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Stream/Area:
-
Subject:
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Paper ID:
IJSREM64020
Volume:
10 |
5 |
May 2026
Pages:
1-10
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 Computer Science and Engineering
Aks University Satna
Department of Computer Science and Engineering
Aks University Satna
Agriculture is one of the most important sectors for human survival and economic development. It provides food, raw materials, and employment to a large portion of the global population. However, modern agriculture faces several challenges, including climate change, irregular rainfall, soil degradation, crop diseases, pest attacks, and the increasing demand for food due to rapid population growth. These challenges make it difficult for farmers to maintain high productivity and manage resources efficiently using traditional farming methods. In recent years, Artificial Intelligence (AI) has emerged as a powerful technology to address these agricultural challenges. AI systems can analyse large volumes of data collected from farms, sensors, satellites, and weather-monitoring systems. By processing this data, AI helps farmers make better decisions regarding crop management, irrigation, fertiliser use, and disease control. Various AI techniques, such as machine learning, deep learning, and computer vision, are widely used in agriculture. These technologies enable systems to learn from historical data and identify patterns that are not easily visible to humans. For instance, AI can detect plant diseases at an early stage using image analysis, predict crop yields based on environmental conditions, and monitor crop growth through drone and satellite imagery. This review paper focuses on the role of artificial intelligence in modern agriculture and explores its applications in improving farming practices. It highlights key areas such as crop monitoring, disease detection, smart irrigation, and yield prediction. Additionally, the paper discusses the challenges associated with the adoption of AI, including high costs, lack of technical knowledge, and limited data availability in rural areas. Overall, artificial intelligence has the potential to transform traditional agriculture into a more efficient, data-driven, and sustainable system. With proper implementation and support, AI technologies can significantly enhance agricultural productivity and help meet future food demands.
KEYWORDS
In conclusion, Artificial Intelligence has the potential to significantly transform modern agriculture by improving productivity, efficiency, and decision-making. This study highlights how AI technologies such as machine learning, deep learning, and IoT are being used in various agricultural applications, including crop monitoring, disease detection, yield prediction, and smart irrigation. From the literature review, it is observed that many researchers have successfully applied AI techniques to solve real-world agricultural problems. These technologies help farmers make better decisions, reduce crop losses, and use resources more efficiently. The methodology proposed in this study also demonstrates how AI can be applied in a structured way to analyse agricultural data and generate useful outputs. Despite its advantages, there are still several challenges, such as high cost, lack of technical knowledge, and limited infrastructure that restrict the adoption of AI in agriculture. Addressing these challenges is important for the successful implementation of AI-based systems. Overall, Artificial Intelligence has a promising future in agriculture. With proper support, awareness, and technological development, AI can play a key role in developing a more efficient, sustainable, and smart agricultural system.
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Publication Date
5/27/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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