Abstract
Abstract One of the grand challenges of our generation is to get ready to feed 9 billion people by 2050 with sustainable use of environmental resources. However, our current agricultural system is not prepared for it. We are facing unprecedented challenges in adopting sustainable management practices, increasing production, and coping with pest and climate stressors that threaten yield; while running a profitable farm operation. In this talk, I will discuss our vision of a new cyber-plant-agricultural system that leads to an ultra-precision technology to monitor and manage plants or small plots at an individual level. To realize this grand vision, machine learning (ML) will play a large role. However, several key aspects of ML such as robustness, interpretability and data requirement need to be studied in the context of agriculture for successful deployment. In addition, high-throughput and effective plant phenotyping systems need to assimilate heterogeneous, multi-modal data for decision-making and should be suitable for distributed implementation for enhanced scalability. I will discuss a few success stories from our recent work to discuss these aspects of ML in plant agriculture that is relevant and potentially beneficial for the animal agriculture community.
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