Farmers in climate-vulnerable regions are increasingly burdened by erratic weather, rising input costs, pest outbreaks and declining yields. These compounding challenges reduce profitability and resilience, especially for smallholders. To address these constraints, Agripilot.ai deployed a suite of AI-driven tools integrated through Microsoft’s Azure Data Manager for Agriculture (FarmBeats) platform. The solution combined real-time sensor data, AI-powered satellite insights and smart advisory tools. FarmVibes.ai enabled IoT-based monitoring of soil and crop conditions, while Azure OpenAI powered farmer-facing chatbots to deliver localized advisories. Together, these tools supported data-informed decisions on irrigation, nutrient application, pest control and harvesting. This integrated approach empowered farmers and agricultural professionals with actionable intelligence, enabling more precise and efficient farm management. As a result, crop yields increased by 40%, labour costs were reduced by 35% and water usage decreased by 30%. Farmers also reported significant reductions in input costs, contributing to stronger profitability and long-term sustainability. The solution curbed water waste, soil degradation and chemical overuse.CASE STUDY 9 Convergence of technologies for precision management of sugar cane – Microsoft 4. Rising food demand and market mismatch: Global food demand is projected to increase substantially due to population growth, urbanization and shifting dietary preferences. However, agricultural markets face persistent mismatches between what is produced and what is demanded, both from a quantity and a quality perspective. Addressing this challenge requires technologies that can boost productivity sustainably while supporting better crop planning. Use case 9: AI-enabled macro crop planning Technology convergence Machine learning, generative AI and satellite-enabled remote sensing Description AI-enabled macro crop planning systems support governments, agri-businesses and food-supply actors in making data-driven decisions on crops to be grown in different regions. The recommendations are aligned with projected demand, agro-ecological conditions and resource availability. These systems analyse large-scale datasets, including historical yield patterns, market signals, climate forecasts and land suitability maps to generate adaptive crop plans across regions or nations. This helps reduce mismatches between production and market demand, optimize input use and enhance food security planning. Shaping the Deep-Tech Revolution in Agriculture 32

Shaping the Deep Tech Revolution in Agriculture 2025