After over two years of work, in collaboration with colleagues from the University of Edinburgh, we finally got our paper A conversational multi-agent AI system for automated plant phenotyping published on Nature Communications. It’s been a long journey, but we got there! Congrats to all the co-authors and collaborators who Read more
Plant phenotyping is the identification of effects on plant structure and function (the phenotype) resulting from genotypic differences (i.e., differences in the genetic code) and the environmental conditions a plant has been exposed to. Knowledge of plant phenotypes is a key ingredient of the knowledge-based bioeconomy, which not only literally Read more
This workshop plans to showcase the challenges raised by working on computer vision for plant phenotyping and agriculture. Workshop goals include demonstrating the state-of-the-art, identifying key unsolved problems, and introducing computer scientists with an interest in plant phenotyping to the field. Effective plant phenotyping is urgently needed to support the Read more
Machine learning (ML) has been recognized as central to artificial intelligence (AI) for many decades. The question of how the things that have been learned in one context can be re-used and adapted in other related contexts, however, has only been brought to the attention of the wider ML research Read more
I am delighted to inform you that I am one of the guest editors for the Special Issue “Acquire and Perceive: Novel Approaches for Imaging-based Plant Phenotyping” on Remote Sensing (MPDI).
I hope you all are doing well during this difficult time.
As a member of the Edinburgh Plant Science community, I have written a blog post on the importance of data and code sharing in research.
I hope it is of your interest: http://www.edinburghplantscience.co.uk/blog/science-benefitting-data-an…
My new journal paper “UNSUPERVISED ROTATION FACTORIZATION IN RESTRICTED BOLTZMANN MACHINES” is now available at HTTPS://IEEEXPLORE.IEEE.ORG/DOCUMENT/8870198. This paper shows a new method to learn rotation-invariant features on shallow neural network, without the necessity to use data augmentation. The paper shows mathematical and experimental proofs of its effectiveness.
The paper titled Phenotiki: An open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants has been accepted for publishing in The Plant Journal.