Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.6
3.6
Journals
Agriculture
Special Issues
Computer Vision and Artificial Intelligence in Agriculture
share announcement

Computer Vision and Artificial Intelligence in Agriculture

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

Deadline for manuscript submissions: 10 July 2024 | Viewed by 2727

Special Issue Editors

Dr. Bo Xu

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: image processing; computer vision; deep learning; agricultural robotics; artificial intelligence
Dr. Weikuan Jia

E-Mail Website
Guest Editor
School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China
Interests: artificial intelligence; computer vision; smart orchard; fruit detection and segmentation; agricultural information technology and equipment

Special Issue Information

Dear Colleagues,

Rapid urbanization, population growth, climate change, and depleting natural resources have raised global food security concerns. Recently, smart farming with innovative technology has started to see agricultural use. Computer vision (CV) and artificial intelligence (AI) have been gaining traction in agriculture. From reducing production costs with intelligent automation to boosting productivity, CV and AI have significant potential to enhance the overall functioning of smart farming. CV and AI-based systems are increasingly being used for smart agriculture applications such as agricultural automation and robotics, the non-destructive detection of living things, livestock and poultry behavior recognition, crop growth monitoring, pest and disease detection, crop yield mapping, targeted spraying, smart irrigation, and nutrient management.

Therefore, this Special Issue aims to promote a deeper understanding of major conceptual and technical challenges and to facilitate the spread of recent breakthroughs in computer vision and artificial intelligence for smart agriculture. All types of articles such as original research, opinions, and reviews are welcome. Topics of interest include but are not limited to the following:

  • Computer vision for agricultural automation and robotics;
  • Computer vision for plant phenotyping;
  • Computer vision for greenhouses, plant factories, and vertical farms;
  • Monitoring/decision support systems for crop/livestock management;
  • IoT, big data, and data analytics for smart agriculture;
  • Edge AI applications for smart agriculture;
  • UAV-based sensing and computer vision for smart agriculture.

Dr. Bo Xu
Dr. Weikuan Jia
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

  • smart agriculture
  • computer vision
  • artificial intelligence
  • image processing technology
  • deep learning
  • artificial neural network
  • machine learning
  • guidance, navigation, and control
  • autonomy, perception, and decision making
  • data analysis and decision support

Published Papers (4 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

25 pages, 9459 KiB  
Article
BerryNet-Lite: A Lightweight Convolutional Neural Network for Strawberry Disease Identification
by Jianping Wang, Zhiyu Li, Guohong Gao, Yan Wang, Chenping Zhao, Haofan Bai, Yingying Lv, Xueyan Zhang and Qian Li
Agriculture 2024, 14(5), 665; https://doi.org/10.3390/agriculture14050665 - 25 Apr 2024
Viewed by 312
Abstract
With the rapid advancements in computer vision, using deep learning for strawberry disease recognition has emerged as a new trend. However, traditional identification methods heavily rely on manual discernment, consuming valuable time and imposing significant financial losses on growers. To address these challenges, [...] Read more.
With the rapid advancements in computer vision, using deep learning for strawberry disease recognition has emerged as a new trend. However, traditional identification methods heavily rely on manual discernment, consuming valuable time and imposing significant financial losses on growers. To address these challenges, this paper presents BerryNet-Lite, a lightweight network designed for precise strawberry disease identification. First, a comprehensive dataset, encompassing various strawberry diseases at different maturity levels, is curated. Second, BerryNet-Lite is proposed, utilizing transfer learning to expedite convergence through pre-training on extensive datasets. Subsequently, we introduce expansion convolution into the receptive field expansion, promoting more robust feature extraction and ensuring accurate recognition. Furthermore, we adopt the efficient channel attention (ECA) as the attention mechanism module. Additionally, we incorporate a multilayer perceptron (MLP) module to enhance the generalization capability and better capture the abstract features. Finally, we present a novel classification head design approach which effectively combines the ECA and MLP modules. Experimental results demonstrate that BerryNet-Lite achieves an impressive accuracy of 99.45%. Compared to classic networks like ResNet34, VGG16, and AlexNet, BerryNet-Lite showcases superiority across metrics, including loss value, accuracy, precision, F1-score, and parameters. It holds significant promise for applications in strawberry disease identification. Full article
(This article belongs to the Special Issue Computer Vision and Artificial Intelligence in Agriculture)
►▼ Show Figures

Figure 1

22 pages, 10436 KiB  
Article
A Lightweight Deep Learning Semantic Segmentation Model for Optical-Image-Based Post-Harvest Fruit Ripeness Analysis of Sugar Apples (Annona squamosa)
by Zewen Xie, Zhenyu Ke, Kuigeng Chen, Yinglin Wang, Yadong Tang and Wenlong Wang
Agriculture 2024, 14(4), 591; https://doi.org/10.3390/agriculture14040591 - 08 Apr 2024
Viewed by 614
Abstract
The sugar apple (Annona squamosa) is valued for its taste, nutritional richness, and versatility, making it suitable for fresh consumption and medicinal use with significant commercial potential. Widely found in the tropical Americas and Asia’s tropical or subtropical regions, it faces [...] Read more.
The sugar apple (Annona squamosa) is valued for its taste, nutritional richness, and versatility, making it suitable for fresh consumption and medicinal use with significant commercial potential. Widely found in the tropical Americas and Asia’s tropical or subtropical regions, it faces challenges in post-harvest ripeness assessment, predominantly reliant on manual inspection, leading to inefficiency and high labor costs. This paper explores the application of computer vision techniques in detecting ripeness levels of harvested sugar apples and proposes an improved deep learning model (ECD-DeepLabv3+) specifically designed for ripeness detection tasks. Firstly, the proposed model adopts a lightweight backbone (MobileNetV2), reducing complexity while maintaining performance through MobileNetV2′s unique design. Secondly, it incorporates the efficient channel attention (ECA) module to enhance focus on the input image and capture crucial feature information. Additionally, a Dense ASPP module is introduced, which enhances the model’s perceptual ability and expands the receptive field by stacking feature maps processed with different dilation rates. Lastly, the proposed model emphasizes the spatial information of sugar apples at different ripeness levels by the coordinate attention (CA) module. Model performance is validated using a self-made dataset of harvested optical images categorized into three ripeness levels. The proposed model (ECD-DeepLabv3+) achieves values of 89.95% for MIoU, 94.58% for MPA, 96.60% for PA, and 94.61% for MF1, respectively. Compared to the original DeepLabv3+, it greatly reduces the number of model parameters (Params) and floating-point operations (Flops) by 89.20% and 69.09%, respectively. Moreover, the proposed method could be directly applied to optical images obtained from the surface of the sugar apple, which provides a potential solution for the detection of post-harvest fruit ripeness. Full article
(This article belongs to the Special Issue Computer Vision and Artificial Intelligence in Agriculture)
►▼ Show Figures

