Utilization of NLP in Smart Agriculture Systems: Trends, Challenges, and Research Directions
Keywords:
NLP, Smart Farming, Text Analysis, Artificial Intelligence, Recommendation SystemsAbstract
The advancement of smart farming is characterized by the increasingly inclusive integration of technologies such as the Internet of Things (IoT), artificial intelligence, and data analytics to increase yields and maintain sustainability. Recent studies have demonstrated the effectiveness of this strategy in improving crop monitoring, supporting data-driven decision-making, and improving resource efficiency. Within this framework, Natural Language Processing (NLP) has begun to be used to analyze various textual data in agriculture, such as field reports, social media content, and agronomic knowledge, although comprehensive research on its functions remains limited. This study aims to identify patterns of NLP use in smart farming systems, analyze emerging barriers, and formulate future research directions. The method used is a systematic literature review, analyzing recent scientific publications from reputable databases. The analysis was conducted qualitatively on articles related to the topics of NLP, artificial intelligence, and smart farming in recent years. The research findings indicate that NLP has been used in various contexts, such as farmer sentiment analysis, information retrieval on plant diseases, the development of agricultural chatbots, and text-based recommendation systems. The integration of NLP with machine learning methods has been shown to improve the effectiveness of decision-making and knowledge management. However, several key challenges remain, such as limited datasets specific to the agricultural domain, variations in regional languages, and difficulties in integrating with IoT systems and sensors. Overall, this study found that NLP has significant potential to improve smart agricultural systems. However, its implementation still faces various obstacles, particularly those related to technical aspects and data availability. Therefore, future research should focus on creating a multilingual agricultural corpus, designing NLP models that are more responsive to domain characteristics, and improving integration with the AI-based agricultural technology ecosystem.
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[1] M. Abd. Mohammed, S. H. Jameel, A. J. Hussein, H. K. Ahmad, L. S. Ismail, and A. Zapryvoda, “The Transformation of Agriculture by Artificial Intelligence in Smart Farming,” in 2024 36th Conference of Open Innovations Association (FRUCT), IEEE, Oct. 2024, pp. 515–526. doi: 10.23919/FRUCT64283.2024.10749901.
[2] C. Giua, V. C. Materia, and L. Camanzi, “Smart farming technologies adoption: Which factors play a role in the digital transition?,” Technol. Soc., vol. 68, p. 101869, Feb. 2022, doi: 10.1016/j.techsoc.2022.101869.
[3] C. Parra-López, C. Carmona-Torres, R. Finger, K. Bronson, S.-L. Ruder, and L. Klerkx, “From smart farming to wise agricultural systems: A conceptual framework for systems-oriented sustainable digital transformation in agriculture,” Agric. Syst., vol. 234, p. 104669, Apr. 2026, doi: 10.1016/j.agsy.2026.104669.
[4] Md. N. Mowla, N. Mowla, A. F. M. S. Shah, K. M. Rabie, and T. Shongwe, “Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey,” IEEE Access, vol. 11, pp. 145813–145852, 2023, doi: 10.1109/ACCESS.2023.3346299.
[5] “Adoption of IoT in Agriculture - Systematic Review,” Applied Agriculture Sciences, vol. 1, no. 1, Feb. 2023, doi: 10.25163/agriculture.9676.
[6] S. Das and M. Ambika, “Revolutionizing Indian Farming: Machine Learning-Powered NLP for Optimal Crop Recommendations,” Procedia Comput. Sci., vol. 258, pp. 2040–2049, 2025, doi: 10.1016/j.procs.2025.04.454.
[7] C. Chathurya, D. Sachdeva, and M. Arora, “Agriculture Chatbot(AGRIBOT)Using Natural Language Processing,” in 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE, Jul. 2023, pp. 1–5. doi: 10.1109/ICCCNT56998.2023.10306512.
[8] S. Rezayi et al., “Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications,” IEEE Trans. Big Data, vol. 11, no. 3, pp. 1235–1246, Jun. 2025, doi: 10.1109/TBDATA.2024.3442542.
