Shaping the Deep Tech Revolution in Agriculture 2025

The following section highlights how solving specific challenges requires a convergence of tech domains that translate into promising use cases. Each use case has been primarily mapped to the challenge where its contribution is likely to be most significant, although it may have broader cross-cutting impact. 1. Shifting workforce dynamics, resulting in lower human capital for agriculture To address declining human capital in agriculture, use cases will need to supplement manual labour with automation to enhance operational efficiency. Replacing repetitive manual tasks with automation will also build resilience against future labour shortages.4.1 Breakthrough use cases Labour shortages in the agricultural sector can affect farmers’ livelihoods and food security. To address this, Infosys, in collaboration with Schmiede.one and FIR (a research institute focused on industrial management) at the Rhenish-Westphalian Technical University in Aachen (RWTH), developed a “5G.NATURAL” programme. This programme focuses on creating a scalable, modular and intelligent swarm system of autonomous agricultural machines, specifically for harvesting. The swarm system aims to ensure economic and sustainable harvesting by using data and transmission between robots for coordinated action on the ground. The core technology enabling this solution is robotics based on a reliable 5G system designed for rural areas. The 5G technology provides the necessary connectivity for autonomous machinery. The programme is currently being piloted in Germany. Swarm robotics can significantly offset labour risks while improving farm operational efficiency. CASE STUDY 5 Autonomous robotics for harvesting – Infosys Use case 1: Task-specific swarm robotics Technology convergence Robotics, computer vision, edge IoT and 5G connectivity or mesh networks (a decentralized network that enables data transmission and communication between nodes of a system) Description The use case involves a cohort of field robots that work in coordinated groups to handle repetitive agricultural tasks such as precision weeding and selective harvesting. By deploying many small, intelligent machines, farmers can efficiently cover larger areas, adapt to diverse crop conditions and reduce soil compaction. This use can augment manual labour and enhance operational resilience in the light of labour shortages. Shaping the Deep-Tech Revolution in Agriculture 26