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Vol. 11, No. 1, February 2026 (Article in Progress)

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Enhancing Plant Recommendation through IoT-integrated LLM Systems

https://doi.org/10.22219/kinetik.v11i1.2241
Panji Maulana
President University
Cutifa Safitri
President University

Corresponding Author(s) : Cutifa Safitri

cutifa@ieee.org

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 11, No. 1, February 2026 (Article in Progress)

Abstract

Over the past decade, artificial intelligence has experienced phenomenally rapid and extensive expansion across a variety of industries. Along with developments over time, the agricultural sector stands to benefit significantly from the integration of technology. A significant challenge encountered by farmers is selecting the appropriate crop to plant. The selection of crops is influenced by various factors. Despite advancements in agricultural technology, a considerable gap remains in the integration of IoT with large language models (LLM) for delivering context-specific and data-driven plant recommendation. This study evaluates the reliability of plant recommendations produced by Internet of Things (IoT) devices utilizing the Llama 3.2 model. The model will utilize real-time environmental data, including soil pH, altitude, and temperature, to recommend appropriate plant. The recommendations will be compared between base model and fine tune model using precision, recall and f1 metrics and be assessed in relation to established agricultural literature concerning plant compatibility and growth requirements with human evaluation. This research achieved an AUC value that exceeded that of the base model by 10%, Precision exhibited a 25% increase relative to the base model, while recall demonstrated a significant rise of 52% from the base model. The F1 score also improved by 39% compared to the base model.

