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

Sai, Siva; Kumar, Sanjeev; Gaur, Aanchal; Goyal, Shivam; Chamola, Vinay; Hussain, Amir

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.

Citation

Sai, S., Kumar, S., Gaur, A., Goyal, S., Chamola, V., & Hussain, A. (2025). Unleashing the Power of Generative AI in Agriculture 4.0 for Smart and Sustainable Farming. Cognitive Computation, 17(1), Article 63. https://doi.org/10.1007/s12559-025-10420-6

• Journal Article Type: Article
• Acceptance Date: Jan 23, 2025
• Online Publication Date: Feb 4, 2025
• Publication Date: 2025-02
• Deposit Date: Feb 18, 2025
• Journal: Cognitive Computation
• Print ISSN: 1866-9956
• Electronic ISSN: 1866-9964
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 1
• Article Number: 63
• DOI: https://doi.org/10.1007/s12559-025-10420-6
• Keywords: Agriculture 4.0, Generative AI, Applications, Case studies
• Public URL: http://researchrepository.napier.ac.uk/Output/4122300