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

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.