Massimo Minervini

Massimo Minervini photo

Short Bio

Currently, I work as computer vision engineer at CET Electronics, where I develop innovative solutions for precision agriculture.

Previously, I have been a guest scholar at IMT School for Advanced Studies, Lucca, Italy, having been therein formerly a post-doctoral research fellow of the Pattern Recognition and Image Analysis (PRIAn) unit. From IMT Lucca I also received my Ph.D. in Computer Science and Engineering in July 2015 (Advisor: Sotirios A. Tsaftaris). My doctoral dissertation entitled “Application-Aware Image Compression and Sensing Platform for Plant Phenotyping.” My main interests are in computer vision, machine learning, image and video compression, and life science applications.

I have pursued my research activity at IMT within the EU FP7 funded project PHIDIAS (Phenotyping with a High-throughput, Intelligent, Distributed, and Interactive Analysis System). My research work was focused on the design and implementation of a low cost sensing device for affordable image-based plant phenotyping, and the development of application-aware compression algorithms that can operate on resource constrained devices.

The research efforts in affordable image-based plant phenotyping have led to the development of the Phenotiki platform. Find out more on

Between 2013 and 2014, I spent a few months in Jülich, Germany, for a research internship at the Institute of Plant Sciences (IBG-2) of the Forschungszentrum Jülich, supervised by Dr. Hanno Scharr.

Before the Ph.D., I graduated at the University of Bari, where I received the M.Sc. degree in Computer Science in 2010, the topic of my thesis being fuzzy logic for user profiling in recommender systems, under the supervision of Prof. Corrado Mencar. From the University of Bari I also received the B.Sc. degree in Computer Science in 2008, with a thesis related to text mining and natural language processing, under the supervision of Prof. Giovanni Semeraro.

My non-academic interests include juggling, hand balancing, and rock climbing.

Follow me on: Google Scholar, LinkedIn, and ResearchGate.

A recent version of my CV is available in English or in Italian.



February 1, 2017
I started working at CET Electronics.

October 21, 2016
I gave a lecture on image acquisition and storage at the University of Padova, Italy, as part of the course entitled "Interpretazione di immagini telerilevate per l’analisi fenomica delle piante" of the GIScience master.

October 20, 2016
I have been invited by Prof. Antonio Masi to give a research seminar at the Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE) of the University of Padova, Italy.

September 5-6, 2016
Keynote speaker at the 5th International Workshop on Image Analysis Methods for the Plant Sciences (IAMPS 2016) in Angers, France.

February 18, 2016
Invited speaker at the ITA-PPN workshop in Rome, Italy, to present the challenges and opportunities of affordable phenotyping.



