Valerio obtained his PhD in Image Analysis at IMT School for Advanced Studies Lucca (Italy) in the 2018, with a thesis entitled “Learning to Count Leaves of Plants”. He has been engaged in several projects related to non-destructive image-based plant phenotyping. He proposed the first award-winning machine learning approach to predict the number of leaves of rosette plants, presented at Computer Vision Problems in Plant Phenotyping Workshop held in conjunction with the BMVC 2015. This algorithm was also included in the Phenotiki Analysis Software (http://phenotiki.com), software also developed in collaboration with him and has been downloaded more than 100 times. His expertise includes deep learning, publishing several papers related to deep leaf counting , , plant image synthesis , and domain adaptation applied plant phenotyping . Several of these works were the first real applications in plant phenotyping. He also collaborated for the BBSRC project BB/P023487/1 as PDRA at the University of Edinburgh, for which he developed the Phenotiki Sensors and the analysis algorithms to extract root system architecture (RSA) traits.
His research interests are computer vision, machine learning, and deep learning applied to plant phenotyping and medical imaging applications. He also focuses in explainability and interpretability of deep networks, and transfer learning (domain adaptation).