agribusinessreview.comOCTOBER 20258 opinionIN MYPREPARING SEED R&D FOR A DECADE OF AI, AUTOMATION, AND ACCELERATED GENETIC GAINBy Judith Rivera, Global Applied Technologies Optimization Leader, SyngentaIn modern seed R&D, operations span controlled environments, cutting edge labs and field trialing. From double haploid production and genotyping to genome editing and phenotyping, success depends on high-stakes, interdependent steps. Over the years, digital tools have improved data traceability and accuracy at many stages. Yet the most transformative phase is beginning. The decade ahead will be defined not by isolated solutions, but by seamless integration of intelligent systems.AI, robotics, IoT, machine learning and visualization will no longer be peripheral tools. They will become the backbone of unified, data-rich R&D pipelines. But to harness their potential, organizations go beyond technology. It requires discipline, training and commitment to ethical, human-centered design, enhancing rather than replacing the expertise of skilled professionals.The Horizon: Integration Over InventionWe're no longer at the threshold of AI and automation, they're already a part of seed R&D. Autonomous systems handle key logistics, advanced sensors monitor environments and machine learning models analyze vast volumes of data. Yet these tools often operate in isolation.What lies ahead is the integration of these systems into coherent, intelligent ecosystems.The next decade will focus on building infrastructure where genotyping data, environmental conditions, equipment telemetry and trial results communicate in real-time. Scientists, breeders and managers will make decisions powered by feedback loops, augmented analytics and predictive modeling.Rather than automation replacing people, the opportunity is human-machine collaboration, technicians using AI to spot anomalies before failure and researchers using models to simulate thousands of trial conditions before planting. Getting there demands integrated platforms, robust change management, and a shared understanding of data as progress.Core Technologies Driving ChangeAutonomous Systems & RoboticsFarming and horticultural equipment, vehicles, and imaging platforms already manage repetitive tasks such as labeling, sowing, sampling, and transporting material. These systems are already evolving from isolated tool-assisted operations to interconnected autonomous networks, with advanced models now operating independently using AI, GPS and sensor integration.IoT Networks for Real-Time AwarenessIoT devices like environmental probes, automated traps and stress sensors offer continuous situational awareness. As noted in Clemson University's Agricultural IoT Research, "integrated sensor networks are the gateway to autonomous optimization of crop systems," especially when linked with AI algorithms to close decision feedback loops in real time.Judith Rivera
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