Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Computer Vision and Pattern Recognition for Advanced Smart Agriculture Solutions
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Computer Vision and Pattern Recognition for Advanced Smart Agriculture Solutions

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 664

Special Issue Editors

Dr. Azlan Zahid

E-Mail Website
Guest Editor
Department of Biological and Agricultural Engineering, College of Agriculture and Life Sciences, Texas A&M University, Dallas, TX, USA
Interests: computer vision; agricultural robotics; electro-mechanical systems; controlled environment agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Yaqoob Majeed

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, University of Wyoming, Laramie, WY 82071, USA
Interests: computer vision; agricultural robotics; electro-mechanical systems; controlled environment agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The sustainability of food production is challenged by the growing population and food demands, with an estimate of a need for up to 70% production growth by 2050, though the arable land available for agriculture is decreasing. Agriculture is evolving into smart farming through innovations in artificial intelligence (AI), big data analytics, the Internet of Things (IoT), and automation/robotics, all aimed at enhancing crop productivity and quality, leading to more cost-effective and reliable food production systems. Computer vision systems are increasingly used for smart agriculture applications such as biotic and abiotic stress detection, crop growth and yield monitoring, targeted spraying and irrigation, precision nutrient management, and robotic operations. Advanced computational and data analytics techniques, such as deep learning, foundational models, image rendering, and 3D reconstruction, have significantly enhanced the robustness, reliability, and practical applications of computer vision technologies in all aspects of production agriculture. Therefore, this Special Issue aims to promote a deeper understanding of major conceptual and technical challenges and facilitate the spread of recent breakthroughs in computer vision for smart farming.

Dr. Azlan Zahid
Dr. Yaqoob Majeed
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. Sensors 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 2600 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

  • computer vision
  • pattern recognition
  • 3D reconstruction
  • deep learning
  • image rendering
  • classification
  • precision agriculture
  • object detection
  • crop sensing

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 57861 KiB  
Article
Automatic Apple Detection and Counting with AD-YOLO and MR-SORT
by Xueliang Yang, Yapeng Gao, Mengyu Yin and Haifang Li
Sensors 2024, 24(21), 7012; https://doi.org/10.3390/s24217012 - 31 Oct 2024
Viewed by 491
Abstract
In the production management of agriculture, accurate fruit counting plays a vital role in the orchard yield estimation and appropriate production decisions. Although recent tracking-by-detection algorithms have emerged as a promising fruit-counting method, they still cannot completely avoid fruit occlusion and light variations [...] Read more.
In the production management of agriculture, accurate fruit counting plays a vital role in the orchard yield estimation and appropriate production decisions. Although recent tracking-by-detection algorithms have emerged as a promising fruit-counting method, they still cannot completely avoid fruit occlusion and light variations in complex orchard environments, and it is difficult to realize automatic and accurate apple counting. In this paper, a video-based multiple-object tracking method, MR-SORT (Multiple Rematching SORT), is proposed based on the improved YOLOv8 and BoT-SORT. First, we propose the AD-YOLO model, which aims to reduce the number of incorrect detections during object tracking. In the YOLOv8s backbone network, an Omni-dimensional Dynamic Convolution (ODConv) module is used to extract local feature information and enhance the model’s ability better; a Global Attention Mechanism (GAM) is introduced to improve the detection ability of a foreground object (apple) in the whole image; a Soft Spatial Pyramid Pooling Layer (SSPPL) is designed to reduce the feature information dispersion and increase the sensory field of the network. Then, the improved BoT-SORT algorithm is proposed by fusing the verification mechanism, SURF feature descriptors, and the Vector of Local Aggregate Descriptors (VLAD) algorithm, which can match apples more accurately in adjacent video frames and reduce the probability of ID switching in the tracking process. The results show that the mAP metrics of the proposed AD-YOLO model are 3.1% higher than those of the YOLOv8 model, reaching 96.4%. The improved tracking algorithm has 297 fewer ID switches, which is 35.6% less than the original algorithm. The multiple-object tracking accuracy of the improved algorithm reached 85.6%, and the average counting error was reduced to 0.07. The coefficient of determination R2 between the ground truth and the predicted value reached 0.98. The above metrics show that our method can give more accurate counting results for apples and even other types of fruit. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition for Advanced Smart Agriculture Solutions)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop