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CORRECTION article

Front. Plant Sci., 01 August 2025

Sec. Technical Advances in Plant Science

Volume 16 - 2025 | https://doi.org/10.3389/fpls.2025.1664228

Correction: Optimizing Mask R-CNN for enhanced quinoa panicle detection and segmentation in precision agriculture

This article is a correction to:

  1. Optimizing Mask R-CNN for Enhanced Quinoa Panicle Detection and Segmentation in Precision Agriculture

    1. Read original article
Manal El Akrouchi,*&#x;Manal El Akrouchi1,2*†Manal Mhada&#x;Manal Mhada1†Dachena Romain GraciaDachena Romain Gracia1Malcolm J. HawkesfordMalcolm J. Hawkesford3Bruno GrardBruno Gérard1
  • 1College of Agriculture and Environmental Sciences, University Mohammed VI Polytechnic (UM6P), Ben Guerir, Morocco
  • 2School of Collective Intelligence, University Mohammed VI Polytechnic (UM6P), Rabat, Morocco
  • 3Sustainable Soils and Crops Department, Rothamsted Research, Harpenden, Hertfordshire, United Kingdom

A Correction on
Optimizing Mask R-CNN for enhanced quinoa panicle detection and segmentation in precision agriculture

By El Akrouchi M, Mhada M, Gracia DR, Hawkesford MJ and Gérard B (2025). Front. Plant Sci. 16:1472688. doi: 10.3389/fpls.2025.1472688

There was a mistake in Figure 3 as published. I was working on two papers simultaneously, this one and another related to citrus (see this link). While preparing the flowcharts for both projects, I inadvertently used the same name for both files, which led to this confusion. The corrected Figure 3 appears below.

Figure 3
Flowchart depicting a pipeline for image-based quinoa panicle counting using Mask R-CNN models. Sections include protocol design, dataset generation, backbone comparison, segmentation and counting, and final outputs. Backbones tested are ResNet50, ResNet101, ViTDet_b, Swin_b, Mish-based EfficientNet B7 with FPN(LN), and FPN(GN). Steps involve image acquisition, data augmentation, and deep learning model training. The final output involves comparing manual and predicted counts and analyzing results.

Figure 3. Overall process flowchart of quinoa panicles detection and segmentation.

The original version of this article has been updated.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: Mask R-CNN, instance segmentation, quinoa, precision agriculture, deep learning

Citation: El Akrouchi M, Mhada M, Gracia DR, Hawkesford MJ and Gérard B (2025) Correction: Optimizing Mask R-CNN for enhanced quinoa panicle detection and segmentation in precision agriculture. Front. Plant Sci. 16:1664228. doi: 10.3389/fpls.2025.1664228

Received: 11 July 2025; Accepted: 18 July 2025;
Published: 01 August 2025.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2025 El Akrouchi, Mhada, Gracia, Hawkesford and Gérard. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Manal El Akrouchi, bWFuYWwuZWxha3JvdWNoaUB1bTZwLm1h

†These authors have contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.