Research Article AI in Agro-techniques – The Future Farming

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AI in Agro-techniques – The Future Farming

Souvick Banik*, Konathachira Sreeja*, Ameda Swarnalatha*,

G K Dinesh** and A B Sharangi ***

*Ph.D. Research Scholar, Bidhan Chandra Krishi Viswavidyalaya, West Bengal

** Assistant Professor (Environmental Sciences), Nodal Officer for ISO 14001:2015 Certification and Environmental Management Systems Coordinator (SRM University: 2023-2025)

***Professor, Bidhan Chandra Krishi Viswavidyalaya, West Bengal​

Global population growth expected to reach 10 billion by 2050 is putting increasing pressure on agriculture to boost production and improve yields. To prevent potential food shortages, two main strategies have emerged: expanding farmland and promoting large-scale farming, or improving productivity on existing land through innovation and advanced technology. However, farmers face numerous challenges, including limited land availability, labor shortages, climate change, environmental degradation, and declining soil fertility. As a result, modern agriculture is rapidly evolving and adopting new approaches. Farming has progressed significantly from the days of hand plows and horse-drawn equipment, with each season introducing new technologies designed to increase efficiency and optimize harvests. Despite this progress, many farmers and agribusinesses have yet to fully utilize the benefits that artificial intelligence (AI) can bring to agriculture.

Benefits of AI in Agro-farming

Until recently, using the words AI and agriculture in the same sentence may have seemed like a strange combination. After all, agriculture has been the backbone of human civilization for millennia, providing sustenance as well as contributing to economic development, while even the most primitive AI only emerged several decades ago. Nevertheless, innovative ideas are being introduced in every industry, and agriculture is no exception. In recent years, the world has witnessed rapid advancements in agricultural technology, revolutionizing farming practices. These innovations are becoming increasingly essential as global challenges such as climate change, population growth together with resource scarcity threaten the sustainability of our food system. Introducing AI solves many challenges and helps to diminish many disadvantages of traditional farming.

Cost savings

Increasing crop yields remains a primary objective for farmers. When integrated with precision agriculture, AI enables farmers to produce more while using fewer resources. It combines effective soil management, variable rate technology, and advanced data analysis to maximize productivity while minimizing costs. AI-powered systems provide real-time insights into crop conditions, helping farmers determine where irrigation, fertilization, or pest control is needed. Innovative approaches like vertical farming further enhance food production while conserving resources. As a result, farmers can reduce herbicide use, improve crop quality, increase profitability, and achieve significant cost savings.

Impact of Automation

Agricultural work is hard, so labor shortages are nothing new. Thankfully, automation provides a solution without the need to hire more people. While mechanization transformed agricultural activities that demanded super-human sweat and draft animal labor into jobs that took just a few hours, a new wave of digital automation is once more revolutionizing the sector. Automated farm machinery like driverless tractors, smart irrigation, fertilization systems, IoT-powered agricultural drones, smart spraying, vertical farming software, and AI-based greenhouse robots for harvesting are just some examples. Compared with any human farm worker, AI-driven tools are far more efficient and accurate.



Yield Mapping and Predictive Analytics

Yield mapping employs machine learning algorithms to analyze large volumes of data in real time. This allows farmers to identify crop patterns and characteristics, improving planning and decision-making. By integrating 3D mapping, sensor inputs, and drone data, farmers can forecast soil productivity for specific crops. Repeated drone monitoring enhances analytical accuracy over time. These advanced methods enable more precise yield predictions, helping farmers determine optimal planting times, crop placement, and resource allocation to maximize returns.

Automatic weeding and harvesting

Just as computer vision can detect pests and plant diseases, it can also identify weeds and invasive species. By using machine learning to analyze leaf size, shape, and color, AI systems can distinguish crops from unwanted plants. These capabilities allow robots to perform automated tasks such as precision weeding. In fact, robotic weeding solutions are already in use. As these technologies become more affordable and widespread, both weeding and harvesting may increasingly be handled by intelligent robotic systems.