[Expired] Fully-funded PhD scholarship [UK applicants]

Image-based plant analysis for sustainable agriculture Image-based plant phenotyping allows the quantification of traits (e.g. leaf area, number, colour) reflecting plant performance non-destructively. This approach helps in deciphering the complex genetic-environment interactions influencing its performance and finding the best-adapted crop varieties to local environmental constraints for sustainable agriculture. Deep learning Read more…

[EXPIRED] Fully Funded PhD Scholarship

I am looking for a PhD student for the 3-year project entitled “Semi-Supervised, Unsupervised, and Self-Supervised Transfer Learning for Computer Vision”. Transfer learning has been demonstrated to improve the generalisation of deep neural networks to real-world applications, especially in situations where labelled training sets are missing. Current proposed methods work Read more…

Research Topic “Synthetic Data for Computer Vision in Agriculture”

I am pleased to communicate that the research topic (special issue) “Synthetic Data for Computer Vision in Agriculture” is now open for submission. This research topic is in the Frontiers in Plant Science journal (Impact Factor 5.7). More information is available at https://www.frontiersin.org/research-topics/29199/synthetic-data-for-computer-vision-in-agriculture. You are invited to submit your original Read more…

Research Topic “Computer Vision in Plant Phenotyping and Agriculture” in Frontiers in Plant Science

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…