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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62047
Volume:
10 |
4 |
April 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of AIDS
AISSMS IOIT Pune
Department of AIDS
AISSMS IOIT Pune
Department of AIDS
AISSMS IOIT Pune
Artificial Intelligence (AI) is rapidly transforming the agricultural sector by enabling data‑driven decision making and intelligent automation. With the global population increasing and the demand for food rising, traditional farming methods are often insufficient to ensure sustainable agricultural productivity. AI technologies such as machine learning, deep learning, computer vision, robotics, and Internet of Things (IoT) devices are increasingly used to monitor crop health, predict yields, detect plant diseases, manage irrigation systems, and optimize resource usage. This research paper reviews existing literature on AI applications in agriculture and discusses major technologies, benefits, limitations, and future developments. The study shows that AI‑based agricultural systems can improve productivity, reduce environmental impact, and support sustainable farming practices.
KEYWORDS
Artificial Intelligence is revolutionizing agriculture by improving productivity, efficiency, and sustainability. AI technologies enable farmers to monitor crops, detect diseases, optimize resources, and make better decisions. Although challenges such as cost and lack of awareness exist, continuous advancements in technology will drive the adoption of AI in agriculture. In the future, AI will play a critical role in ensuring global food security and sustainable farming practices.
K. G. Liakos et al., 'Machine learning in agriculture: A review,' Sensors, vol. 18, no. 8, 2018. [2] T. Chen et al., 'Empowering agrifood systems with artificial intelligence,' arXiv preprint, 2023. [3] U. Nawaz et al., 'AI in agriculture: A survey of deep learning techniques,' arXiv preprint, 2025. [4] E. Olaniyi et al., 'GANs for image augmentation in agriculture,' arXiv preprint, 2022. [5] X. Hu et al., 'Diffusion models in smart agriculture,' arXiv preprint, 2025.
Publication Date
4/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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