Benchmarking Large Language Models in Evaluating Workforce Risk of Robotization: Insights from Agriculture DOI Creative Commons
Lefteris Benos, Vasso Marinoudi, Patrizia Busato

et al.

AgriEngineering, Journal Year: 2025, Volume and Issue: 7(4), P. 102 - 102

Published: April 3, 2025

Understanding the impact of robotization on workforce dynamics has become increasingly urgent. While expert assessments provide valuable insights, they are often time-consuming and resource-intensive. Large language models (LLMs) offer a scalable alternative; however, their accuracy reliability in evaluating potential remain uncertain. This study systematically compares general-purpose LLM-generated with evaluations to assess effectiveness agricultural sector by considering human judgments as ground truth. Using ChatGPT, Copilot, Gemini, LLMs followed three-step evaluation process focusing (a) task importance, (b) for robotization, (c) attribute indexing 15 occupations, mirroring methodology used assessors. The findings indicate significant tendency overestimate potential, most errors falling within range 0.229 ± 0.174. can be attributed primarily LLM reliance grey literature idealized technological scenarios, well limited capacity, account complexities work. Future research should focus integrating knowledge into training improving bias detection mitigation datasets, expanding studied enhance assessment reliability.

Language: Английский

Artificial intelligence in horticulture DOI Creative Commons
Simon Pearson

The Journal of Horticultural Science and Biotechnology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 7

Published: March 9, 2025

Horticultural crop production systems are being transformed by the advent of artificial intelligence (AI). The pace technology development is extraordinary. well-known large language model (LLMs) CHAT-GPT was only released in November 2022, yet it and analogous transforming lives, industry education. Prior to LLMs, 'narrower' AI machine learning image recognition natural processing. Multiple applications now deployed across global horticulture, whether robots for harvesting, imaging technologies forecasting, decision support optimise greenhouse control, accelerated knowledge transfer or general business management. Here, we review these within horticulture. Whilst journal 100 years old, starting realise its potential, an accelerator with power transform next century

Language: Английский

Citations

0
Benchmarking Large Language Models in Evaluating Workforce Risk of Robotization: Insights from Agriculture DOI Creative Commons
Lefteris Benos, Vasso Marinoudi, Patrizia Busato

et al.

AgriEngineering, Journal Year: 2025, Volume and Issue: 7(4), P. 102 - 102

Published: April 3, 2025

Understanding the impact of robotization on workforce dynamics has become increasingly urgent. While expert assessments provide valuable insights, they are often time-consuming and resource-intensive. Large language models (LLMs) offer a scalable alternative; however, their accuracy reliability in evaluating potential remain uncertain. This study systematically compares general-purpose LLM-generated with evaluations to assess effectiveness agricultural sector by considering human judgments as ground truth. Using ChatGPT, Copilot, Gemini, LLMs followed three-step evaluation process focusing (a) task importance, (b) for robotization, (c) attribute indexing 15 occupations, mirroring methodology used assessors. The findings indicate significant tendency overestimate potential, most errors falling within range 0.229 ± 0.174. can be attributed primarily LLM reliance grey literature idealized technological scenarios, well limited capacity, account complexities work. Future research should focus integrating knowledge into training improving bias detection mitigation datasets, expanding studied enhance assessment reliability.

Language: Английский

Citations

0