Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges

arXiv:2308.06668v4 Announce Type: replace
Abstract: The past decade has witnessed the rapid development and adoption of ML & DL methodologies in agricultural systems, showcased by great successes in agricultural applications. However, these conventional ML/DL models have certain limitations: they heavily rely on large, costly-to-acquire labeled datasets for training, require specialized expertise for development and maintenance, and are mostly tailored for specific tasks, thus lacking generalizability. Recently, large pre-trained models, also known as FMs, have demonstrated remarkable successes in language, vision, …

abstract adoption agriculture applications arxiv basics challenges cs.cv cs.lg datasets development foundation however language language models large language large language models limitations opportunities smart systems type