Valerio Giuffrida
valerio giuffrida

Dr Valerio Giuffrida

Lecturer

 0131 455 2744

  V.Giuffrida@napier.ac.uk

  

Biography

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 [27], [30], plant image synthesis [31], and domain adaptation applied plant phenotyping [4]. 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).

School

Research Groups

Themes

Research Areas

Esteem

Invited Speaker

  • Computer Vision for Agriculture
  • Big Data in Society Meeting
  • Camera Trap Symposium
  • Presentation of the paper 'Explicit Factorization of Rotations in Restricted Boltzmann Machines'
  • Generating Images using Neural Networks

 

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20 results

Root Gap Correction with a Deep Inpainting Model

Conference Proceeding
Chen, H., Giuffrida, M. V., Doerner, P., & Tsaftaris, S. A. (2018)
Root Gap Correction with a Deep Inpainting Model
Imaging roots of growing plants in a non-invasive and affordable fashion has been a long-standing problem in image-assisted plant breeding and phenotyping. One of the most aff...

Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting

Journal Article
Giuffrida, M. V., Doerner, P., & Tsaftaris, S. A. (2018)
Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting. Plant Journal, 96(4), 880-890. https://doi.org/10.1111/tpj.14064
Direct observation of morphological plant traits is tedious and a bottleneck for high‐throughput phenotyping. Hence, interest in image‐based analysis is increasing, with the r...

Citizen crowds and experts: observer variability in image-based plant phenotyping

Journal Article
Giuffrida, M. V., Chen, F., Scharr, H., & Tsaftaris, S. A. (2018)
Citizen crowds and experts: observer variability in image-based plant phenotyping. Plant Methods, 14(1), https://doi.org/10.1186/s13007-018-0278-7
Background: Image-based plant phenotyping has become a powerful tool in unravelling genotype–environment interactions. The utilization of image analysis and machine learning h...

Leveraging multiple datasets for deep leaf counting

Conference Proceeding
Dobrescu, A., Giuffrida, M. V., & Tsaftaris, S. A. (2018)
Leveraging multiple datasets for deep leaf counting. https://doi.org/10.1109/ICCVW.2017.243
The number of leaves a plant has is one of the key traits (phenotypes) describing its development and growth. Here, we propose an automated, deep learning based approach for c...

ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network

Conference Proceeding
Giuffrida, M. V., Scharr, H., & Tsaftaris, S. A. (2017)
ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network. https://doi.org/10.1109/ICCVW.2017.242
In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems ,...

Phenotiki: an open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants

Journal Article
Minervini, M., Giuffrida, M. V., Perata, P., & Tsaftaris, S. A. (2017)
Phenotiki: an open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants. Plant Journal, 90(1), 204-216. https://doi.org/10.1111/tpj.13472
Phenotyping is important to understand plant biology, but current solutions are costly, not versatile or are difficult to deploy. To solve this problem, we present Phenotiki, ...

Whole Image Synthesis Using a Deep Encoder-Decoder Network

Conference Proceeding
Sevetlidis, V., Giuffrida, M. V., & Tsaftaris, S. A. (2016)
Whole Image Synthesis Using a Deep Encoder-Decoder Network. In Simulation and Synthesis in Medical Imaging. , (127-137). https://doi.org/10.1007/978-3-319-46630-9_13
The synthesis of medical images is an intensity transformation of a given modality in a way that represents an acquisition with a different modality (in the context of MRI thi...

On Blind Source Camera Identification

Conference Proceeding
Farinella, G. M., Giuffrida, M. V., Digiacomo, V., & Battiato, S. (2015)
On Blind Source Camera Identification. In Advanced Concepts for Intelligent Vision Systems. , (464-473). https://doi.org/10.1007/978-3-319-25903-1_40
An interesting and challenging problem in digital image forensics is the identification of the device used to acquire an image. Although the source imaging device can be retri...

An interactive tool for semi-automated leaf annotation

Conference Proceeding
Minervini, M., Giuffrida, M. V., & Tsaftaris, S. (2015)
An interactive tool for semi-automated leaf annotation. In Proceedings of the Computer Vision Problems in Plant Phenotyping Workshop 2015, (6.1-6.13). https://doi.org/10.5244/c.29.cvppp.6
High throughput plant phenotyping is emerging as a necessary step towards meeting agricultural demands of the future. Central to its success is the development of robust compu...

Chickpea (Cicer arietinum L.) root system architecture adaptation to initial soil moisture improves seed development in dry-down conditions

Working Paper
Bontpart, T., Robertson, I., Giuffrida, V., Concha, C., Scorza, L. C. T., McCormick, A. J., …Doerner, P. (2020)
Chickpea (Cicer arietinum L.) root system architecture adaptation to initial soil moisture improves seed development in dry-down conditions
Soil water deficit (WD) impacts vascular plant phenology, morpho-physiology, and reproduction. Chickpea, which is mainly grown in semi-arid areas, is a good model plant to dis...

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