Massimo Minervini

1.5k total citations
19 papers, 874 citations indexed

About

Massimo Minervini is a scholar working on Plant Science, Computer Vision and Pattern Recognition and Ecology. According to data from OpenAlex, Massimo Minervini has authored 19 papers receiving a total of 874 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Plant Science, 6 papers in Computer Vision and Pattern Recognition and 5 papers in Ecology. Recurrent topics in Massimo Minervini's work include Smart Agriculture and AI (10 papers), Remote Sensing in Agriculture (5 papers) and Remote Sensing and LiDAR Applications (4 papers). Massimo Minervini is often cited by papers focused on Smart Agriculture and AI (10 papers), Remote Sensing in Agriculture (5 papers) and Remote Sensing and LiDAR Applications (4 papers). Massimo Minervini collaborates with scholars based in Italy, United Kingdom and Germany. Massimo Minervini's co-authors include Sotirios A. Tsaftaris, Hanno Scharr, Andreas Fischbach, Mario Valerio Giuffrida, Mohammed M. Abdelsamea, Pierdomenico Perata, Xi Yin, Jean-Michel Pape, Imanol Luengo and Andrew P. French and has published in prestigious journals such as The Plant Journal, IEEE Signal Processing Magazine and Pattern Recognition Letters.

In The Last Decade

Massimo Minervini

17 papers receiving 837 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Massimo Minervini Italy 9 731 432 161 110 97 19 874
Pouria Sadeghi‐Tehran United Kingdom 12 633 0.9× 374 0.9× 128 0.8× 168 1.5× 152 1.6× 24 924
Mads Dyrmann Denmark 15 969 1.3× 422 1.0× 126 0.8× 254 2.3× 68 0.7× 32 1.2k
Simon Madec France 11 788 1.1× 575 1.3× 302 1.9× 201 1.8× 94 1.0× 16 1.0k
G.W.A.M. van der Heijden Netherlands 18 620 0.8× 259 0.6× 106 0.7× 378 3.4× 45 0.5× 54 1.0k
Henrik Skov Midtiby Denmark 14 688 0.9× 248 0.6× 57 0.4× 213 1.9× 59 0.6× 36 1.0k
Dong Liang China 19 792 1.1× 367 0.8× 169 1.0× 322 2.9× 184 1.9× 50 1.2k
Gensheng Hu China 18 947 1.3× 291 0.7× 134 0.8× 412 3.7× 116 1.2× 53 1.3k
Xanthoula Eirini Pantazi Greece 14 800 1.1× 362 0.8× 143 0.9× 297 2.7× 32 0.3× 28 1.1k
Everton Castel�ão Tetila Brazil 9 463 0.6× 252 0.6× 176 1.1× 124 1.1× 77 0.8× 19 712
Chengquan Zhou China 23 818 1.1× 992 2.3× 550 3.4× 311 2.8× 64 0.7× 39 1.5k

Countries citing papers authored by Massimo Minervini

Since Specialization
Citations

This map shows the geographic impact of Massimo Minervini's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Massimo Minervini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Massimo Minervini more than expected).

Fields of papers citing papers by Massimo Minervini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Massimo Minervini. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Massimo Minervini. The network helps show where Massimo Minervini may publish in the future.

Co-authorship network of co-authors of Massimo Minervini

This figure shows the co-authorship network connecting the top 25 collaborators of Massimo Minervini. A scholar is included among the top collaborators of Massimo Minervini based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Massimo Minervini. Massimo Minervini is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Gaiotti, Federica, et al.. (2024). Vineyard water status assessment using a new 3D-video-sensor-system. Acta Horticulturae. 393–400.
2.
Belfiore, Nicola Pio, et al.. (2022). A novel system for assessing kiwifruit water status by AI and 3D image analysis. Acta Horticulturae. 231–238.
3.
Minervini, Massimo, Mario Valerio Giuffrida, Pierdomenico Perata, & Sotirios A. Tsaftaris. (2017). Phenotiki: an open software and hardware platform for affordable and easy image‐based phenotyping of rosette‐shaped plants. The Plant Journal. 90(1). 204–216. 81 indexed citations
4.
Scharr, Hanno, Massimo Minervini, Andrew P. French, et al.. (2015). Leaf segmentation in plant phenotyping: a collation study. Machine Vision and Applications. 27(4). 585–606. 195 indexed citations
5.
Minervini, Massimo, Hanno Scharr, & Sotirios A. Tsaftaris. (2015). The significance of image compression in plant phenotyping applications. Functional Plant Biology. 42(10). 971–971. 7 indexed citations
6.
Giuffrida, Mario Valerio, Massimo Minervini, & Sotirios A. Tsaftaris. (2015). Learning to Count Leaves in Rosette Plants. Research Output (Edinburgh Napier University). 1.1–1.13. 65 indexed citations
7.
Minervini, Massimo & Sotirios A. Tsaftaris. (2015). Classification-aware distortion metric for HEVC intra coding. 30. 1–4. 1 indexed citations
8.
Minervini, Massimo, Hanno Scharr, & Sotirios A. Tsaftaris. (2015). Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner]. IEEE Signal Processing Magazine. 32(4). 126–131. 172 indexed citations
9.
Minervini, Massimo, Andreas Fischbach, Hanno Scharr, & Sotirios A. Tsaftaris. (2015). Finely-grained annotated datasets for image-based plant phenotyping. Pattern Recognition Letters. 81. 80–89. 186 indexed citations
10.
Minervini, Massimo, Mario Valerio Giuffrida, & Sotirios A. Tsaftaris. (2015). An interactive tool for semi-automated leaf annotation. Research Output (Edinburgh Napier University). 6.1–6.13. 18 indexed citations
11.
Scharr, Hanno, Massimo Minervini, Andreas Fischbach, & Sotirios A. Tsaftaris. (2014). Annotated Image Datasets of Rosette Plants. 34 indexed citations
12.
Minervini, Massimo, Cristian Rusu, Mario Damiano, et al.. (2014). Large-scale analysis of neuroimaging data on commercial clouds with content-aware resource allocation strategies. The International Journal of High Performance Computing Applications. 29(4). 473–488. 4 indexed citations
13.
Minervini, Massimo, Cristian Rusu, & Sotirios A. Tsaftaris. (2014). Unsupervised and supervised approaches to color space transformation for image coding. NORMA. 3. 5576–5580. 4 indexed citations
14.
Minervini, Massimo, Cristian Rusu, & Sotirios A. Tsaftaris. (2013). Learning computationally efficient approximations of complex image segmentation metrics. NORMA. 60–65. 3 indexed citations
15.
Minervini, Massimo, Mohammed M. Abdelsamea, & Sotirios A. Tsaftaris. (2013). Image-based plant phenotyping with incremental learning and active contours. Ecological Informatics. 23. 35–48. 91 indexed citations
16.
Minervini, Massimo & Sotirios A. Tsaftaris. (2013). Application-aware image compression for low cost and distributed plant phenotyping. 1–6. 9 indexed citations
17.
Casalino, Gabriella, Nicoletta Del Buono, & Massimo Minervini. (2012). Nonnegative Matrix Factorizations Performing Object Detection and Localization. Applied Computational Intelligence and Soft Computing. 2012. 1–19. 1 indexed citations
18.
Minervini, Massimo, Mario Damiano, Valter Tucci, et al.. (2012). Mouse neuroimaging phenotyping in the cloud. 6853. 55–60. 2 indexed citations
19.
Minervini, Massimo, et al.. (1980). [Individual characteristics and biological rhythms. I. Morning and evening subject typing based on cluster analysis].. PubMed. 56(20). 2090–6. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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