[9] S. Safiuddin and F. P. E. Putra, “Strategi Efisiensi Wireless Sensor Network (WSN),” INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics, vol. 8, no. 1, p. 52, Jul. 2023, doi: 10.51211/itbi.v8i1.2441.
[10] S. Yadav and A. Kaushik, “Comparative Study of Pre-trained Language Models for Text Classification in Smart Agriculture Domain,” 2023, pp. 267–279. doi: 10.1007/978-981-99-0981-0_21.
[11] S. Fountas, B. Espejo-García, A. Kasimati, M. Gemtou, H. Panoutsopoulos, and E. Anastasiou, “Agriculture 5.0: Cutting-Edge Technologies, Trends, and Challenges,” IT Prof., vol. 26, no. 1, pp. 40–47, Jan. 2024, doi: 10.1109/MITP.2024.3358972.
[12] Y. Kumar and V. Kumar, “Security in IoT systems using natural language processing: Future challenges and directions,” Internet Technology Letters, vol. 6, no. 4, Jul. 2023, doi: 10.1002/itl2.411.
[13] J. Polpinij, M. Kaenampornpan, C. S. G. Khoo, W.-N. Cheng, and B. Luaphol, “A Multi-Stage NLP Framework for Knowledge Discovery from Crop Disease Research Literature,” Mathematics, vol. 14, no. 2, p. 299, Jan. 2026, doi: 10.3390/math14020299.
[14] J. D. Rose, keerthana mani, R. Sulthana, and L. S. Ramesh, “Predicting Crop Disease Outbreak Based on Unstructured Farmer Complaint Text Using Nlp and Weak Supervision,” Feb. 04, 2026. doi: 10.21203/rs.3.rs-8581964/v1.
[15] R. Sapkota et al., “Multi-Modal LLMs in Agriculture: A Comprehensive Review,” IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 22510–22540, 2025, doi: 10.1109/TASE.2025.3612154.
[16] E. A. Nismi Mol and M. B. Santosh Kumar, “Review on knowledge extraction from text and scope in agriculture domain,” Artif. Intell. Rev., vol. 56, no. 5, pp. 4403–4445, May 2023, doi: 10.1007/s10462-022-10239-9.
[17] C. Vuppalapati, A. Ilapakurti, S. Vuppalapati, S. Kedari, S. Kedari, and J. Vuppalapati, “Edge AI and IoT-Driven SLMs: Enabling Multilingual Access and Sustainable Futures for Small and Subsistence Farmers,” in 2025 IEEE International Conference on Big Data (BigData), IEEE, Dec. 2025, pp. 6210–6219. doi: 10.1109/BigData66926.2025.11401575.
[18] F. P. E. Putra, R. W. Efendi, A. B. Tamam, and W. A. Pramadi, “Tren dan Praktik Terbaik dalam Pengembangan Web Berbasis API : Kajian Literatur terhadap Framework Laravel dan React,” Infomatek, vol. 27, no. 1, pp. 165–178, Jun. 2025, doi: 10.23969/infomatek.v27i1.25122.
[19] J. Kim, H.-U. Kim, J. Adamowski, S. Hatami, and H. Jeong, “Comparative study of term-weighting schemes for environmental big data using machine learning,” Environmental Modelling & Software, vol. 157, p. 105536, Nov. 2022, doi: 10.1016/j.envsoft.2022.105536.
[20] X. Duan, Y. Liu, Z. You, and Z. Li, “Agricultural Text Classification Method Based on ERNIE 2.0 and Multi-Feature Dynamic Fusion,” IEEE Access, vol. 13, pp. 52959–52971, 2025, doi: 10.1109/ACCESS.2025.3537277.
[21] L.-C. Chen, “An extended TF-IDF method for improving keyword extraction in traditional corpus-based research: An example of a climate change corpus,” Data Knowl. Eng., vol. 153, p. 102322, Sep. 2024, doi: 10.1016/j.datak.2024.102322.
