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Analysis of institutional authors

Ulloa, CcCorresponding AuthorKrus, AAuthorKrus, AnneAuthorBarrientos, AAuthorDel Cerro, JAuthorValero, CAuthor

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February 15, 2021
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Robotic Fertilisation Using Localisation Systems Based on Point Clouds in Strip-Cropping Fields

Publicated to: Agronomy-Basel. 11 (1): 11- - 2021-01-01 11(1), DOI: 10.3390/agronomy11010011

Authors:

Ulloa, CC; Krus, A; Barrientos, A; Del Cerro, J; Valero, C
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Affiliations

‎ Univ Politecn Madrid, Ctr Automat & Robot, CSIC, Madrid 28006, Spain - Author
‎ Univ Politecn Madrid, Dept Ingn Agroforestal, ETSI Agronom Alimentaria & Biosistemas, Madrid 28040, Spain - Author

Abstract

The use of robotic systems in organic farming has taken on a leading role in recent years; the Sureveg CORE Organic Cofund ERA-Net project seeks to evaluate the benefits of strip-cropping to produce organic vegetables. This includes, among other objectives, the development of a robotic tool that facilitates the automation of the fertilisation process, allowing the individual treatment (at the plant level). In organic production, the slower nutrient release of the used fertilisers poses additional difficulties, as a tardy detection of deficiencies can no longer be corrected. To improve the detection, as well as counter the additional labour stemming from the strip-cropping configuration, an integrated robotic tool is proposed to detect individual crop deficiencies and react on a single-crop basis. For the development of this proof-of-concept, one of the main objectives of this work is implementing a robust localisation method within the vegetative environment based on point clouds, through the generation of general point cloud maps (G-PC) and local point cloud maps (L-PC) of a crop row. The plants' geometric characteristics were extracted from the G-PC as a framework in which the robot's positioning is defined. Through the processing of real-time lidar data, the L-PC is then defined and compared to the predefined reference system previously deduced. Both subsystems are integrated with ROS (Robot Operating System), alongside motion planning, and an inverse kinematics CCD (Cyclic Coordinate Descent) solver, among others. Tests were performed using a simulated environment of the crop row developed in Gazebo, followed by actual measurements in a strip-cropping field. During real-time data-acquisition, the localisation error is reduced from 13 mm to 11 mm within the first 120 cm of measurement. The encountered real-time geometric characteristics were found to coincide with those in the G-PC to an extend of 98.6%.
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Keywords

LidarOrganic farmingPoint cloud localisationRobotic systemsRosStrip cropping

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Agronomy-Basel due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position 18/90, thus managing to position itself as a Q1 (Primer Cuartil), in the category Agronomy.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.26. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 1.28 (source consulted: FECYT Mar 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-27, the following number of citations:

  • WoS: 14
  • Scopus: 16
  • Google Scholar: 15
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-27:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 32.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 31 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 13.
  • The number of mentions on the social network X (formerly Twitter): 5 (Altmetric).
  • The number of mentions in news outlets: 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/65844/
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (CRUZ ULLOA, CHRISTYAN) and Last Author (VALERO UBIERNA, CONSTANTINO).

the author responsible for correspondence tasks has been CRUZ ULLOA, CHRISTYAN.

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Awards linked to the item

This research has been possible thanks to the financing of the European project Sureveg: Strip-cropping and recycling for biodiverse and resource-efficient intensive vegetable production, belonging to the action ERA-net CORE Organic Cofund: http://projects.au.dk/coreorganiccofund/and RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by Programas de Actividades I+D en la Comunidad Madrid and cofunded by Structural Funds of the EU.
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