Massimo Minervini

1.5k citations
21 papers · 956 · h-index 9

Impact in

    • Smart Agriculture and AI
    • Leaf Properties and Growth Measurement
    • Greenhouse Technology and Climate Control
  • Ecology top 5%
    • Remote Sensing in Agriculture

Papers in

Massimo Minervini

19 papers receiving 924 citations

Peers

Massimo Minervini
Comparison fields: 5 of 79
  • Plant Science 750
  • Ecology 418
  • Environmental Engineering 166
  • Analytical Chemistry 107
  • Ecological Modeling 31
Replace Henrik Skov Midtiby with:
Henrik Skov Midtiby Denmark
Nicolas Virlet United Kingdom
Simon Madec France
Kaihua Wu China
Pengcheng Hu China
Dong Liang China
Mario Valerio Giuffrida United Kingdom
Geng Bai United States
Michael Pflanz Germany
Massimo Minervini relative to Henrik Skov Midtiby Denmark Henrik Skov Midtiby's profile →
Citations per field
00.5×2.8×
Henrik Skov Midtiby · 1×
Citations per year

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-authors

The 25 scholars most cited alongside Massimo Minervini, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Massimo Minervini Line = papers co-authored together Massimo Minervini links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015211
2 2015209
3 2015181
4 201399
5 201785
6 201573
7
Annotated Image Datasets of Rosette Plants
201437
8 201518
9 201311
10 20157
11 20136
12 20145
13 20144
14 20124
15 20122
16 20111
17
[Individual characteristics and biological rhythms. I. Morning and evening subject typing based on cluster analysis].
19801
18 20151
19 20151
20 20240

About Massimo Minervini

Massimo Minervini is a scholar working on Plant Science, Computer Vision and Pattern Recognition, Ecology, Environmental Engineering and Molecular Biology, having authored 21 papers that have together received 956 indexed citations. Recurring topics across this work include Smart Agriculture and AI (10 papers), Remote Sensing in Agriculture (5 papers), Advanced Data Compression Techniques (4 papers), Remote Sensing and LiDAR Applications (4 papers), Image Retrieval and Classification Techniques (4 papers), Leaf Properties and Growth Measurement (3 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Cell Image Analysis Techniques (3 papers). The work is most often cited by research in Plant Science (750 citations), Ecology (418 citations), Environmental Engineering (166 citations), Analytical Chemistry (107 citations) and Ecological Modeling (31 citations). Massimo Minervini has collaborated with scholars based in Italy, United Kingdom and Germany. Frequent co-authors include Sotirios A. Tsaftaris, Hanno Scharr, Andreas Fischbach, Mario Valerio Giuffrida, Mohammed M. Abdelsamea, Pierdomenico Perata, Xiaoming Liu, Imanol Luengo, Andrew P. French and David Kramer. Their work appears in journals such as Pattern Recognition Letters, IEEE Signal Processing Magazine, The International Journal of High Performance Computing Applications, Machine Vision and Applications and Functional Plant Biology.

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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