Keywords

LLaMA Large Language Model Plant Recommendation Prompt Engineering TALLRec
Maulana, P., & Cutifa Safitri. (2026). Enhancing Plant Recommendation through IoT-integrated LLM Systems . Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 11(1). https://doi.org/10.22219/kinetik.v11i1.2241
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References
  1. A. Usardi and B. Drut, “Will Artificial Intelligence increase economic growth? Economy & Markets,” Jan. 2024.
  2. A. A. AlZubi and K. Galyna, “Artificial Intelligence and Internet of Things for Sustainable Farming and Smart Agriculture,” IEEE Access, vol. 11, pp. 78686–78692, 2023. https://doi.org/10.1109/ACCESS.2023.3298215
  3. K. Archana and Dr.K.G.Saranya, “Crop Yield Prediction, Forecasting and Fertilizer Recommendation using Voting Based Ensemble Classifier,” SSRG International Journal of Computer Science and Engineering, vol. 7, no. 5, pp. 1-4, 2020. https://doi.org/10.14445/23488387/IJCSE-V7I5P101
  4. T. Silveira, M. Zhang, X. Lin, Y. Liu, and S. Ma, “How good your recommender system is? A survey on evaluations in recommendation,” International Journal of Machine Learning and Cybernetics, vol. 10, no. 5, pp. 813–831, 2019. https://doi.org/10.1007/s13042-017-0762-9
  5. P. V. Reddy, K. N. Prasad, and C. Puttamadappa, “Farmer’s friend: Conversational AI bot for smart agriculture,” Journal of Positive School Psychology, vol. 6, no. 2, pp. 2541–2549, 2022.
  6. Z. Zhao et al., “Recommender Systems in the Era of Large Language Models (LLMs),” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 11, pp. 6889-6907, Nov. 2024. https://doi.org/10.48550/arXiv.2307.02046
  7. R. Gunawan, T. Andhika, S. Sandi, and F. Hibatulloh, “Monitoring System for Soil Moisture, Temperature, pH and Automatic Watering of Tomato Plants Based on Internet of Things,” Telekontran : Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan, vol. 7, no. 1, pp. 66–78, Apr. 2019. https://doi.org/10.34010/telekontran.v7i1.1640
  8. H. Husdi and Y. Lasena, “Real Time Analisys Berbasis Internet Of Things Untuk Prediksi Iklim Lahan Pertanian,” Jurnal Media Informatika Budidarma, vol. 4, no. 3, pp. 834-840, 2020.
  9. Y. Setiawan, H. Tanudjaja, and S. Octaviani, “Penggunaan Internet of Things (IoT) untuk Pemantauan dan Pengendalian Sistem Hidroponik,” TESLA, vol. 20, no. 2, pp. 175–182, Feb. 2019. https://doi.org/10.24912/tesla.v20i2.2994
  10. J. Li et al., “Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges,” Computers and Electronics in Agriculture, vol. 222, no. 3, pp. 1-18, Jul. 2024. https://doi.org/10.1016/j.compag.2024.109032
  11. M. T. Kuska, M. Wahabzada, and S. Paulus, “AI for crop production – Where can large language models (LLMs) provide substantial value?,” Comput Electron Agric, vol. 221, p. 108924, Jun. 2024. https://doi.org/10.1016/J.COMPAG.2024.108924
  12. B. Zhao, W. Jin, J. Del Ser, and G. Yang, “ChatAgri: Exploring potentials of ChatGPT on cross-linguistic agricultural text classification,” Neurocomputing, vol. 557, p. 126708, Nov. 2023. https://doi.org/10.1016/J.NEUCOM.2023.126708
  13. J. Park and S. Choi, “LLMs for Enhanced Agricultural Meteorological Recommendations,” arXiv preprint arXiv:2408.04640, Jul. 2024. https://doi.org/10.48550/arXiv.2408.04640
  14. E. Johnson and N. Wilson, “Enhancing Agricultural Machinery Management through Advanced LLM Integration,” arXiv preprint arXiv:2407.20588, Jul, 2024. https://doi.org/10.48550/arXiv.2407.20588
  15. M. Tomar, A. Tiwari, T. Saha, P. Jha, and S. Saha, “An EcoSage Assistant: Towards Building A Multimodal Plant Care Dialogue Assistant,” In European Conference on Information Retrieval, pp. 318-332, 2024. https://doi.org/10.1007/978-3-031-56060-6_21
  16. S. Rezayi et al., “AgriBERT: Knowledge-Infused Agricultural Language Models for Matching Food and Nutrition,” Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, pp. 5150–5156, Jul. 2022. https://doi.org/10.24963/ijcai.2022/715
  17. X. Yang, J. Gao, W. Xue, and E. Alexandersson, “PLLaMa: An Open-source Large Language Model for Plant Science,” arXiv preprint arXiv:2401.01600, Jan. 2024. https://doi.org/10.48550/arXiv.2401.01600
  18. K. Bao, J. Zhang, Y. Zhang, W. Wang, F. Feng, and X. He, “TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation,” in Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Association for Computing Machinery, Inc, Sep. 2023, pp. 1007–1014. https://doi.org/10.1145/3604915.3608857
  19. A. Vaswani et al., “Attention Is All You Need,” Advances in Neural Information Processing Systems, Jun. 2017. https://doi.org/10.48550/arXiv.1706.03762
  20. J. Alammar and M. Grootendorst, “Hands-On Large Language Models: Language Understanding and Generation,” O’Reilly Media, Inc., 2024.
  21. H. Naveed et al., “A Comprehensive Overview of Large Language Models,” arXiv preprint arXiv:2307.06435, 2024. https://doi.org/10.48550/arXiv.2307.06435
  22. L. Team and A. @ Meta, “The Llama 3 Herd of Models,” arXiv preprint arXiv:2407.21783, 2024. https://doi.org/10.48550/arXiv.2407.21783
  23. I. John, “The Art of Asking ChatGPT for High-Quality Answers A Complete Guide to Prompt Engineering Techniques,” Nzunda Technologies Ltd., 2023.
  24. E. J. Hu et al., “LoRA: Low-Rank Adaptation of Large Language Models,” arXiv preprint arXiv:2106.09685, Jun. 2021. https://doi.org/10.48550/arXiv.2106.09685
  25. P. Fränti and R. Mariescu-Istodor, “Soft precision and recall,” Pattern Recognit Lett, vol. 167, pp. 115–121, Mar. 2023. https://doi.org/10.1016/j.patrec.2023.02.005
  26. A. P. Bradley, “The use of the area under the ROC curve in the evaluation of machine learning algorithms,” Pattern Recognit, vol. 30, no. 7, pp. 1145–1159, 1997. https://doi.org/10.1016/S0031-3203(96)00142-2
  27. C. Saparinto and R. Susiana, “Grow Your Own Fruits, Panduan Praktis Menanam 28 Tanaman Buah Populer di Pekarangan” Penerbit Andi, 2024.
  28. V. C. Tjokro and S. A. Sanjaya, “Methods and Applications of Fine-Tuning Llama-2 and Llama-Based Models: A Systematic Literature Analysis,” Journal of System and Management Sciences, vol. 14, no. 10, pp. 254-266, Jun. 2024. https://doi.org/10.33168/jsms.2024.1015
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