Can AI solve the yield gap in Indian agriculture?

India Today

2 days ago

Despite advancements in agriculture, India continues to grapple with a significant yield gap, with production levels for key crops such as rice and wheat lagging behind their potential. Recently, artificial intelligence (AI) has emerged as a promising solution to this issue, offering data-driven insights that could transform farming practices.AI technology offers solutions across various stages of farming. In the pre-sowing phase, AI can assess soil health and recommend crops suited to specific climatic conditions, increasing the likelihood of successful harvests. During cultivation, AI-driven sensors and drones monitor plant health, enabling precision disease management and optimal use of resources like fertilisers and water. These technologies not only enhance productivity but also contribute to sustainable farming practices by reducing waste and improving input efficiency.advertisementIndiatoday spoke with M.K Dhanuka, Chairman, Dhanuka Agritech Limited to get more insights.
Post-harvest, AI aids in forecasting market demand and optimising logistics, thereby reducing storage wastage and enhancing farmer earnings. Initiatives in Andhra Pradesh, for example, have shown that AI-powered apps can increase crop yields by up to 30%, demonstrating the technology's potential impact. Such advancements highlight the transformative power of AI in addressing post-harvest inefficiencies and improving the overall agricultural supply chain.The Indian government is actively promoting AI in agriculture through the Digital Agriculture Mission (2021–2025). This initiative highlights the role of AI as a key enabler of data-driven farming, encouraging public-private partnerships to integrate AI into existing agricultural systems. However, widespread adoption faces significant hurdles.A major challenge is the availability and quality of data. AI models require robust datasets, yet India's farm-level data often lacks the necessary depth or is outdated. Furthermore, inadequate digital infrastructure, such as poor internet connectivity and low smartphone penetration, hampers scalability. These infrastructural challenges must be addressed to ensure that AI technologies can be effectively deployed across rural areas.Cost is another barrier, with many smallholder farmers unable to afford AI tools unless subsidised. Additionally, a lack of trust persists, as farmers are cautious about adopting AI advice over traditional practices, especially when there is no communication in local languages or demonstrable effectiveness. Building trust through transparent communication and demonstrable results is essential for fostering acceptance among farmers.To harness AI's full potential, experts suggest investing in open-source agri-data platforms and enhancing rural digital infrastructure. Providing AI tools through cooperatives or farmer producer organisations (FPOs) could improve access for small farmers. Training FPOs to facilitate technology adoption and localising AI platforms could also drive greater uptake. These strategies could bridge the gap between technology and its practical application in the field.Ultimately, AI holds the promise of enabling Indian farmers by providing timely information and optimising resource use. While the yield gap is a formidable challenge, the right combination of technology, policy, and collaboration could pave the way for 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.- Ends