[22] F. P. E. Putra, U. Ubaidi, A. Hamzah, W. A. Pramadi, and A. Nuraini, “Systematic Literature Review: Security Gap Detection On Websites Using Owasp Zap,” Brilliance: Research of Artificial Intelligence, vol. 4, no. 1, pp. 348–355, Jul. 2024, doi: 10.47709/brilliance.v4i1.4227.
[23] Y. Y. N. Reddy, T. Narayanan, and A. Balaji, “Transformer based embedding in query-response generation system for agriculture,” Comput. Electron. Agric., vol. 244, p. 111406, Mar. 2026, doi: 10.1016/j.compag.2026.111406.
[24] S. Bhattacharya and M. Pandey, “Developing an agriculture ontology for extracting relationships from texts using Natural Language Processing to enhance semantic understanding,” International Journal of Information Technology, Mar. 2024, doi: 10.1007/s41870-024-01809-x.
[25] N. M. Gardazi, A. Daud, M. K. Malik, A. Bukhari, T. Alsahfi, and B. Alshemaimri, “BERT applications in natural language processing: a review,” Artif. Intell. Rev., vol. 58, no. 6, p. 166, Mar. 2025, doi: 10.1007/s10462-025-11162-5.
[26] N. L and R. Kumar. J. S, “BERT-Based Agricultural Crop Prediction System Using Soil and Weather Parameters with Interactive Streamlit Deployment,” in 2025 4th International Conference on Applied Artificial Intelligence and Computing (ICAAIC), IEEE, Dec. 2025, pp. 529–535. doi: 10.1109/ICAAIC64647.2025.11330288.
[27] A. Onan, “Hierarchical graph-based text classification framework with contextual node embedding and BERT-based dynamic fusion,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 7, p. 101610, Jul. 2023, doi: 10.1016/j.jksuci.2023.101610.
[28] J. Mutinda, W. Mwangi, and G. Okeyo, “Sentiment Analysis of Text Reviews Using Lexicon-Enhanced Bert Embedding (LeBERT) Model with Convolutional Neural Network,” Applied Sciences, vol. 13, no. 3, p. 1445, Jan. 2023, doi: 10.3390/app13031445.
[29] H. Panoutsopoulos, B. Espejo-Garcia, S. Raaijmakers, X. Wang, S. Fountas, and C. Brewster, “Investigating the effect of different fine-tuning configuration scenarios on agricultural term extraction using BERT,” Comput. Electron. Agric., vol. 225, p. 109268, Oct. 2024, doi: 10.1016/j.compag.2024.109268.
[30] S. Yang et al., “ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources,” 2025, pp. 284–298. doi: 10.1007/978-981-96-0573-6_21.
[31] M. Awais, A. H. Salem Abdulla Alharthi, A. Kumar, H. Cholakkal, and R. M. Anwer, “AgroGPT : Efficient Agricultural Vision-Language Model with Expert Tuning,” in 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, Feb. 2025, pp. 5687–5696. doi: 10.1109/WACV61041.2025.00555.
[32] V. C. S.S., A. H. S., and G. F. Albaaji, “Precision farming for sustainability: An agricultural intelligence model,” Comput. Electron. Agric., vol. 226, p. 109386, Nov. 2024, doi: 10.1016/j.compag.2024.109386.
[33] P. Suebsombut, P. Sureephong, A. Sekhari, S. Chernbumroong, and A. Bouras, “Chatbot Application to Support Smart Agriculture in Thailand,” in 2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), IEEE, Jan. 2022, pp. 364–367. doi: 10.1109/ECTIDAMTNCON53731.2022.9720318.
[34] S. S. Patil et al., “Agriassist - Smart Agriculture Chatbot,” in 2025 2nd International Conference on Integration of Computational Intelligent System (ICICIS), IEEE, Sep. 2025, pp. 1–6. doi: 10.1109/ICICIS65613.2025.11371051.
[35] J. Rizki, “Social Media Sentiment Analysis to Understand Agricultural Market Trends and Consumer Preferences,” Jurnal Minfo Polgan, vol. 12, no. 2, pp. 1734–1743, Sep. 2023, doi: 10.33395/jmp.v12i2.12970.
[36] Z. Guo, X. Chen, and Y. Zhang, “Impact of environmental regulation perception on farmers’ agricultural green production technology adoption: A new perspective of social capital,” Technol. Soc., vol. 71, p. 102085, Nov. 2022, doi: 10.1016/j.techsoc.2022.102085.
[37] D. Garg and M. Alam, “Smart agriculture: a literature review,” Journal of Management Analytics, vol. 10, no. 2, pp. 359–415, Apr. 2023, doi: 10.1080/23270012.2023.2207184.
[38] F. P. E. Putra, U. Ubaidi, R. N. Saputra, F. M. Haris, and S. N. R. Barokah, “Application of Internet of Things Technology in Monitoring Water Quality in Fishponds,” Brilliance: Research of Artificial Intelligence, vol. 4, no. 1, pp. 356–361, Jul. 2024, doi: 10.47709/brilliance.v4i1.4231.
[39] F. Prasetyo Eka Putra, S. R. Sutarsih, S. Sofiyulloh, P. Permana, and M. Umar Mansyur, “OPTIMALISASI PERANCANGAN APLIKASI MANAJEMEN DATA KOLOMAN, DI DESA PULAU MANDANGIN SAMPANG – MADURA BERBASIS WEBSITE,” Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, vol. 9, no. 2, pp. 285–294, Jul. 2024, doi: 10.36341/rabit.v9i2.4840.
[40] V. Vanitha, N. Rajathi, and K. Prakash Kumar, “AI-Based Agriculture Recommendation System for Farmers,” 2023, pp. 91–103. doi: 10.1007/978-981-99-3754-7_7.
[41] P. Chavula, F. Kayusi, G. Lungu, H. Mambwe, and A. Uwimbabazi, “AI Application in Climate-Smart Agricultural Technologies: A Synthesis Study,” LatIA, vol. 2, p. 330, Nov. 2024, doi: 10.62486/latia2025330.
[42] F. P. Eka Putra, I. N. Sudana Degeng, S. Ulfa, and W. Kamdi, “The Evolution of Quality Education: Impacts and Challenges of Using Open Educational Resources (OER) and Open Educational Practices (OEP) in the Conceive - Design - Implement - Operate (CDIO) Framework,” TEM Journal, pp. 386–395, Feb. 2024, doi: 10.18421/TEM131-40.
[43] A. Ali, M. Khan, K. Khan, R. Ullah Khan, and A. Aloraini, “Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning,” Computers, Materials & Continua, vol. 79, no. 1, pp. 713–733, 2024, doi: 10.32604/cmc.2024.048712.
[44] F. P. E. Putra, Moh. Irfan, M. Aziz, and R. N. Saputra, “Wireless Network Design at Pamekasan Regency Public Library,” Brilliance: Research of Artificial Intelligence, vol. 5, no. 1, pp. 144–150, May 2025, doi: 10.47709/brilliance.v5i1.5876.
[45] Y. Akkem, S. K. Biswas, and A. Varanasi, “AI-Driven smart farming portal: overcoming language barriers and enhancing agricultural productivity through machine learning,” Engineering Research Express, vol. 7, no. 2, p. 025288, Jun. 2025, doi: 10.1088/2631-8695/ade19d.
[46] H. A. Tahir, W. Alayed, and W. U. Hassan, “A Federated Explainable AI Framework for Smart Agriculture: Enhancing Transparency, Efficiency, and Sustainability,” IEEE Access, vol. 13, pp. 97567–97584, 2025, doi: 10.1109/ACCESS.2025.3571340.
