Analysing the potential of ChatGPT to support plant disease risk forecasting systems
This study explores the potential of two versions of the ChatGPT large language model (GPT-3.5 Turbo and GPT-4) in supporting plant disease risk forecasting through the translation of model-based predictions into advisory messages. A dataset of 3125 messages, each referred to an artificially generat...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525000589 |
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| author | Roberta Calone Elisabetta Raparelli Sofia Bajocco Eugenio Rossi Lorenzo Crecco Danilo Morelli Chiara Bassi Rocchina Tiso Riccardo Bugiani Fabio Pietrangeli Giovanna Cattaneo Camilla Nigro Marco Secondo Gerardi Simone Bussotti Angela Sanchioni Danilo Tognetti Mariangela Sandra Irene De Lillo Paolo Framarin Sandra Di Ferdinando Simone Bregaglio |
| author_facet | Roberta Calone Elisabetta Raparelli Sofia Bajocco Eugenio Rossi Lorenzo Crecco Danilo Morelli Chiara Bassi Rocchina Tiso Riccardo Bugiani Fabio Pietrangeli Giovanna Cattaneo Camilla Nigro Marco Secondo Gerardi Simone Bussotti Angela Sanchioni Danilo Tognetti Mariangela Sandra Irene De Lillo Paolo Framarin Sandra Di Ferdinando Simone Bregaglio |
| author_sort | Roberta Calone |
| collection | DOAJ |
| description | This study explores the potential of two versions of the ChatGPT large language model (GPT-3.5 Turbo and GPT-4) in supporting plant disease risk forecasting through the translation of model-based predictions into advisory messages. A dataset of 3125 messages, each referred to an artificially generated five-day disease risk scenario, was inspected using lexical, consistency, and sentiment analyses. A participatory approach was adopted involving officers and technicians from eleven Italian regional phytosanitary services in the message evaluation. Lexical analysis indicated that GPT-4 produced more detailed advises, leading to diversified responses across disease pressure scenarios. In contrast, GPT-3.5 generated concise and straightforward messages, making it well-suited for routine tasks requiring clarity and brevity. Sentiment analysis revealed the greater adaptability of GPT-4 in shifting from a reassurance to an urgency tone as the risk level increased, while GPT-3.5 maintained a more neutral stance across disease pressure scenarios. Consistency analysis demonstrated greater stability in GPT-3.5 messages, whereas GPT-4 exhibited more expressivity and creativity. Expert evaluations highlighted promising potential of both models for operational use, with GPT-4 noted for its precision in communicating disease risk and supporting technical bulletins drafting. However, both GPT versions were criticized for producing overly generic advices, highlighting the importance of domain-specific training to integrate information on best management practices, locally authorized substances, and historical treatment schedules. Such implementation is crucial to align large language models with Integrated Pest Management principles and improve their precision towards their operational use. |
| format | Article |
| id | doaj-art-d31494b8b0c94e7ba75bef9993df95bf |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-d31494b8b0c94e7ba75bef9993df95bf2025-08-20T02:52:21ZengElsevierSmart Agricultural Technology2772-37552025-03-011010082410.1016/j.atech.2025.100824Analysing the potential of ChatGPT to support plant disease risk forecasting systemsRoberta Calone0Elisabetta Raparelli1Sofia Bajocco2Eugenio Rossi3Lorenzo Crecco4Danilo Morelli5Chiara Bassi6Rocchina Tiso7Riccardo Bugiani8Fabio Pietrangeli9Giovanna Cattaneo10Camilla Nigro11Marco Secondo Gerardi12Simone Bussotti13Angela Sanchioni14Danilo Tognetti15Mariangela Sandra16Irene De Lillo17Paolo Framarin18Sandra Di Ferdinando19Simone Bregaglio20CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, Bologna BO I-40128, Rome I-00184 , Italy; Corresponding author.CREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, Bologna BO I-40128, Rome I-00184 , ItalyCREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, Bologna BO I-40128, Rome I-00184 , ItalyCREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, Bologna BO I-40128, Rome I-00184 , ItalyCREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, Bologna BO I-40128, Rome I-00184 , ItalyCREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, Bologna BO I-40128, Rome I-00184 , ItalyServizi Alle Imprese Agricole e Florovivaismo, CAAR (Centro Agrometeorologia Applicata Regionale), Laboratori Regionali Analisi Terreni-Produzioni Vegetali e Fitopatologico, Liguria Region, Sarzana SP I-19038, ItalyPlant Protection Service, Emilia-Romagna Region, Via A. da Formigine 3, Bologna BO I-40129, ItalyPlant Protection Service, Emilia-Romagna Region, Via A. da Formigine 3, Bologna BO I-40129, ItalyServizio Supporto Specialistico all'Agricoltura, Via Nazionale 37, Villanova di Cepagatti, Pescara PE I-65012, ItalyRegione Lombardia, Plant Protection Service, Milan MI I-20124, ItalyLucana Agency for Development and Innovation in Agriculture, Basilicata Region, Via Annunziatella 64, Matera MT I-75100, ItalyLAORE Sardegna, Regional Agency for Agriculture Development, Via Caprera 8, Cagliari CA I-09123, ItalyFondazione Agrion, Via Falicetto, 24, Manta CN I-12030, ItalyAMAP - Agenzia per l'innovazione nel settore agroalimentare e della pesca ''Marche Agricoltura Pesca'', Osimo AN I-60027, ItalyAMAP - Agenzia per l'innovazione nel settore agroalimentare e della pesca ''Marche Agricoltura Pesca'', Osimo AN I-60027, ItalyServizio fitosanitario e chimico, ricerca, sperimentazione e assistenza tecnica. Agenzia regionale per lo sviluppo rurale del Friuli-Venezia Giulia – ERSA, Via Sabbatini 5, Pozzuolo del Friuli UD I-33050, ItalyARPAV. Dipartimento Regionale per La Sicurezza Del Territorio. U.O.C. Meteorologia e Climatologia, Veneto Region, Via Marconi 55, Teolo PD I-35037, ItalyRegione Veneto - Unità Organizzativa Fitosanitario, Viale dell'Agricoltura 1/A, Buttapietra VR I-37060, ItalyARSIAL – Agenzia Regionale per lo Sviluppo e l'Innovazione dell'Agricoltura del Lazio, Area Qualità e Pianificazione Territoriale, Via Rodolfo Lanciani 38, Roma I–00162, ItalyCREA - Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, Bologna BO I-40128, Rome I-00184 , ItalyThis study explores the potential of two versions of the ChatGPT large language model (GPT-3.5 Turbo and GPT-4) in supporting plant disease risk forecasting through the translation of model-based predictions into advisory messages. A dataset of 3125 messages, each referred to an artificially generated five-day disease risk scenario, was inspected using lexical, consistency, and sentiment analyses. A participatory approach was adopted involving officers and technicians from eleven Italian regional phytosanitary services in the message evaluation. Lexical analysis indicated that GPT-4 produced more detailed advises, leading to diversified responses across disease pressure scenarios. In contrast, GPT-3.5 generated concise and straightforward messages, making it well-suited for routine tasks requiring clarity and brevity. Sentiment analysis revealed the greater adaptability of GPT-4 in shifting from a reassurance to an urgency tone as the risk level increased, while GPT-3.5 maintained a more neutral stance across disease pressure scenarios. Consistency analysis demonstrated greater stability in GPT-3.5 messages, whereas GPT-4 exhibited more expressivity and creativity. Expert evaluations highlighted promising potential of both models for operational use, with GPT-4 noted for its precision in communicating disease risk and supporting technical bulletins drafting. However, both GPT versions were criticized for producing overly generic advices, highlighting the importance of domain-specific training to integrate information on best management practices, locally authorized substances, and historical treatment schedules. Such implementation is crucial to align large language models with Integrated Pest Management principles and improve their precision towards their operational use.http://www.sciencedirect.com/science/article/pii/S2772375525000589Large Language ModelsIntegrated Pest ManagementAdvisory MessagesStakeholders EngagementLexical AnalysisSentiment Analysis |
| spellingShingle | Roberta Calone Elisabetta Raparelli Sofia Bajocco Eugenio Rossi Lorenzo Crecco Danilo Morelli Chiara Bassi Rocchina Tiso Riccardo Bugiani Fabio Pietrangeli Giovanna Cattaneo Camilla Nigro Marco Secondo Gerardi Simone Bussotti Angela Sanchioni Danilo Tognetti Mariangela Sandra Irene De Lillo Paolo Framarin Sandra Di Ferdinando Simone Bregaglio Analysing the potential of ChatGPT to support plant disease risk forecasting systems Smart Agricultural Technology Large Language Models Integrated Pest Management Advisory Messages Stakeholders Engagement Lexical Analysis Sentiment Analysis |
| title | Analysing the potential of ChatGPT to support plant disease risk forecasting systems |
| title_full | Analysing the potential of ChatGPT to support plant disease risk forecasting systems |
| title_fullStr | Analysing the potential of ChatGPT to support plant disease risk forecasting systems |
| title_full_unstemmed | Analysing the potential of ChatGPT to support plant disease risk forecasting systems |
| title_short | Analysing the potential of ChatGPT to support plant disease risk forecasting systems |
| title_sort | analysing the potential of chatgpt to support plant disease risk forecasting systems |
| topic | Large Language Models Integrated Pest Management Advisory Messages Stakeholders Engagement Lexical Analysis Sentiment Analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525000589 |
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