文献类型: 外文期刊
作者: Wyckhuys, Kris A. G.;Akutse, Komivi S.;Amalin, Divina M.;Araj, Salah-Eddin;Beltran, Marie Joy B.;Ben Fekih, Ibtissem;Calatayud, Paul-Andre;Cicero, Lizette;Cokola, Marcellin C.;Colmenarezm, Yelitza C.;Dessauvagesj, Kenza;Dubois, Thomas;Durocher-Grangern, Lena;Fernandez-Triana, Jose L.;Francis, Frederic;Haddi, Khalid;Harrison, Rhett D.;Haseeb, Muhammad;Iwanicki, Natasha S. A.;Jaber, Lara R.;Khamis, Fathiya M.;Legaspi, Jesusa C.;Lomeli-Flores, Refugio J.;Lyu, Baoqian;Montoya-Lerma, James;Nurkomar, Ihsan;O'hara, James E.;Perier, Jermaine D.;Ramirez-Romero, Ricardo;Sanchez-Garcia, Francisco J.;Tavares, Wagner De Souza;Robinson-Baker, Ann Marie S.;Silveira, Luis C. P.;Simeon, Larisner;Solter, Leellen F.;Santos-Amaya, Oscar F.;Trabanino, Rogelio;Valicente, Fernando H.;Vasquez, Carlos;Wang, Zhenying;Zang, Lian-Sheng;Zhang, Wei;Zimba, Kennedy J.;Wu, Kongming;Yubak, D. Gc
作者机构:
关键词: Evidence synthesis;Agroecology;Biological control;Pesticide reduction;Artificial intelligence;Chatbot;Systematic literature review;Large language models
期刊名称: COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN: 0168-1699
年卷期: 2025 年 242 卷
页码:
收录情况: SCIE(2025版) ; ; EI(2025版)
摘要: Generative artificial intelligence (AI) could transform evidence synthesis and revolutionize the global scientific enterprise, yet its agricultural applications are understudied. Here, we systematically assess the performance of three web-grounded AI engines (ChatGPT, ScholarAI and DeepSeek) in synthesizing the global literature on biological control of the fall armyworm Spodoptera frugiperda, and benchmark their outputs against a recent, near-exhaustive human review. Though all engines rapidly screened vast literature corpora, they exhibited shortcomings in factual accuracy, reporting reliability and data consistency. In machine-run syntheses, natural enemy prevalence and performance data often diverged from published records while the level of agreement in enumerating top-performing taxa was evenly low. Meanwhile, internal consistency between laboratory and field-level parasitism data for ScholarAI and DeepSeek was similar to that in human-run reviews. All models tended towards faulty data extrapolation, hallucination and data fabrication, and a sporadic exclusion of key species. While autonomous, machine-only efforts accurately capture coarse-grained patterns in natural enemy identity, abundance, and impacts, they carry limited utility for (living) evidence syntheses or rigorous decision-support. Yet, handled with prudence and due human oversight, machine power might eventually revitalize underfunded disciplines and advance nature-friendly farming.
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