  1. M. Minervini, M. V. Giuffrida, P. Perata, S. A. Tsaftaris, “Phenotiki: an open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants,” The Plant Journal, vol. 90, no. 1, pp. 204–216, Apr. 2017. [PDF][Online][Website]
  2. S. A. Tsaftaris, M. Minervini, H. Scharr, “Machine learning for plant phenotyping needs image processing,” Trends in Plant Science, vol. 21, no. 12, pp. 989–991, Dec. 2016. [PDF][Online]
  3. M. Minervini, A. Fischbach, H. Scharr, S. A. Tsaftaris, “Finely-grained annotated datasets for image-based plant phenotyping,” Pattern Recognition Letters, vol. 81, pp. 80–89, Oct. 2016. [PDF][Online][Website]
  4. H. Scharr, M. Minervini, A. P. French, C. Klukas, D. M. Kramer, X. Liu, I. Luengo, J.-M. Pape, G. Polder, D. Vukadinovic, X. Yin, S. A. Tsaftaris, “Leaf segmentation in plant phenotyping: a collation study,” Machine Vision and Applications, vol. 27, no. 4, pp. 585–606, May 2016. [PDF][Online]
  5. M. Minervini, S. A. Tsaftaris, “Classification-aware distortion metric for HEVC intra coding,” in International Conference on Visual Communications and Image Processing (VCIP), Singapore, Dec. 2015. [PDF][Online]
  6. M. Minervini, C. Rusu, M. Damiano, V. Tucci, A. Bifone, A. Gozzi, S. A. Tsaftaris, “Large-scale analysis of neuroimaging data on commercial clouds with content-aware resource allocation strategies,” International Journal of High Performance Computing Applications, vol. 29, no. 4, pp. 473–488, Nov. 2015. [PDF][Online]
  7. M. Minervini, H. Scharr, S. A. Tsaftaris, “The significance of image compression in plant phenotyping applications,” Functional Plant Biology, vol. 42, no. 10, pp. 971–988, Sep. 2015. [PDF][Online]
  8. M. Minervini, M. V. Giuffrida, S. A. Tsaftaris, “An interactive tool for semi-automated leaf annotation,” in Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP) Workshop, pp. 6.1–6.13. BMVA Press, Sep. 2015. [PDF][Online]
  9. M. V. Giuffrida, M. Minervini, S. A. Tsaftaris, “Learning to Count Leaves in Rosette Plants,” in Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP) Workshop, pp. 1.1–1.13. BMVA Press, Sep. 2015. [PDF][Online]
  10. M. Minervini, C. Rusu, S. A. Tsaftaris, “Computationally efficient data and application driven color transforms for the compression and enhancement of images and video,” in Color Image and Video Enhancement, pp. 371–393. Springer, 2015, ch. 13. [PDF][Online]
  11. M. Minervini, H. Scharr, S. A. Tsaftaris, “Image analysis: The new bottleneck in plant phenotyping,” IEEE Signal Processing Magazine, vol. 32, no. 4, pp. 126–131, Jul. 2015. [PDF][Online]
  12. M. Minervini, C. Rusu, S. A. Tsaftaris, “Unsupervised and Supervised Approaches to Color Space Transformation for Image Coding,” in 21st International Conference on Image Processing (ICIP), Paris, France, Oct. 2014, pp. 5576–5580. [PDF][Poster][Online]
  13. M. Minervini, M. M. Abdelsamea, S. A. Tsaftaris, “Image-based plant phenotyping with incremental learning and active contours,” Ecological Informatics, vol. 23, pp. 35–48, Sep. 2014. [PDF][Online]
  14. H. Scharr, M. Minervini, A. Fischbach, S. A. Tsaftaris, “Annotated Image Datasets of Rosette Plants,” Forschungszentrum Jülich GmbH, Jülich, Germany, Tech. Rep. FZJ-2014-03837, Jul. 2014. [PDF][Online]
  15. M. Minervini, C. Rusu, S. A. Tsaftaris, “Learning Computationally Efficient Approximations of Complex Image Segmentation Metrics,” in 8th International Symposium on Image and Signal Processing and Analysis (ISPA), Trieste, Italy, Sep. 2013, pp. 60–65. [PDF][Online]
  16. M. Minervini, S. A. Tsaftaris, “Application-Aware Image Compression for Low Cost and Distributed Plant Phenotyping,” in 18th International Conference on Digital Signal Processing (DSP), Santorini, Greece, Jul. 2013, pp. 1–6. [PDF][Online]
  17. M. Minervini, M. Damiano, V. Tucci, A. Bifone, A. Gozzi, S. A. Tsaftaris, “Mouse Neuroimaging Phenotyping in the Cloud,” in 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA), Istanbul, Turkey, Oct. 2012, pp. 55–60. [PDF][Online]
  18. G. Casalino, N. Del Buono, M. Minervini, “Nonnegative Matrix Factorizations Performing Object Detection and Localization,” Applied Computational Intelligence and Soft Computing, vol. 2012, Jan. 2012. [PDF][Online]
  19. D. Dell’Agnello, A. M. Fanelli, C. Mencar, M. Minervini, “Serendipitous Fuzzy Item Recommendation with ProfileMatcher,” in Fuzzy Logic and Applications, vol. 6857, pp. 220–227, Aug. 2011. [PDF][Online]
  20. M. Minervini, “Serendipity Injection in a Fuzzy Recommender System,” in Abstract Booklet of the First AI*IA Doctoral Consortium, Brescia, Italy, Dec. 2010.
  21. M. Minervini, N. Del Buono, “Nonnegative matrix factorizations performing object detection,” Department of Mathematics, University of Bari, Bari, Italy, Tech. Rep. 14/10, Jul. 2010. [Online]


Research Interests

  • Computer vision
  • Image and video compression
  • Image processing and analysis
  • Machine learning and pattern recognition
  • Life science applications
  • Plant phenotyping
  • Cloud computing
  • Fuzzy logic