Figure 1

15 pages, 5353 KiB  
Article
The Detection of Ear Tag Dropout in Breeding Pigs Using a Fused Attention Mechanism in a Complex Environment
by Fang Wang, Xueliang Fu, Weijun Duan, Buyu Wang and Honghui Li
Agriculture 2024, 14(4), 530; https://doi.org/10.3390/agriculture14040530 - 27 Mar 2024
Viewed by 641
Abstract
The utilization of ear tags for identifying breeding pigs is a widely used technique in the field of animal production. Ear tag dropout can lead to the loss of pig identity information, resulting in missing data and ambiguity in production management and genetic [...] Read more.
The utilization of ear tags for identifying breeding pigs is a widely used technique in the field of animal production. Ear tag dropout can lead to the loss of pig identity information, resulting in missing data and ambiguity in production management and genetic breeding data. Therefore, the identification of ear tag dropout is crucial for intelligent breeding in pig farms. In the production environment, promptly detecting breeding pigs with missing ear tags is challenging due to clustering overlap, small tag targets, and uneven sample distributions. This study proposes a method for detecting the dropout of breeding pigs’ ear tags in a complex environment by integrating an attention mechanism. Firstly, the approach involves designing a lightweight feature extraction module called IRDSC using depthwise separable convolution and an inverted residual structure; secondly, the SENet channel attention mechanism is integrated for enhancing deep semantic features; and finally, the IRDSC and SENet modules are incorporated into the backbone network of Cascade Mask R-CNN and the loss function is optimized with Focal Loss. The proposed algorithm, Cascade-TagLossDetector, achieves an accuracy of 90.02% in detecting ear tag dropout in breeding pigs, with a detection speed of 25.33 frames per second (fps), representing a 2.95% improvement in accuracy, and a 3.69 fps increase in speed compared to the previous method. The model size is reduced to 443.03 MB, a decrease of 72.90 MB, which enables real-time and accurate dropout detection while minimizing the storage requirements and providing technical support for the intelligent breeding of pigs. Full article
(This article belongs to the Special Issue Computer Vision and Artificial Intelligence in Agriculture)
►▼ Show Figures

Figure 1

18 pages, 3554 KiB  
Article
Diagnosis of Cotton Nitrogen Nutrient Levels Using Ensemble MobileNetV2FC, ResNet101FC, and DenseNet121FC
by Peipei Chen, Jianguo Dai, Guoshun Zhang, Wenqing Hou, Zhengyang Mu and Yujuan Cao
Agriculture 2024, 14(4), 525; https://doi.org/10.3390/agriculture14040525 - 26 Mar 2024
Viewed by 675
Abstract
Nitrogen plays a crucial role in cotton growth, making the precise diagnosis of its nutrition levels vital for the scientific and rational application of fertilizers. Addressing this need, our study introduced an EMRDFC-based diagnosis model specifically for cotton nitrogen nutrition levels. In our [...] Read more.
Nitrogen plays a crucial role in cotton growth, making the precise diagnosis of its nutrition levels vital for the scientific and rational application of fertilizers. Addressing this need, our study introduced an EMRDFC-based diagnosis model specifically for cotton nitrogen nutrition levels. In our field experiments, cotton was subjected to five different nitrogen application rates. To enhance the diagnostic capabilities of our model, we employed ResNet101, MobileNetV2, and DenseNet121 as base models and integrated the CBAM (Convolutional Block Attention Module) into each to improve their ability to differentiate among various nitrogen levels. Additionally, the Focal loss function was introduced to address issues of data imbalance. The model’s effectiveness was further augmented by employing integration strategies such as relative majority voting, simple averaging, and weighted averaging. Our experimental results indicated significant accuracy improvements in the enhanced ResNet101, MobileNetV2, and DenseNet121 models by 2.3%, 2.91%, and 2.93%, respectively. Notably, the integration of these models consistently improved accuracy, with gains of 0.87% and 1.73% compared to the highest-performing single model, DenseNet121FC. The optimal ensemble model, which utilized the weighted average method, demonstrated superior learning and generalization capabilities. The proposed EMRDFC model shows great promise in precisely identifying cotton nitrogen status, offering critical insights into the diagnosis of crop nutrient status. This research contributes significantly to the field of agricultural technology by providing a reliable tool for nitrogen-level assessment in cotton cultivation. Full article
(This article belongs to the Special Issue Computer Vision and Artificial Intelligence in Agriculture)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop