Philipp Lottes

2.4k total citations · 1 hit paper
19 papers, 1.6k citations indexed

About

Philipp Lottes is a scholar working on Plant Science, Ecology and Environmental Engineering. According to data from OpenAlex, Philipp Lottes has authored 19 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Plant Science, 8 papers in Ecology and 6 papers in Environmental Engineering. Recurrent topics in Philipp Lottes's work include Smart Agriculture and AI (19 papers), Remote Sensing in Agriculture (8 papers) and Remote Sensing and LiDAR Applications (6 papers). Philipp Lottes is often cited by papers focused on Smart Agriculture and AI (19 papers), Remote Sensing in Agriculture (8 papers) and Remote Sensing and LiDAR Applications (6 papers). Philipp Lottes collaborates with scholars based in Germany, United Kingdom and United States. Philipp Lottes's co-authors include Cyrill Stachniss, Andres Milioto, Roland Siegwart, Raghav Khanna, Jens Behley, Nived Chebrolu, Johannes Pfeifer, Alexander Schaefer, Wolfram Burgard and Wera Winterhalter and has published in prestigious journals such as SHILAP Revista de lepidopterología, The International Journal of Robotics Research and Remote Sensing.

In The Last Decade

Philipp Lottes

19 papers receiving 1.6k citations

Hit Papers

UAV-based crop and weed classification for smart farming 2017 2026 2020 2023 2017 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. UAV-based crop and weed classification for smart farming
    2017 285 citations Philipp Lottes, Raghav Khanna et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Philipp Lottes Germany 17 1.3k 569 251 228 207 19 1.6k
Rasmus Nyholm Jørgensen Denmark 28 1.4k 1.1× 658 1.2× 289 1.2× 266 1.2× 221 1.1× 111 2.2k
Raphaël Canals France 13 862 0.7× 456 0.8× 209 0.8× 205 0.9× 159 0.8× 27 1.3k
Manuel Pérez Ruiz Spain 24 1.5k 1.1× 509 0.9× 324 1.3× 207 0.9× 110 0.5× 66 2.0k
Yeyin Shi United States 23 1.1k 0.9× 823 1.4× 436 1.7× 235 1.0× 77 0.4× 80 1.8k
Raghav Khanna Switzerland 11 627 0.5× 381 0.7× 195 0.8× 105 0.5× 309 1.5× 18 1.2k
Inkyu Sa Australia 19 1.4k 1.0× 448 0.8× 225 0.9× 386 1.7× 647 3.1× 41 2.3k
Longsheng Fu China 27 2.3k 1.8× 409 0.7× 279 1.1× 661 2.9× 261 1.3× 90 3.0k
Filipe Neves dos Santos Portugal 20 815 0.6× 188 0.3× 172 0.7× 172 0.8× 358 1.7× 102 1.4k
Ángela Ribeiro Spain 31 2.0k 1.5× 978 1.7× 565 2.3× 428 1.9× 295 1.4× 85 2.9k
Francisco Rovira-Más Spain 18 875 0.7× 249 0.4× 177 0.7× 64 0.3× 196 0.9× 62 1.4k

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 of co-authors of Philipp Lottes

This figure shows the co-authorship network connecting the top 25 collaborators of Philipp Lottes. A scholar is included among the top collaborators of Philipp Lottes 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 Philipp Lottes. Philipp Lottes 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.
Magistri, Federico, et al.. (2023). From one field to another—Unsupervised domain adaptation for semantic segmentation in agricultural robotics. Computers and Electronics in Agriculture. 212. 108114–108114. 18 indexed citations
2.
Lottes, Philipp, et al.. (2023). Unsupervised Generation of Labeled Training Images for Crop-Weed Segmentation in New Fields and on Different Robotic Platforms. IEEE Robotics and Automation Letters. 8(8). 5259–5266. 5 indexed citations
3.
Lottes, Philipp, et al.. (2022). Joint Plant and Leaf Instance Segmentation on Field-Scale UAV Imagery. IEEE Robotics and Automation Letters. 7(2). 3787–3794. 21 indexed citations
4.
Mahlein, Anne‐Katrin, et al.. (2021). UAV-Based Classification of Cercospora Leaf Spot Using RGB Images. Drones. 5(2). 34–34. 44 indexed citations
5.
Lottes, Philipp, et al.. (2021). Automatic UAV-based counting of seedlings in sugar-beet field and extension to maize and strawberry. Computers and Electronics in Agriculture. 191. 106493–106493. 44 indexed citations
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Wu, Xiaolong, et al.. (2020). Robotic weed control using automated weed and crop classification. Journal of Field Robotics. 37(2). 322–340. 109 indexed citations
9.
Chebrolu, Nived, Philipp Lottes, Thomas Läbe, & Cyrill Stachniss. (2019). Robot Localization Based on Aerial Images for Precision Agriculture Tasks in Crop Fields. 1787–1793. 38 indexed citations
10.
Lottes, Philipp, Jens Behley, Nived Chebrolu, Andres Milioto, & Cyrill Stachniss. (2019). Robust joint stem detection and crop‐weed classification using image sequences for plant‐specific treatment in precision farming. Journal of Field Robotics. 37(1). 20–34. 70 indexed citations
11.
Walter, Achim, Raghav Khanna, Philipp Lottes, et al.. (2018). Flourish - A robotic approach for automation in crop management. 5051. 11 indexed citations
12.
Lottes, Philipp, Jens Behley, Andres Milioto, & Cyrill Stachniss. (2018). Fully Convolutional Networks With Sequential Information for Robust Crop and Weed Detection in Precision Farming. IEEE Robotics and Automation Letters. 3(4). 2870–2877. 202 indexed citations
13.
Sa, Inkyu, Marija Popović, Raghav Khanna, et al.. (2018). WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming. Remote Sensing. 10(9). 1423–1423. 202 indexed citations
14.
Lottes, Philipp & Cyrill Stachniss. (2017). Semi-supervised online visual crop and weed classification in precision farming exploiting plant arrangement. 5155–5161. 38 indexed citations
15.
Milioto, Andres, Philipp Lottes, & Cyrill Stachniss. (2017). REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS. SHILAP Revista de lepidopterología. IV-2/W3. 41–48. 114 indexed citations
16.
Chebrolu, Nived, Philipp Lottes, Alexander Schaefer, et al.. (2017). Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields. The International Journal of Robotics Research. 36(10). 1045–1052. 238 indexed citations
17.
Lottes, Philipp, Raghav Khanna, Johannes Pfeifer, Roland Siegwart, & Cyrill Stachniss. (2017). UAV-based crop and weed classification for smart farming. 3024–3031. 285 indexed citations breakdown →
18.
19.
Lottes, Philipp, et al.. (2016). Effective Vision‐based Classification for Separating Sugar Beets and Weeds for Precision Farming. Journal of Field Robotics. 34(6). 1160–1178. 90 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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