The agricultural sector faces growing pressures from labour shortages, rising input costs, climate variability and the imperative of sustainable resource management. This paper presents the design, implementation and evaluation of a “Robotic Agro-Analytics Platform” (RAAP) that integrates autonomous ground and aerial robotic vehicles, multi-modal sensing, data‐analytics and decision‐support tools to enable field automation and sustainable crop production. The platform utilises real-time sensor data (soil moisture, nutrient status, plant health indices, canopy structure) fused with geo-spatial and temporal analytics to generate actionable insights for precision agronomy. Autonomous robotic subsystems perform repeatable field operations (scouting, selective spraying, weeding, targeted irrigation) under the orchestration of a central analytics hub. Field experiments show that RAAP achieved reductions in input usage (water, fertiliser, herbicide) of ~ 18-25 %, while maintaining or improving yield by ~ 5-8 % compared to conventional practice. The system also delivered finer spatial resolution of field monitoring (grid size ~ 1 m²) enabling early detection of stress zones and pest-infestation patches. The proposed platform thus demonstrates how robotics plus analytics can contribute to sustainable field automation, reducing environmental impact, lowering labour dependency, and enabling data-driven agronomy. Keywords derived include robotics, agro-analytics, precision agriculture, field automation, sustainability. The wider implication is a step toward Agriculture 5. 0 ecosystems where autonomous machines, sensor networks and AI cooperate to support resilient and efficient farming.