Philipp Lottes

2.4k citations
19 papers · 1.6k indexed · 1 hit paper · h-index 17

Philipp Lottes

19 papers receiving 1.6k citations

Hit Papers

UAV-based crop and weed classification for smart farming285201720262020202350100150200250

Peers

Philipp Lottes
Comparison fields: 5 of 77
  • Plant Science 1.3k
  • Analytical Chemistry 228
  • Ecology 569
  • Environmental Engineering 251
  • Ecological Modeling 45
Replace Rasmus Nyholm Jørgensen with:
Rasmus Nyholm Jørgensen Denmark
Yeyin Shi United States
Raphaël Canals France
Manuel Pérez Ruiz Spain
Raghav Khanna Switzerland
Inkyu Sa Australia
Ángela Ribeiro Spain
Lucas Prado Osco Brazil
Jiwan Han China
Philipp Lottes relative to Rasmus Nyholm Jørgensen Denmark Rasmus Nyholm Jørgensen's profile →
Citations per field
00.5×1.5×
Rasmus Nyholm Jørgensen · 1×
Citations per year

Countries citing papers authored by Philipp Lottes

Since Specialization
Citations

This map shows the geographic impact of Philipp Lottes'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 Philipp Lottes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philipp Lottes more than expected).

Fields of papers citing papers by Philipp Lottes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Philipp Lottes. 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 Philipp Lottes. The network helps show where Philipp Lottes may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Philipp Lottes, 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 Philipp Lottes Line = papers co-authored together Philipp Lottes links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 202318
2 20235
3 202221
4 202144
5 202144
6 202018
7 202029
8 2020109
9 201938
10 201970
11
Flourish - A robotic approach for automation in crop management
201811
12 2018202
13 2018202
14 201738
15 2017114
16 2017238
17
UAV-based crop and weed classification for smart farmingbreakdown →
2017285
18 201668
19 201690

About Philipp Lottes

Philipp Lottes is a scholar working on Plant Science, Environmental Engineering, Ecology, Computer Vision and Pattern Recognition and Aerospace Engineering, having authored 19 papers that have together received 1.6k indexed citations. Recurring topics across this work include Smart Agriculture and AI (19 papers), Remote Sensing in Agriculture (8 papers), Remote Sensing and LiDAR Applications (6 papers), Plant Disease Management Techniques (2 papers), Plant Virus Research Studies (2 papers), Advanced Image and Video Retrieval Techniques (2 papers), Biosensors and Analytical Detection (2 papers) and Robotics and Sensor-Based Localization (2 papers). The work is most often cited by research in Plant Science (1.3k citations), Analytical Chemistry (228 citations), Ecology (569 citations), Environmental Engineering (251 citations) and Ecological Modeling (45 citations). Philipp Lottes has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Cyrill Stachniss, Andres Milioto, Roland Siegwart, Raghav Khanna, Jens Behley, Nived Chebrolu, Johannes Pfeifer, Alexander Schaefer, Wolfram Burgard and Wera Winterhalter. Their work appears in journals such as Journal of Field Robotics, IEEE Robotics and Automation Letters, Computers and Electronics in Agriculture, Remote Sensing and Drones.

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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