| Titre : | AI for crop production - Where can large language models (LLMs) provide substantial value? (2024) |
| Auteurs : | M.T. Kuska ; M. Wahabzada ; S. Paulus |
| Type de document : | Article |
| Dans : | Computers and Electronics in Agriculture (vol. 221, June 2024) |
| Article en page(s) : | p. 108924 |
| Langues : | Anglais |
| Langues du résumé : | Anglais |
| Catégories : |
Catégories principales 06 - AGRICULTURE. FORÊTS. PÊCHES ; 6.4 - Production Agricole. Système de ProductionThésaurus IAMM SYSTEME DE PRODUCTION ; INTELLIGENCE ARTIFICIELLE ; PRODUCTION AGRICOLE |
| Résumé : |
Since the launch of the “Generative Pre-trained Transformer 3.5”, ChatGPT by Open, artificial intelligence (AI) has been a main discussion topic in public. Especially large language models (LLM), so called “intelligent” chatbots, and the possibility to automatically generate highly professional technical texts get high attention. Companies, as well as researchers, are evaluating possible applications and how such a powerful LLM can be integrated into daily work and bring benefits, improve their business or to make the research outcome more efficient.
In general, underlying models are trained on large datasets, mainly on sources from websites, and online books and articles. In combination with information provided by the user, the model can give an impressively fast response. Even if the range of questions and answers look unrestricted, there are limits to the models. In this paper, possible use cases for agricultural tasks are elucidated. This includes the textual preparation of facts, consulting tasks, interpretation of decision support models in plant disease management, as well as guides for tutorials to integrate modern digital techniques into agricultural work. Opportunities and challenges are described, as well as limitations and insufficiencies. The authors describe a map of easy-to-reach topics in agriculture where the integration of LLMs seems to be very likely within the next few years. |
| Cote : | Réservé lecteur CIHEAM |
| URL / DOI : | https://doi.org/10.1016/j.compag.2024.108924 |
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