Fields of the future: Digital transformation in smart agriculture with large language models and generative AI

• Published in: Computer standards and interfaces Vol. 94; p. 104005
• Main Authors: Shaikh, Tawseef Ayoub, Rasool, Tabasum, Mir, Waseem Ahmad
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2025
• Subjects: Agricultural text classification; ChatGPT; Generative AI; Generative pre-trained enerative pre-trained transformer (GPT); Language models; Natural language processing; Semantic matching; Very large pre-trained language model; Very large pre-trained language model; Semantic matching; Generative AI; ChatGPT; Generative pre-trained enerative pre-trained transformer (GPT); Natural language processing; Agricultural text classification; Language models
• ISSN: 0920-5489
• DOI: 10.1016/j.csi.2025.104005

•We provide a detailed background of different types of large language models and their general architecture.•A comprehensive literature survey about large language models related to various computer science fields. A state-of-the-art review, analysis, and comparison of security issues for large language models.•Motivated by the progress of large pre-trained language models like ChatGPT, we conducted a preliminary study on agricultural text classification.•The applications of large language models and Generative AI in smart and precision agriculture are discussed. More specifically, the applications are categorized into six domains ranging from smart farming and livestock, smart and precision agriculture, generative adversarial network in agricultural language processing (ALP), agricultural robots (AR), plant phenotyping (PP), and postharvest quality assessment.•An analysis of large language models security requirements and challenges, possible solutions, and areas for future research are discussed. Language models (LLMs) have shown to be very useful in many fields like healthcare and finance, as natural language comprehension and generation have advanced. The capacity of LLM to participate in textual discussion has been the subject of much research, and the findings have proved encouraging across several domains. The inability of conventional image classification networks to comprehend the causes of crop diseases and etiology further impedes precise diagnosis. Agricultural diagnostic models on a grand scale will be based on generative pre-trained transformers (GPT) assisted with agrarian settings. By examining the efficacy of text corpora linked to agriculture for pretraining transformer-based language (TBL) models, this research delves into agricultural natural language processing (ANLP). To make the most of it, we looked at several important aspects, including prompt building, response parsing, and several ChatGPT versions. Despite the proven effectiveness and huge potential, there has been little exploration of LLM and Generative AI to agriculture artificial intelligence (AI). Therefore, this study aims to explore the possibility of LLM and Generative AI in smart agriculture. In particular, we present conceptual tools and technical background to facilitate understanding the problem space and uncover new research directions in this field. The paper presents an overview of the evolution of generative adversarial network (GAN) architectures followed by a first systematic review of various applications in smart agriculture and precision farming systems, involving a diversity of visual recognition tasks for smart farming and livestock, precision agriculture, agricultural language processing (ALP), agricultural robots (AR), plant phenotyping (PP), and postharvest quality assessment. We outline the possibilities, difficulties, constraints, and shortcomings. The study lays forth a road map of accessible areas in agriculture where LLM integration is likely to happen shortly. The research suggests exciting directions for further study in this area, which could lead to better agricultural NLP applications.