Despite notable progress in agricultural practices, India still faces a considerable yield gap, with key crops like rice and wheat underperforming relative to their potential. Artificial intelligence (AI) is emerging as a powerful tool to address this issue, offering data-driven solutions that could revolutionize farming across the country.
AI technology provides valuable support at various stages of the farming cycle. Before sowing, AI can evaluate soil health and suggest suitable crops based on local climatic conditions, improving the chances of a successful harvest. During cultivation, AI-powered sensors and drones monitor crop health, enabling precise disease management and efficient use of resources such as water and fertilizers. These innovations not only boost productivity but also promote sustainable farming by minimizing waste and optimizing input use.
Post-harvest, AI assists in forecasting market demand and streamlining logistics, reducing storage losses and increasing farmer incomes. For instance, AI-driven apps in Andhra Pradesh have demonstrated the potential to increase crop yields by up to 30%, showcasing the technology’s ability to enhance agricultural efficiency and profitability.
The Indian government is actively supporting the integration of AI in agriculture through initiatives like the Digital Agriculture Mission (2021–2025). This program emphasizes AI as a critical component of data-driven farming and encourages collaboration between public and private sectors to embed AI into agricultural systems. However, the widespread adoption of AI in farming faces several challenges.
One of the primary obstacles is the availability and quality of data. AI models rely on comprehensive datasets, yet India’s farm-level data is often insufficient or outdated. Additionally, inadequate digital infrastructure, including poor internet connectivity and low smartphone usage in rural areas, limits the scalability of AI solutions. Addressing these infrastructural gaps is essential for the effective deployment of AI technologies in agriculture.
Cost is another significant barrier, as many smallholder farmers cannot afford AI tools without financial support. There is also a trust deficit, with farmers hesitant to adopt AI recommendations over traditional methods, particularly when these technologies are not communicated in local languages or lack proven effectiveness. Building trust through clear communication and demonstrable results is crucial for encouraging farmer acceptance.
To fully leverage AI’s potential, experts recommend investing in open-source agricultural data platforms and improving rural digital infrastructure. Providing AI tools through cooperatives or farmer producer organizations (FPOs) could enhance access for small farmers. Training these organizations to facilitate technology adoption and localizing AI platforms could further drive uptake, bridging the gap between innovation and practical application.
AI offers the potential to empower Indian farmers by delivering timely information and optimizing resource use. While the yield gap remains a significant challenge, a combination of technology, supportive policies, and collaboration could lead to a more productive agricultural future in India. By fostering innovation and addressing existing barriers, AI can play a pivotal role in transforming Indian agriculture from subsistence to surplus.
In an interview with Indiatoday, M.K. Dhanuka, Chairman of Dhanuka Agritech Limited, emphasized the need for a collaborative approach to integrate AI into farming practices effectively. He noted that while AI holds immense promise, its success depends on addressing the practical challenges faced by farmers and ensuring that these technologies are accessible and reliable.