The global AI market, valued at $136 billion in 2022, is projected to grow more than 13 times in just eight years, reaching $1.8 trillion by 2030.1 In agriculture, as in other sectors, AI’s growing use has started delivering results, including in emerging countries in the Global South. For instance, a recent pilot project in Telangana (Saagu Bagu),2 conducted in collaboration with the World Economic Forum and the Government of Telangana, provided four AI- enabled applications to around 7,000 chilli-growing smallholder farmers.3 After just one season, the farmers reported a 21% improvement in yields, an 11% increase in the unit prices their produce commanded and a 9% reduction in the fertilizers and pesticides they needed to use. Consequently, these farmers were able to significantly boost their net profits, earning an average of $800 more per acre per crop season – an impressive increase considering Indian farmers’ average annual income is less than $1,500.4 AI-based sowing advice has proven transformative in the Indian state of Andhra Pradesh, helping farmers boost yields by up to 30%. Similarly, AI-enabled pest- detection models have empowered around 3,000 farmers in the state to forecast and address pest infestations proactively.5 By delivering data-driven predictions and augmenting traditional farming, AI creates opportunities for farmers to enhance their resilience, productivity and profitability. For instance, precision agricultural solutions address farmers’ resource constraints by offering best practices to maximize output with optimized input. Similarly, predictive analytics reduce climate vulnerability by providing weather forecasts and helping to manage pest infestations before they scale. Furthermore, AI-enabled market intelligence systems help alleviate the economic pressures faced by farmers, such as rising input costs or fluctuating market prices, by bridging information gaps and providing timely, data-driven insights for better decision-making. Additionally, at a macro level, AI has the potential to curb emissions from the agricultural sector by optimizing resource use to allow economies to reach their net-zero goals, while also enhancing agricultural GDP . In the Indian context, use cases for AI in agriculture can help address systemic challenges in agriculture such as lower productivity in certain crops compared to global averages, restricted finance for smallholder farmers, increasing soil- degradation and high pre- and post-harvest losses. If implemented at scale, AI could be a critical tool to boost the agricultural sector’s contribution to GDP , which is currently at 18% while employing close to 42% of the country’s population.6While the AI-driven transformation of agriculture may have begun, deploying technology at scale is not easy. Its use is still limited in agriculture: according to experts, fewer than 20% of Indian farmers use digital technologies, which are a superset of AI-enabled solutions. There are several reasons for this low rate of adoption. For instance, the low income of Indian farmers (around $1,500 annually)7 restricts both their ability and willingness to pay for AI solutions. Without financing support, technology interventions are perceived to be an added burden, given the already increasing cost of cultivation. Additionally, close to 85% of India’s 150 million farmers are smallholders and the Indian farmer’s average landholding is just 1.08 hectares (about 2.67 acres). With such small landholdings, which are often fragmented, the cost of delivering AI solutions in rural settings increases tremendously. This pivots solution providers to focus primarily on larger farmers or businesses. On the supply side, the development and use of AI solutions relies on the collection of large volumes of data, often in real time, which needs investment in infrastructure and resources, and this drives up the cost of AI development. This indirectly increases the cost of services, further affecting their affordability. Additionally, there are very limited institutional mechanisms for validating technology before it is deployed, increasing the perceived risk of adoption.The potential of AI in agriculture Global AI trends: A growing opportunity $1.8 trillion Expected global market size for AI by 203040% Growth rate of AI markets in India between 2020 and 2025 1,500 Estimated number of agritech start-ups in India$65 billion Estimated value potential unlocked in India by investing in 15 foundational agricultural datasets AI creates opportunities for farmers to enhance their resilience, productivity and profitability. Future Farming in India 8

Future Farming in India 2025