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 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 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…
Special Session in “Deep Learning for Crop Science”
More information at: https://sites.google.com/alumni.imtlucca.it/deep-learning-for-crop-science
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.
From January 18th to February 2nd, I spent two weeks in Debre Zeyt, Ethiopia, as part of my task for the BBSRC GCF project I am currently working on (further details at http://chickpearoots.org). We provided in-situ support to build a rhizobox imaging station, replicating the setup we had built in Edinburgh. It has been an incredible experience, from the professional and personal point of view. The take-away thought was: international cooperation is important in collaborative research.