Shaping the Deep Tech Revolution in Agriculture 2025

Integration of robotics: Before and after FIGURE 10 Drivers and barriers to the use of robotics for agriculture FIGURE 11 Source: Consultations with AI4AI community experts AFTER A large farm covering many hectar es relies heavily on seasonal labour for planting, weeding and harvesting. Labour shortages, rising wages and unpr edictable worker availability make it difficult to manage peak demand seasons. Tasks such as weeding and pr ecision spraying ar e time- consuming and often done manually or with br oad-scale machinery that wastes inputs and takes longer . Crop monitoring r elies on farm workers scouting fields, leading to inconsistent coverage and delayed detection of pests or diseases. Overall, efficiency and consistency vary widely acr oss fields.By relying on fleets of small, lightweight autonomous r obots that work collaboratively as a swarm, the farm has of fset some of its labour -related risks. These r obots handle weeding, targeted spraying and harvesting in parallel acr oss multiple fields. When working in swarms, r obots shar e real- time data, adapting r outes and tasks dynamically to changing field conditions. Continuous monitoring also enables early pest or disease detection, triggering pr ecision tr eatments only wher e needed. Labour dependency dr ops and input use is optimized, making the entir e operation mor e resilient, scalable and cost-ef fective.BEFORE Advances in AI-enabled perception: Breakthr oughs in computer vision and sensor fusion now allow r obots to r eliably distinguish cr ops fr om weeds, assess cr op maturity and navigate complex field envir onments. This has incr eased their applicability for agricultur e. Seamless cloud–edge integration: Robust edge-computing platforms and cloud-based coor dination ar e making it possible for r obots to take decisions in r eal time. Additionally , advances in connectivity allow autonomous r obots to work as swarms, impr oving their pr ecision and making them more applicable to larger farms. High initial costs: The initial cost of deploying r obotics at the farm level is extremely high and may be af fordable only for large commer cial farms. Additionally , operational costs such as softwar e and maintenance can deter adoption. Complex operational environments: Agricultural farms may often be unstructur ed in comparison to industrial units and this may ther efore limit autonomous decision-making. Similarly , terrains may limit the autonomous movement of r obots. Diverse requirements of end-effectors: Unlike the mechanical objects often handled by industrial r obots, agricultural pr oduce is often delicate and irr egular in shape and size. This r equir es agricultural r obots to have diverse end-ef fectors (devices that r obots need to interact with objects) and significantly af fects the adaptability of r obotics in agricultural activities. DRI VERS BARRIER S Shaping the Deep-Tech Revolution in Agriculture 19