Toward a tipping point in federated learning in healthcare and life sciences.

We discuss the real-world application of federated learning (FL) in the healthcare and life sciences industry, noting a tipping point in its adoption beyond academia. Sharing our experiences with multi-hospital and multi-pharma collaborations, we highlight the importance of involving key stakeholders to develop production-grade FL solutions that are fully compliant with stringent privacy and security standards.

A photometric stereo-based 3D imaging system using computer vision and deep learning for tracking plant growth.

Background: Tracking and predicting the growth performance of plants in different environments is critical for predicting the impact of global climate change. Automated approaches for image capture and analysis have allowed for substantial increases in the throughput of quantitative growth trait measurements compared with manual assessments. Recent work has focused on adopting computer vision and machine learning approaches to improve the accuracy of automated plant phenotyping.

Innovative 3D and 2D machine vision methods for analysis of plants and crops in the field.

Machine vision systems offer great potential for automating crop control, harvesting, fruit picking, and a range of other agricultural tasks. However, most of the reported research on machine vision in agriculture involves a 2D approach, where the utility of the resulting data is often limited by effects such as parallax, perspective, occlusion and changes in background light - particularly when operating in the field. The 3D approach to plant and crop analysis described in this paper offers potential to obviate many of these difficulties by utilising the richer information that 3D data can generate.