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Applied Sciences
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Applied Computer Vision in Industry and Agriculture
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Applied Computer Vision in Industry and Agriculture

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 6553

Special Issue Editors

Dr. Salik Khanal

E-Mail Website
Guest Editor
Center for Precision & Automated Agricultural Systems, Washington State University, Pullman, WA, USA
Interests: machine vision; field robotics; computer vision; machine learning; Industry Technology 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Vitor Filipe

E-Mail Website
Guest Editor
Associate Professor, Department of Engineering, School of Science and Technology, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal
Interests: computer vision; machine learning; animal and human movement analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, machine vision has been applied in a wide range of areas, including industry, agriculture, psychology, sports, etc. With the advancement of computer vision technology, industrial technology is moving into another era, which has led to an improvement in the overall gain. This Special Issue aims to publish high-quality articles that represent cutting-edge research on the development of machine vision-based industry and agriculture automation. This Special Issue also aims to promote and motivate research to obtain better technology using computer vision in industry and agriculture automation.

The topics include, but are not limited to:

  • Machine vision in industry and agriculture;
  • Industrial automation;
  • Industrial robotics;
  • Agriculture automation using machine vision;
  • Anomaly detection using machine vision;
  • Industrial robot design;
  • 3D vision technology in industry and agriculture;
  • Applied computer science in industry.

Dr. Salik Ram Khanal
Dr. Vitor Filipe
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

  • machine vision
  • computer vision
  • agriculture automation
  • industry automation

Published Papers (5 papers)

