Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
3.3
Journals
Agriculture
Special Issues
Current and Future Application of Computer Vision and Data Analysis...
share announcement

Current and Future Application of Computer Vision and Data Analysis in Smart Agriculture and Agroforestry

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: closed (10 April 2024) | Viewed by 4524

Special Issue Editors

Dr. Salviano Pinto Soares

E-Mail Website
Guest Editor
1. ECE PhD Director, Engineering Department, School of Sciences and Technology, University of Trás-os-Montes and Alto Douro (UTAD), Vila Real, Portugal
2. IEETA—Institute of Electronics and Informatic Engineering of Aveiro, Aveiro, Portugal
Interests: signal processing for IoT; data analysis in smart agriculture and agroforestry
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Antonio Valente

E-Mail Website
Guest Editor
1. Habilitation at Engineering Department, UTAD—University of Trás-os-Montes and Alto Douro, Vila Real, Portugal
2. INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
Interests: educational robotics; robotic competitions; robotics for agriculture; IoT; sensors; sensors for agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Filipe Cabral Pinto

E-Mail Website1 Website2
Co-Guest Editor
Altice Labs, 3810-106 Aveiro, Portugal
Interests: machine learning; IoT; applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of technology is an increasingly in-demand requirement for many of the primary economic activities such as agriculture, mining or forestry. Factors such as population growth, climate change or the shifting of business models due to the recent changes across the world have added additional pressure to these industries to improve productivity while maintaining sustainability. Digital agriculture broadly encompasses technologies used to assist producers in farming, most commonly known as precision agriculture technologies, that need to be empowered with data analysis and machine learning facilities. On the other hand, forestry nowadays is an industry that presents an ample opportunity for automation and the use of robotic vehicles and computer vision to perform specific tasks, and despite the large body of development and research into robotics in terms of perception, navigation and control being vast, their practical application in this industry is still limited.

Dr. Salviano Pinto Soares
Dr. Filipe Cabral Pinto
Prof. Dr. Antonio Valente
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. Agriculture is an international peer-reviewed open access monthly 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

  • sustainable and digital agriculture
  • agroforestry
  • computer vision
  • data analysis
  • artificial/augmented intelligence
  • machine learning

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 (2 papers)

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

Research

17 pages, 5367 KiB  
Article
Recognition Method of Cabbage Heads at Harvest Stage under Complex Background Based on Improved YOLOv8n
by Yongqiang Tian, Chunjiang Zhao, Taihong Zhang, Huarui Wu and Yunjie Zhao
Agriculture 2024, 14(7), 1125; https://doi.org/10.3390/agriculture14071125 - 11 Jul 2024
Cited by 3 | Viewed by 1253
Abstract
To address the problems of low recognition accuracy and slow processing speed when identifying harvest-stage cabbage heads in complex environments, this study proposes a lightweight harvesting period cabbage head recognition algorithm that improves upon YOLOv8n. We propose a YOLOv8n-Cabbage model, integrating an enhanced [...] Read more.
To address the problems of low recognition accuracy and slow processing speed when identifying harvest-stage cabbage heads in complex environments, this study proposes a lightweight harvesting period cabbage head recognition algorithm that improves upon YOLOv8n. We propose a YOLOv8n-Cabbage model, integrating an enhanced backbone network, the DyHead (Dynamic Head) module insertion, loss function optimization, and model light-weighting. To assess the proposed method, a comparison with extant mainstream object detection models is conducted. The experimental results indicate that the improved cabbage head recognition model proposed in this study can adapt cabbage head recognition under different lighting conditions and complex backgrounds. With a compact size of 4.8 MB, this model achieves 91% precision, 87.2% recall, and a mAP@50 of 94.5%—the model volume has been reduced while the evaluation metrics have all been improved over the baseline model. The results demonstrate that this model can be applied to the real-time recognition of harvest-stage cabbage heads under complex field environments. Full article
(This article belongs to the Special Issue Current and Future Application of Computer Vision and Data Analysis in Smart Agriculture and Agroforestry)
►▼ Show Figures

Figure 1

19 pages, 7269 KiB  
Article
Application of Deep Learning in Image Recognition of Citrus Pests
by Xinyu Jia, Xueqin Jiang, Zhiyong Li, Jiong Mu, Yuchao Wang and Yupeng Niu
Agriculture 2023, 13(5), 1023; https://doi.org/10.3390/agriculture13051023 - 7 May 2023
Cited by 10 | Viewed by 2430
Abstract
The occurrence of pests at high frequencies has been identified as a major cause of reduced citrus yields, and early detection and prevention are of great significance to pest control. At present, studies related to citrus pest identification using deep learning suffer from [...] Read more.
The occurrence of pests at high frequencies has been identified as a major cause of reduced citrus yields, and early detection and prevention are of great significance to pest control. At present, studies related to citrus pest identification using deep learning suffer from unbalanced sample sizes between data set classes, which may cause slow convergence of network models and low identification accuracy. To address the above problems, this study built a dataset including 5182 pest images in 14 categories. Firstly, we expanded the dataset to 21,000 images by using the Attentive Recurrent Generative Adversarial Network (AR-GAN) data augmentation technique, then we built Visual Geometry Group Network (VGG), Residual Neural Network (ResNet) and MobileNet citrus pest recognition models by using transfer learning, and finally, we introduced an appropriate attention mechanism according to the model characteristics to enhance the ability of the three models to operate effectively in complex, real environments with greater emphasis placed on incorporating the deep features of the pests themselves. The results showed that the average recognition accuracy of the three models reached 93.65%, the average precision reached 93.82%, the average recall reached 93.65%, and the average F1-score reached 93.62%. The integrated application of data augmentation, transfer learning and attention mechanisms in the research can significantly enhance the model’s ability to classify citrus pests while saving training cost and time, which can be a reference for researchers in the industry or other fields. Full article
(This article belongs to the Special Issue Current and Future Application of Computer Vision and Data Analysis in Smart Agriculture and Agroforestry)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop