{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T15:55:11Z","timestamp":1787932511232,"version":"build-2784847793"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032213235","type":"print"},{"value":"9783032213242","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-21324-2_37","type":"book-chapter","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T08:39:43Z","timestamp":1774255183000},"page":"489-504","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AgriIR: A Scalable Framework for\u00a0Domain-Specific Knowledge Retrieval"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0714-569X","authenticated-orcid":false,"given":"Shuvam","family":"Banerji Seal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6262-4103","authenticated-orcid":false,"given":"Aheli","family":"Poddar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7472-0812","authenticated-orcid":false,"given":"Alok","family":"Mishra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5962-5983","authenticated-orcid":false,"given":"Dwaipayan","family":"Roy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,24]]},"reference":[{"key":"37_CR1","unstructured":"Adhikary, S., Banerji\u00a0Seal, S., Sar, S., Roy, D.: IISERK@ToT_2024: query reformulation and layered retrieval for tip-of-tongue items. In: Proceedings of the Thirty-Third Text REtrieval Conference (TREC 2024). National Institute of Standards and Technology (NIST) (2024). https:\/\/trec.nist.gov\/pubs\/trec33\/papers\/IISER-K.tot.pdf"},{"key":"37_CR2","unstructured":"Almazrouei, E., et al.: The Falcon series of open language models (2023). https:\/\/arxiv.org\/abs\/2311.16867"},{"key":"37_CR3","doi-asserted-by":"publisher","unstructured":"Bender, E.M., Gebru, T., McMillan-Major, A., Shmitchell, S.: On the dangers of stochastic parrots: can language models be too big? In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT \u201921, pp. 610\u2013623. Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3442188.3445922","DOI":"10.1145\/3442188.3445922"},{"key":"37_CR4","doi-asserted-by":"publisher","unstructured":"Bernard, N., Balog, K.: A systematic review of fairness, accountability, transparency, and ethics in information retrieval. ACM Comput. Surv. 57(6) (2025). https:\/\/doi.org\/10.1145\/3637211","DOI":"10.1145\/3637211"},{"key":"37_CR5","unstructured":"Bommasani, R., et\u00a0al.: On the opportunities and risks of foundation models. arXiv (2021). https:\/\/crfm.stanford.edu\/assets\/report.pdf"},{"key":"37_CR6","doi-asserted-by":"publisher","unstructured":"Cabrera, M.V., Johnstone, M., Hayward, J., Bolton, K.A., Creighton, D.: Integration of large-scale community-developed causal loop diagrams: a natural language processing approach to merging factors based on semantic similarity. BMC Pub. Health 25(1), 923 (2025). https:\/\/doi.org\/10.1186\/s12889-025-22142-3","DOI":"10.1186\/s12889-025-22142-3"},{"key":"37_CR7","doi-asserted-by":"publisher","unstructured":"Chen, Y., Kuang, J., Cheng, D., Zheng, J., Gao, M., Zhou, A.: AgriKG: an agricultural knowledge graph and its applications. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) Database Systems for Advanced Applications, pp. 533\u2013537. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-18590-9_81","DOI":"10.1007\/978-3-030-18590-9_81"},{"key":"37_CR8","doi-asserted-by":"publisher","unstructured":"Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measure. 20(1), 37\u201346 (1960). https:\/\/doi.org\/10.1177\/001316446002000104","DOI":"10.1177\/001316446002000104"},{"key":"37_CR9","unstructured":"Department of Animal Husbandry and Dairying: Annual report 2022\u201323. Tech. rep., Ministry of Fisheries, Animal Husbandry and Dairying, Government of India (2023). https:\/\/dahd.gov.in\/sites\/default\/files\/2023-06\/FINALREPORT2023ENGLISH.pdf"},{"key":"37_CR10","unstructured":"Douze, M., et al.: The Faiss library (2024)"},{"key":"37_CR11","unstructured":"Food and Agriculture Organization of the United Nations: Employment indicators 2000\u20132023 (July 2025 update). FAOSTAT Highlights Archive (2025). https:\/\/www.fao.org\/statistics\/highlights-archive\/highlights-detail\/employment-indicators-2000-2023-%28july-2025-update%29\/en. Accessed 27 Oct 2025"},{"key":"37_CR12","doi-asserted-by":"publisher","unstructured":"Gessesse, A.A., Melesse, A.M.: Temporal relationships between time series CHIRPS-rainfall estimation and eMODIS-NDVI satellite images in Amhara region, Ethiopia, Chap. 8. In: Melesse, A.M., Abtew, W., Senay, G. (eds.) Extreme Hydrology and Climate Variability, pp. 81\u201392. Elsevier (2019). https:\/\/doi.org\/10.1016\/B978-0-12-815998-9.00008-7, https:\/\/www.sciencedirect.com\/science\/article\/pii\/B9780128159989000087","DOI":"10.1016\/B978-0-12-815998-9.00008-7"},{"key":"37_CR13","unstructured":"Grattafiori, A., et\u00a0al., A.D.: The Llama 3 herd of models (2024). https:\/\/arxiv.org\/abs\/2407.21783"},{"key":"37_CR14","unstructured":"Hugging Face: Massive text embedding benchmark (MTEB) leaderboard (2025). https:\/\/huggingface.co\/spaces\/mteb\/leaderboard. Accessed 27 Oct 2025"},{"key":"37_CR15","unstructured":"ICAR: Indian council of agricultural research. Official Website (2023). https:\/\/icar.org.in\/. Accessed 27 Oct 2025"},{"key":"37_CR16","doi-asserted-by":"publisher","unstructured":"Izacard, G., Grave, E.: Leveraging passage retrieval with generative models for open domain question answering. In: Merlo, P., Tiedemann, J., Tsarfaty, R. (eds.) Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Online, April 2021, pp. 874\u2013880. Association for Computational Linguistics (201). https:\/\/doi.org\/10.18653\/v1\/2021.eacl-main.74, https:\/\/aclanthology.org\/2021.eacl-main.74\/","DOI":"10.18653\/v1\/2021.eacl-main.74"},{"key":"37_CR17","doi-asserted-by":"publisher","unstructured":"Ji, Z., et al.: Survey of hallucination in natural language generation. ACM Comput. Surv. 55(12) (2023). https:\/\/doi.org\/10.1145\/3571730","DOI":"10.1145\/3571730"},{"key":"37_CR18","unstructured":"Jiang, A.Q., et al.: Mistral 7B (2023). https:\/\/arxiv.org\/abs\/2310.06825"},{"issue":"3","key":"37_CR19","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1109\/TBDATA.2019.2921572","volume":"7","author":"J Johnson","year":"2019","unstructured":"Johnson, J., Douze, M., J\u00e9gou, H.: Billion-scale similarity search with GPUs. IEEE Trans. Big Data 7(3), 535\u2013547 (2019)","journal-title":"IEEE Trans. Big Data"},{"key":"37_CR20","doi-asserted-by":"publisher","unstructured":"Katharria, A., Pant, M., Vel\u00e1squez, J.D., Sn\u00e1\u0161el, V., Rajwar, K., Deep, K.: Information fusion in smart agriculture: machine learning applications and future research directions (2026). https:\/\/doi.org\/10.1016\/j.inffus.2025.104040, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253525011029","DOI":"10.1016\/j.inffus.2025.104040"},{"issue":"4","key":"37_CR21","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1007\/s00799-023-00369-y","volume":"25","author":"B Koopman","year":"2023","unstructured":"Koopman, B., et al.: AgAsk: an agent to help answer farmer\u2019s questions from scientific documents. Int. J. Digit. Libr. 25(4), 569\u2013584 (2023). https:\/\/doi.org\/10.1007\/s00799-023-00369-y","journal-title":"Int. J. Digit. Libr."},{"key":"37_CR22","doi-asserted-by":"publisher","unstructured":"Kuska, M.T., Wahabzada, M., Paulus, S.: AI for crop production \u2013 where can large language models (LLMs) provide substantial value? Comput. Electron. Agric. 221, 108924 (2024). https:\/\/doi.org\/10.1016\/j.compag.2024.108924, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0168169924003156","DOI":"10.1016\/j.compag.2024.108924"},{"key":"37_CR23","unstructured":"Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol.\u00a033, pp. 9459\u20139474. Curran Associates, Inc. (2020). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/6b493230205f780e1bc26945df7481e5-Paper.pdf"},{"key":"37_CR24","unstructured":"Lin, X., et al.: LLM-based agents suffer from hallucinations: a survey of taxonomy, methods, and directions (2025). https:\/\/arxiv.org\/abs\/2509.18970"},{"key":"37_CR25","doi-asserted-by":"publisher","unstructured":"Macdonald, C., Tonellotto, N., MacAvaney, S., Ounis, I.: PyTerrier: declarative experimentation in Python from BM25 to dense retrieval. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 4526\u20134533. CIKM \u201921, Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3459637.3482013","DOI":"10.1145\/3459637.3482013"},{"key":"37_CR26","doi-asserted-by":"publisher","unstructured":"Mai, G., et al.: On the opportunities and challenges of foundation models for GeoAI (vision paper). ACM Trans. Spat. Algorithms Syst. 10(2) (2024). https:\/\/doi.org\/10.1145\/3653070","DOI":"10.1145\/3653070"},{"key":"37_CR27","unstructured":"Maliakel, P.J., Ilager, S., Brandic, I.: Investigating energy efficiency and performance trade-offs in LLM inference across tasks and DVFS settings (2025). https:\/\/arxiv.org\/abs\/2501.08219"},{"key":"37_CR28","doi-asserted-by":"publisher","unstructured":"Mitchell, M., et al.: Model cards for model reporting. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* \u201919, pp. 220\u2013229. Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3287560.3287596","DOI":"10.1145\/3287560.3287596"},{"key":"37_CR29","unstructured":"Mustofa, S., Munna, M.M.H., Emon, Y.R., Rabbany, G., Ahad, M.T.: A comprehensive review on plant leaf disease detection using deep learning (2023). https:\/\/arxiv.org\/abs\/2308.14087"},{"key":"37_CR30","doi-asserted-by":"publisher","unstructured":"Olteanu, A., et al.: FACTS-IR: fairness, accountability, confidentiality, transparency, and safety in information retrieval. SIGIR Forum 53(2), 20\u201343 (2021). https:\/\/doi.org\/10.1145\/3458553.3458556","DOI":"10.1145\/3458553.3458556"},{"key":"37_CR31","unstructured":"Peng, R., Liu, K., Yang, P., Yuan, Z., Li, S.: Embedding-based retrieval with LLM for effective agriculture information extracting from unstructured data (2023). https:\/\/arxiv.org\/abs\/2308.03107"},{"key":"37_CR32","doi-asserted-by":"publisher","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, November 2019, pp. 3982\u20133992. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1410, https:\/\/aclanthology.org\/D19-1410\/","DOI":"10.18653\/v1\/D19-1410"},{"key":"37_CR33","doi-asserted-by":"publisher","unstructured":"Reimers, N., Gurevych, I.: Making monolingual sentence embeddings multilingual using knowledge distillation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, November 2020, pp. 4512\u20134525. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.365, https:\/\/aclanthology.org\/2020.emnlp-main.365\/","DOI":"10.18653\/v1\/2020.emnlp-main.365"},{"key":"37_CR34","doi-asserted-by":"crossref","unstructured":"Rivest, R.: The MD5 message-digest algorithm (RFC1321) (1992). http:\/\/www.ietf.org\/rfc\/rfc1321.txt","DOI":"10.17487\/rfc1321"},{"key":"37_CR35","doi-asserted-by":"crossref","unstructured":"Salvador, A., et al.: Learning cross-modal embeddings for cooking recipes and food images. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017. IEEE (2017)","DOI":"10.1109\/CVPR.2017.327"},{"key":"37_CR36","unstructured":"Samuel, D.J., Skarga-Bandurova, I., Sikolia, D., Awais, M.: AgroLLM: connecting farmers and agricultural practices through large language models for enhanced knowledge transfer and practical application (2025). https:\/\/arxiv.org\/abs\/2503.04788"},{"key":"37_CR37","doi-asserted-by":"publisher","unstructured":"Shaikh, T.A., Rasool, T., Veningston, K., Yaseen, S.M.: The role of large language models in agriculture: harvesting the future with LLM intelligence. Prog. Artif. Intell. 14(2), 117\u2013164 (2025). https:\/\/doi.org\/10.1007\/s13748-024-00359-4","DOI":"10.1007\/s13748-024-00359-4"},{"key":"37_CR38","doi-asserted-by":"publisher","unstructured":"Strubell, E., Ganesh, A., McCallum, A.: Energy and policy considerations for deep learning in NLP. In: Korhonen, A., Traum, D., M\u00e0rquez, L. (eds.) Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, July 2019, pp. 3645\u20133650. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/P19-1355, https:\/\/aclanthology.org\/P19-1355\/","DOI":"10.18653\/v1\/P19-1355"},{"key":"37_CR39","unstructured":"Gemma Team: Gemma 3 technical report (2025). https:\/\/arxiv.org\/abs\/2503.19786"},{"key":"37_CR40","unstructured":"Ollama Team: Ollama: an open source framework for running and serving large language models locally (2023). https:\/\/github.com\/ollama\/ollama, version latest. Accessed 27 Oct 2025"},{"key":"37_CR41","unstructured":"Thakur, N., Reimers, N., R\u00fcckl\u00e9, A., Srivastava, A., Gurevych, I.: BEIR: a heterogeneous benchmark for zero-shot evaluation of information retrieval models. In: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) (2021). https:\/\/openreview.net\/forum?id=wCu6T5xFjeJ"},{"key":"37_CR42","doi-asserted-by":"publisher","unstructured":"Upadhyay, A., et al.: Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture. Artif. Intell. Rev. 58(3), 92 (2025). https:\/\/doi.org\/10.1007\/s10462-024-11100-x","DOI":"10.1007\/s10462-024-11100-x"},{"key":"37_CR43","unstructured":"Wilson, S., Ginige, A., Goonatilake, J.: Design science research approach for ontology development in agriculture: utilising advances of LLM for automated entity extraction. In: Proceedings of the Australasian Conference on Information Systems (ACIS 2024). No.\u00a0150. Association for Information Systems (2024). aCIS 2024 Proceedings, Paper 150. https:\/\/aisel.aisnet.org\/acis2024\/150"},{"key":"37_CR44","doi-asserted-by":"publisher","unstructured":"Yang, S., et al.: ShizishanGPT: an agricultural large language model integrating tools and resources (2024). https:\/\/doi.org\/10.1007\/978-981-96-0573-6_21","DOI":"10.1007\/978-981-96-0573-6_21"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-21324-2_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T23:13:15Z","timestamp":1774307595000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-21324-2_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032213235","9783032213242"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-21324-2_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"24 March 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests that influenced the research, authorship, or publication of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Delft","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 March 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"48","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2026.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}