{rfName}
We

Indexed in

License and use

Icono OpenAccess

Citations

11

Altmetrics

Grant support

This work was supported by the Spanish Research State Agency (AEI) through the Project PID2020-113229RB-C41/AEI/10.13039/501100011033. The lead author, G.A. Mesias-Ruiz has been a beneficiary of a FPI fellowship by the Spanish Ministry of Education and Professional Training (PRE2018-083227) . The research of I. Borra-Serrano was financed by the grant FJC2021-047687-1 funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR.

Analysis of institutional authors

Mesias-Ruiz, G AAuthor

Share

June 18, 2024
Publications
>
Article
Sí

Weed species classification with UAV imagery and standard CNN models: Assessing the frontiers of training and inference phases

Publicated to:Crop Protection. 182 106721- - 2024-08-01 182(), DOI: 10.1016/j.cropro.2024.106721

Authors: Mesías-Ruiz, GA; Borra-Serrano, I; Peña, JM; de Castro, AI; Fernández-Quintanilla, C; Dorado, J

Affiliations

Natl Agr & Food Res & Technol Inst INIA CSIC, Environm & Agron Dept, Crta Coruna Km 7-5, Madrid 28008, Spain - Author
Spanish Natl Res Council, Inst Agr Sci, C Serrano 115b, Madrid 28006, Spain - Author
Univ Politecn Madrid, Sch Agr Food & Biosyst Engn ETSIAAB, Ave Puerta Hierro 2, Madrid 28040, Spain - Author

Abstract

Accurate weed species identification is crucial for effective site -specific weed management (SSWM), enabling targeted and timely control measures for each weed in crop field. This study advanced the current approach to species -level weed identification during the early growth stage by integrating unmanned aerial vehicles (UAVs) imagery with standard convolutional neural networks (CNNs) models such as VGG16, Resnet152 and InceptionResnet-v2. For this, a robust dataset was created with 33,467 labels of weeds ( Atriplex patula , Chenopodium album , Convolvulus arvensis , Cyperus rotundus , Lolium rigidum , Portulaca oleracea , Salsola kali , Solanum nigrum ) and crops (maize, tomato), which was subjected to different training, validation and test scenarios. Model inputs were adjusted in order to align them with the information represented by the UAV images. Initially, models were developed in balanced scenarios, gradually increasing label numbers to assess their performance. InceptionResNet-v2 achieved over 90% accuracy with 400 labels, while ResNet152 and VGG16 required 600 and 800 labels, respectively, for similar accuracy. In a more complex and realistic scenarios with unbalanced datasets, Inception-ResNet-v2 outperformed, likely due to its deeper architecture and enhanced capability to capture intricate features and patterns within UAV images. The study emphasized the importance of the minority -tomajority species ratio in unbalanced datasets, which affects minority species classification. To prevent misclassification, it is crucial to determine the right number of labels for CNN model training and validation. Weed maps were generated after species classification using the Faster R -CNN algorithm as an object detector. This advancement in methodology facilitates the precise and efficient implementation of SSWM techniques.

Keywords

Deep learningFaster r–cnnFasterr-cnInception-resnet-v2Resnet152Site-specific weed management (sswm)Vgg16

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Crop Protection 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, 2024 there are still no calculated indicators, but in 2023, it was in position 31/129, thus managing to position itself as a Q1 (Primer Cuartil), in the category Agronomy.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-08-02:

  • Google Scholar: 1
  • WoS: 3
  • Scopus: 6

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 2025-08-02:

  • 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: 36.
  • 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: 45 (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: 5.75.
  • The number of mentions on the social network X (formerly Twitter): 7 (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.

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 (MESIAS RUIZ, GUSTAVO) .