The role of AI in modern agriculture
Abstract
The global population is growing, and soon be reaching 10 billion by 2050, and with it, the pressure on the agricultural sector to increase crop production (FAO, 2017; Hoque & Padhiary, 2024). AI is emerging as a critical solution to this challenge, offering a new wave of digital automation to overcome traditional obstacles like limited land, labour shortages, climate change and declining soil fertility (Basa, 2025). AI-driven technologies, combined with other innovations like IoT sensors, drones and big data analytics are transforming farming from a traditional manual practice into a data-driven, highly efficient industry and soon would be overtaking the traditional practices and design each season with new technologies.
Full Text
Tables and figures are in the full-text PDF.
Introduction
Agriculture has been the backbone of human civilization and also in contributing to economic development (Bongiovanni & Lowenberg-DeBoer, 2024) [2] . In the recent years the world has witnessed rapid advancements in agricultural technology-making it a big revolution in farming practices and facing challenges caused due to climate change, extensive growth in the population causing drastic resource scarcity thus causing food scarcity and sustainability. Introducing AI helps in solves the challenges and the disadvantages of traditional farming (Hoque & Padhiary, 2024) [4] . AI-powered predictive analytics enable farmers to process vast amounts of data in a short time (Basa, 2025) [1] . This data provides insights into everything from soil health and weather conditions and market demand (IJRASET, 2024; Springer Discover Robotics, 2025) [6] . This allows for informed decisions at every stage of the crop cultivation process, AI can analyze market demand, forecast prices and also help in providing optimal time for sowing and harvesting (Basa, 2025; Journal of Agriculture and Food Research, 2025) [1,7] . AI in agriculture can help explore the soil health and recommend the application of fertilizer and pesticides that could boost the production and gain profitability (IJRASET, 2024; ScienceDirect, 2023). AI-driven precision agriculture directly impacts a farmer's bottom line through several key methods:
• Optimizing Resource Allocation: Instead of uniformly applying resources across an entire field, AI algorithms can pinpoint specific areas that require attention (AIoT for Precision Agriculture, 2025). This can reduce the use of water by up to 50% and chemical pesticides by as much as 90%, according to some studies (Bongiovanni & Lowenberg-DeBoer, 2024) [2] . This not only lowers material costs but also minimizes environmental impact (Journal of Agriculture and Food Research, 2025) [7] . (Hoque & Padhiary, 2024) [4] . AI provides a solution to labour shortages by automating laborious and timeconsuming tasks (IAEME, 2022). Automated farm machinery, smart irrigation systems, and AI-based robots are more efficient and accurate than human workers (PubMed, 2024). • Task Automation: AI and robotics can perform repetitive, manual tasks like weeding, planting, and harvesting (ScienceDirect, 2023). This frees up the human workforce for more complex or supervisory roles, shifting the focus from physical labour to technology management (Hoque & Padhiary, 2024) [4] . ). This reduces waste and damage to crops, something that is difficult to achieve with manual labor alone (Basa, 2025) [1] .
In places like Japan, where the agricultural workforce is aging, autonomous rice-planting robots are being used to maintain stable food production (IAEME, 2022). Similarly, in the US, companies are developing robotic harvesters to address the scarcity of seasonal farm workers (Science Direct, 2023).
Applications of AI in Agriculture
The AI in agriculture market is projected to grow significantly, indicating a rapid adoption of these technologies. The text highlights several key applications: raises legal questions, and there are risks of cyber attacks and data leaks that could disrupt food supplies. The lack of clear regulations is a significant concern.
The Future of AI in Agriculture
The future of AI in agriculture is promising. Technology has always been a driver of progress in farming, and AI is the next logical step (FAO, 2017). The integration of AI with big data, sensors, and robotics will continue to increase efficiency and productivity while reducing the traditional challenges of farming. Public and private sector collaboration is crucial to overcome the adoption challenges (Hoque & Padhiary, 2024) [4] . To reap all the benefits of AI, farmers first need a technology infrastructure. It could take years to develop that infrastructure, but doing so could result in a robust, future proof technology ecosystem. Understanding how AI works and how best to integrate technical knowledge into real-life processes is vital for maximizing its benefits. Providers of AgTech solutions have an important role to play. Each must consider how they can improve their tools, address challenges, and clearly convey the measurable benefits of AI and machine learning. If this can be achieved, the future of AI in agriculture is bound to be fruitful. The success of human society is essentially dependent on the optimization of its agricultural systems. Traditional farming methods are becoming outdated, need for advanced technological solutions. Worldwide, the impact of automation on industries has always been considerable. Digital technology is now playing a huge role in transforming agriculture and the impact of artificial intelligence in agriculture is set to be vast.
References
- Basa S. Artificial intelligence applications in agriculture: enhancing productivity and sustainability. Agric Technol Rev. 2025;12(1):45-60.
- Bongiovanni R, Lowenberg-DeBoer J. Precision agriculture and sustainability: economic and environmental impacts. J Agric Econ. 2024;75(2):321339. doi:10.1111/1477-9552.12567
- Food and Agriculture Organization of the United Nations. The future of food and agriculture -trends and challenges. Rome: FAO; 2017.
- Hoque ME, Padhiary A. Artificial intelligence in agriculture: opportunities, challenges, and future perspectives. J Smart Agric. 2024;6(3):101-118.
- International Association of Engineering and Management Education. Role of artificial intelligence and robotics in modern agriculture. Int J Mech Eng Technol. 2022;13(4):1-10.
- International Journal for Research in Applied Science & Engineering Technology. Applications of artificial intelligence in soil health monitoring and crop management. IJRASET. 2024;12(5):220-226.
- Journal of Agriculture and Food Research Editorial Board. AI-enabled decision support systems for sustainable agriculture. J Agric Food Res. 2025;10:100115. doi:10.1016/j.jafr.2025.100115
- MDPI Drones Editorial Office. Use of drones and artificial intelligence for crop monitoring and pest detection. Drones. 2024;8(2):55. doi:10.3390/drones8020055
- National Library of Medicine. Artificial intelligencebased automation in agricultural systems. Bethesda (MD): National Institutes of Health; 2024.
- ScienceDirect Editorial Board. Machine learning and robotics applications in precision farming. Agric Syst. 2023.
- Springer Nature Robotics Editorial Team. Predictive analytics and robotics in agriculture. Discov Robot. 2025.
How this paper connects to the literature. Drag to explore, click any node to open that paper.