Abstract: 

Although initially a slow adopter of machine learning and computer vision, agriculture has become an important domain for these approaches. Computer vision is now a key element of agricultural systems to determine crop type, count plants, guide harvesting robots, identify issues like crop stress and weeds, and forecast yield. Adoption and extension of these approaches is critical due to the challenges facing global agriculture: the world's population is predicted to reach 9.7billion by 2050, water supply is expected to fall 40% short of global needs by 2030, and climate change produces significant challenges and uncertainty.

Fortunately advances in deep learning and remote sensing technologies have unlocked unprecedented opportunities for precision agriculture. However, challenges remain in leveraging common SOTA approaches, which are often developed for natural scene imagery like Imagenet, with remote sensing data; this data may be massive, spatiotemporal, multispectral, contain very small objects, and possess fundamentally different statistics than natural scene images.

In this session we’ll explore some of these challenges around remote sensing data for precision agriculture and approaches for addressing them including spatiotemporal modeling, self-supervised and contrastive learning, and multi-task learning within a deep learning framework.

Bio: 

Jennifer Hobbs is the Director of Machine Learning at Intelinair. Her team is responsible for the development and delivery of computer vision and machine learning models to deliver intelligence and insights to the agriculture industry. She completed her PhD in Physics and Astronomy at Northwestern University. Throughout her career she has been involved in all phases of the machine learning lifecycle, transforming raw data into compelling technology products through data modeling and architecture, pipeline design and management, machine learning, and visualization.