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Unleashing the Power of Generative AI in Agriculture 4.0 for Smart and Sustainable Farming

  • Siva Sai*
  • , Sanjeev Kumar
  • , Aanchal Gaur
  • , Shivam Goyal
  • , Vinay Chamola
  • , Amir Hussain
  • *Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

37 Scopus citations

Abstract

Generative artificial intelligence (GAI) represents a pioneering class of artificial intelligence systems renowned for producing diverse media, such as text and images. Agriculture 4.0 (AG-4.0) is a concept that integrates advanced technologies such as the Internet of Things (IoT), data analytics, artificial intelligence, and precision agriculture into the agricultural sector. The integration of GAI and AG-4.0 can generate new and valuable agricultural insights and solutions through pattern recognition and data analysis. This integration enhances farming practices by generating predictive models, simulating optimal growth conditions, diagnosing plant diseases, and optimizing genetic traits. In spite of the tremendous scope of GAI in agriculture, there has been no detailed study concerning the applications and scope of GAI in AG-4.0. Addressing this research gap, we explore various applications, real-world products, and limitations of GAI in agriculture. We explore how GAI models such as ChatGPT and Dall-E can be personalized advisors for farmers, help increase awareness about farmer relief programs, design farm layouts, and many other such applications. Additionally, we cover four real-world GAI products deployed to assist farmers. Since GAI is a growing technology, it poses challenges such as scarcity of data, data privacy, and interpretability. We elaborately discuss these limitations and suggest multiple directions for future research in GAI for agriculture.

Original languageEnglish
Article number63
JournalCognitive Computation
Volume17
Issue number1
DOIs
StatePublished - Feb 2025
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 2 - Zero Hunger
    SDG 2 Zero Hunger
  2. SDG 12 - Responsible Consumption and Production
    SDG 12 Responsible Consumption and Production

Keywords

  • Agriculture 4.0
  • Applications
  • Case studies
  • Generative AI

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Cognitive Neuroscience

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