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Applied Sciences
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Computer Vision in Automatic Detection and Identification, 2nd Edition
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Computer Vision in Automatic Detection and Identification, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 10 December 2025 | Viewed by 687

Special Issue Editors

Dr. Yongliang Qiao

E-Mail Website
Guest Editor
Australian Institute for Machine Learning (AIML), The University of Adelaide, Adelaide 5005, Australia
Interests: artificial intelligence; agricultural robots; smart farming; intelligent perception
Special Issues, Collections and Topics in MDPI journals
Dr. Daobilige Su

E-Mail Website
Guest Editor
College of Engineering, China Agricultural University, Beijing 100083, China
Interests: field robotics; SLAM; robot audition; computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Meili Wang

E-Mail Website
Guest Editor
College of Information and Engineering, Northwest A&F University, Xi’an 712100, China
Interests: computer graphics; computer vision; virtual reality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of our previous Special Issue entitled “Computer Vision in Automatic Detection and Identification”.

With recent advances, computer vision and Artificial Intelligence (AI) approaches have demonstrated great promise in Industry 4.0, smart agriculture, medicine, and other fields. The recent development and application of big data and AI approaches in particular boost computer-vision-based detection and identification. There are many theories, algorithms, and application approaches that have been proposed to solve challenges in the domains of science, engineering, and society. The purpose of this Special Issue is to report on advances and applications in computer-vision-based detection and identification. We welcome original research and review articles.

Potential topics include but are not limited to the following:

  • Detection and identification;
  • Image processing;
  • Object detection and segmentation;
  • Computer vision tools and applications;
  • Pattern recognition;
  • Digital image techniques;
  • Multispectral image-based detection

Dr. Yongliang Qiao
Dr. Daobilige Su
Prof. Dr. Meili Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • detection and identification
  • image processing
  • object detection and segmentation
  • computer vision tools and applications
  • pattern recognition
  • digital image techniques
  • multispectral image-based detection

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Published Papers (1 paper)

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Research

20 pages, 47324 KB  
Article
A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
by Lei Yang, Wenhao Cui, Jingqian Li, Guotao Han, Qi Zhou, Yubin Lan, Jing Zhao and Yongliang Qiao
Appl. Sci. 2025, 15(14), 8085; https://doi.org/10.3390/app15148085 - 21 Jul 2025
Viewed by 418
Abstract
Existing methods for detecting cotton boll diseases frequently exhibit high rates of both false negatives and false positives under complex field conditions (e.g., lighting variations, shadows, and occlusions) and struggle to achieve real-time performance on edge devices. To address these limitations, this study [...] Read more.
Existing methods for detecting cotton boll diseases frequently exhibit high rates of both false negatives and false positives under complex field conditions (e.g., lighting variations, shadows, and occlusions) and struggle to achieve real-time performance on edge devices. To address these limitations, this study proposes an enhanced YOLOv11n model (YOLOv11n-ECS) for improved detection accuracy. A dataset of cotton boll diseases under different lighting conditions and shooting angles in the field was constructed. To mitigate false negatives and false positives encountered by the original YOLOv11n model during detection, the EMA (efficient multi-scale attention) mechanism is introduced to enhance the weights of important features and suppress irrelevant regions, thereby improving the detection accuracy of the model. Partial Convolution (PConv) is incorporated into the C3k2 module to reduce computational redundancy and lower the model’s computational complexity while maintaining high recognition accuracy. Furthermore, to enhance the localization accuracy of diseased bolls, the original CIoU loss is replaced with Shape-IoU. The improved model achieves floating point operations (FLOPs), parameter count, and model size at 96.8%, 96%, and 96.3% of the original YOLOv11n model, respectively. The improved model achieves an mAP@0.5 of 85.6% and an mAP@0.5:0.95 of 62.7%, representing improvements of 2.3 and 1.9 percentage points, respectively, over the baseline YOLOv11n model. Compared with CenterNet, Faster R-CNN, YOLOv8-LSW, MSA-DETR, DMN-YOLO, and YOLOv11n, the improved model shows mAP@0.5 improvements of 25.7, 21.2, 5.5, 4.0, 4.5, and 2.3 percentage points, respectively, along with corresponding mAP@0.5:0.95 increases of 25.6, 25.3, 8.3, 2.8, 1.8, and 1.9 percentage points. Deployed on a Jetson TX2 development board, the model achieves a recognition speed of 56 frames per second (FPS) and an mAP of 84.2%, confirming its suitability for real-time detection. Furthermore, the improved model effectively reduces instances of both false negatives and false positives for diseased cotton bolls while yielding higher detection confidence, thus providing robust technical support for intelligent cotton boll disease detection. Full article
(This article belongs to the Special Issue Computer Vision in Automatic Detection and Identification, 2nd Edition)
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