[47] F. P. E. Putra, D. A. M. Putra, A. Firdaus, and A. Hamzah, “Analisis Kecepatan Dan Kinerja Jaringan 5G (generasi ke 5) Pada Wilayah Perkotaan,” INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics, vol. 8, no. 1, p. 47, Jul. 2023, doi: 10.51211/itbi.v8i1.2439.
[48] S. Sharma, A. Malik, C. Sharma, I. Batra, M. S. Kaswan, and J. A. Garza-Reyes, “Adoption of industry 4.0 in different sectors: a structural review using natural language processing,” International Journal on Interactive Design and Manufacturing (IJIDeM), vol. 18, no. 8, pp. 6069–6091, Oct. 2024, doi: 10.1007/s12008-023-01550-y.
[49] W. Bandara and R. Syed, “The role of a protocol in a systematic literature review,” J. Decis. Syst., vol. 33, no. 4, pp. 583–600, Oct. 2024, doi: 10.1080/12460125.2023.2217567.
[50] F. L. da Silva, B. K. Slodkowski, K. K. A. da Silva, and S. C. Cazella, “A systematic literature review on educational recommender systems for teaching and learning: research trends, limitations and opportunities,” Educ. Inf. Technol. (Dordr)., vol. 28, no. 3, pp. 3289–3328, Mar. 2023, doi: 10.1007/s10639-022-11341-9.
[51] Travis Satnarine, “Systematic Review Methodology: Conducting High-Quality Reviews and Understanding Their Significance in Evidence-Based Practice,” Journal For International Medical Graduates, vol. 2, no. 1, May 2023, doi: 10.56570/jimgs.v2i1.76.
[52] L. Alabood, Z. Aminolroaya, D. Yim, O. Addam, and F. Maurer, “A systematic literature review of the Design Critique method,” Inf. Softw. Technol., vol. 153, p. 107081, Jan. 2023, doi: 10.1016/j.infsof.2022.107081.
[53] D. Fan, D. Breslin, J. L. Callahan, and M. Iszatt‐White, “Advancing literature review methodology through rigour, generativity, scope and transparency,” International Journal of Management Reviews, vol. 24, no. 2, pp. 171–180, Apr. 2022, doi: 10.1111/ijmr.12291.
[54] F. P. E. Putra, R. W. Efendi, A. B. Tamam, and W. A. Pramadi, “Trends and Best Practices in API-Based Web Development Using Laravel and React,” Brilliance: Research of Artificial Intelligence, vol. 5, no. 1, pp. 223–233, Jun. 2025, doi: 10.47709/brilliance.v5i1.5971.
[55] A. K. Mannarath, “Systematic literature review for an effective research: a structured framework for social science researches,” Qual. Quant., vol. 60, no. 2, pp. 4773–4799, Nov. 2025, doi: 10.1007/s11135-025-02458-3.
[56] J. N. de Alencar Neto, M. K. Scheffer, B. P. Correia, K. G. Franchini, S. P. Felicioni, and M. F. N. De Marchi, “Systematic review and meta-analysis of diagnostic test accuracy of ST-segment elevation for acute coronary occlusion,” Int. J. Cardiol., vol. 402, p. 131889, May 2024, doi: 10.1016/j.ijcard.2024.131889.
[57] Z. M. Amin, N. Anwar, M. S. Mohd Shoid, and S. Samuri, “Method for Conducting Systematic Literature Review (SLR) for Cyber Risk Assessment,” Environment-Behaviour Proceedings Journal, vol. 7, no. SI10, pp. 255–260, Nov. 2022, doi: 10.21834/ebpj.v7iSI10.4130.
[58] F. Horgan, N. Kenny, and P. Flynn, “A systematic review of the experiences of autistic young people enrolled in mainstream second-level (post-primary) schools,” Autism, vol. 27, no. 2, pp. 526–538, Feb. 2023, doi: 10.1177/13623613221105089.
[59] S. Bedi et al., “Testing and Evaluation of Health Care Applications of Large Language Models,” JAMA, vol. 333, no. 4, p. 319, Jan. 2025, doi: 10.1001/jama.2024.21700.
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