35, no. 1, pp. 59–70, 2024.
[33] Hazelton, Pam, and B. Murphy, Interpreting soil test results: What do all the numbers mean? CSIRO publishing, 2025.
[34] G. A. GmbH, Anbar Agricultural Water Resources Management and Conservation Study. Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH, 2025. [Online]. Available: https://www.giz.de/sites/default/files/media/pkb-document/2025-11/giz2025-en-water-study.pdf
[35] M. N. Fadli, B. Ibrahim, and A. Robbo, “Evaluasi Kesesuaian Lahan pada Tanaman Padi (Oriza sativa L .) di Kecamatan Gantarang Kabupaten Bulukumba,” AGrotekMAS J. Indones. J. Ilmu Peranian, vol. 5, no. 3, pp. 244–254, 2024.
[36] F. P. Hutabarat et al., “Sistem Pakar Diagnosis Hama dan Penyakit pada Tanaman Padi menggunakan Metode Certainty Factor,” MEANS (Media Inf. Anal. dan Sist. p-ISSN, vol. 9, no. 1, pp. 7–14, 2024.
[37] V. Zuraida, D. Kusbianto, and M. R. Pahlevi, “Sistem Pakar Diagnosis Penyakit dan Hama pada Tanaman Padi dengan Metode Forward Chaining,” J. Minfo Polgan, vol. 12, no. 1, pp. 378–384, 2023.
[38] S. R. Fadila, “Aplikasi Ricezzy Untuk Menghitung Irigasi Tanaman Padi Menggunakan Metode Fuzzy Mamdani,” J. Tek. Inform., vol. 2, no. 1, pp. 1–8, 2022.
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