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Research

16 pages, 8702 KiB  
Article
An Edge-Guided Deep Learning Solar Panel Hotspot Thermal Image Segmentation Algorithm
by Fangbin Wang, Zini Wang, Zhong Chen, Darong Zhu, Xue Gong and Wanlin Cong
Appl. Sci. 2023, 13(19), 11031; https://doi.org/10.3390/app131911031 - 07 Oct 2023
Viewed by 1007
Abstract
To overcome the deficiencies in segmenting hot spots from thermal infrared images, such as difficulty extracting the edge features, low accuracy, and a high missed detection rate, an improved Mask R-CNN photovoltaic hot spot thermal image segmentation algorithm has been proposed in this [...] Read more.
To overcome the deficiencies in segmenting hot spots from thermal infrared images, such as difficulty extracting the edge features, low accuracy, and a high missed detection rate, an improved Mask R-CNN photovoltaic hot spot thermal image segmentation algorithm has been proposed in this paper. Firstly, the edge image features of hot spots were extracted based on residual neural networks. Secondly, by combining the feature pyramid structure, an edge-guided feature pyramid structure was designed, and the hot spot edge features were injected into a Mask R-CNN network. Thirdly, an infrared spatial attention module was introduced into the Mask R-CNN network when feature extraction and the infrared features of the detected hot spots were enhanced. Fourthly, the size ratio of the candidate frames was adjusted self-adaptively according to the structural characteristics of the aspect ratio of the hot spots. Finally, the validation experiments were conducted, and the results demonstrated that the hot spot contours of thermal infrared images were enhanced through the algorithm proposed in this paper, and the segmentation accuracy was significantly improved. Full article
(This article belongs to the Special Issue Applied Computer Vision in Industry and Agriculture)
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14 pages, 2813 KiB  
Article
Generating Image Descriptions of Rice Diseases and Pests Based on DeiT Feature Encoder
by Chunxin Ma, Yanrong Hu, Hongjiu Liu, Ping Huang, Yikun Zhu and Dan Dai
Appl. Sci. 2023, 13(18), 10005; https://doi.org/10.3390/app131810005 - 05 Sep 2023
Cited by 1 | Viewed by 794
Abstract
We propose a DeiT (Data-Efficient Image Transformer) feature encoder-based algorithm for identifying disease types and generating relevant descriptions of diseased crops. It solves the scarcity problem of the image description algorithm applied in agriculture. We divided the original image into a sequence of [...] Read more.
We propose a DeiT (Data-Efficient Image Transformer) feature encoder-based algorithm for identifying disease types and generating relevant descriptions of diseased crops. It solves the scarcity problem of the image description algorithm applied in agriculture. We divided the original image into a sequence of image patches to fit the input form of the DeiT encoder, which was distilled by RegNet. Then, we used the Transformer decoder to generate descriptions. Compared to “CNN + LSTM” models, our proposed model is entirely convolution-free and has high training efficiency. On the Rice2k dataset created by us, the model achieved a 47.3 BLEU-4 score, 65.0 ROUGE_L score, and 177.1 CIDEr score. The extensive experiments demonstrate the effectiveness and the strong robustness of our model. It can be better applied to automatically generate descriptions of similar crop disease characteristics. Full article
(This article belongs to the Special Issue Applied Computer Vision in Industry and Agriculture)
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13 pages, 1851 KiB  
Article
Exploration of Machine Learning Algorithms for pH and Moisture Estimation in Apples Using VIS-NIR Imaging
by Erhan Kavuncuoğlu, Necati Çetin, Bekir Yildirim, Mohammad Nadimi and Jitendra Paliwal
Appl. Sci. 2023, 13(14), 8391; https://doi.org/10.3390/app13148391 - 20 Jul 2023
Viewed by 925
Abstract
Non-destructive assessment of fruits for grading and quality determination is essential to automate pre- and post-harvest handling. Near-infrared (NIR) hyperspectral imaging (HSI) has already established itself as a powerful tool for characterizing the quality parameters of various fruits, including apples. The adoption of [...] Read more.
Non-destructive assessment of fruits for grading and quality determination is essential to automate pre- and post-harvest handling. Near-infrared (NIR) hyperspectral imaging (HSI) has already established itself as a powerful tool for characterizing the quality parameters of various fruits, including apples. The adoption of HSI is expected to grow exponentially if inexpensive tools are made available to growers and traders at the grassroots levels. To this end, the present study aims to explore the feasibility of using a low-cost visible-near-infrared (VIS-NIR) HSI in the 386–1028 nm wavelength range to predict the moisture content (MC) and pH of Pink Lady apples harvested at three different maturity stages. Five different machine learning algorithms, viz. partial least squares regression (PLSR), multiple linear regression (MLR), k-nearest neighbor (kNN), decision tree (DT), and artificial neural network (ANN) were utilized to analyze HSI data cubes. In the case of ANN, PLSR, and MLR models, data analysis modeling was performed using 11 optimum features identified using a Bootstrap Random Forest feature selection approach. Among the tested algorithms, ANN provided the best performance with R (correlation), and root mean squared error (RMSE) values of 0.868 and 0.756 for MC and 0.383 and 0.044 for pH prediction, respectively. The obtained results indicate that while the VIS-NIR HSI promises success in non-destructively measuring the MC of apples, its performance for pH prediction of the studied apple variety is poor. The present work contributes to the ongoing research in determining the full potential of VIS-NIR HSI technology in apple grading, maturity assessment, and shelf-life estimation. Full article
(This article belongs to the Special Issue Applied Computer Vision in Industry and Agriculture)
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17 pages, 3260 KiB  
Article
Determination of the Live Weight of Farm Animals with Deep Learning and Semantic Segmentation Techniques
by Erdal Guvenoglu
Appl. Sci. 2023, 13(12), 6944; https://doi.org/10.3390/app13126944 - 08 Jun 2023
Cited by 3 | Viewed by 2004
Abstract
In cattle breeding, regularly taking the animals to the scale and recording their weight is important for both the performance of the enterprise and the health of the animals. This process, which must be carried out in businesses, is a difficult task. For [...] Read more.
In cattle breeding, regularly taking the animals to the scale and recording their weight is important for both the performance of the enterprise and the health of the animals. This process, which must be carried out in businesses, is a difficult task. For this reason, it is often not performed regularly or not performed at all. In this study, we attempted to estimate the weights of cattle by using stereo vision and semantic segmentation methods used in the field of computer vision together. Images of 85 animals were taken from different angles with a stereo setup consisting of two identical cameras. The distances of the animals to the camera plane were calculated by stereo distance calculation, and the areas covered by the animals in the images were determined by semantic segmentation methods. Then, using all these data, different artificial neural network models were trained. As a result of the study, it was revealed that when stereo vision and semantic segmentation methods are used together, live animal weights can be predicted successfully. Full article
(This article belongs to the Special Issue Applied Computer Vision in Industry and Agriculture)
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23 pages, 30525 KiB  
Article
A Computer Vision Milky Way Compass
by Yiting Tao, Michael Lucas, Asanka Perera, Samuel Teague, Eric Warrant and Javaan Chahl
Appl. Sci. 2023, 13(10), 6062; https://doi.org/10.3390/app13106062 - 15 May 2023
Viewed by 1041
Abstract
The Milky Way is used by nocturnal flying and walking insects for maintaining heading while navigating. In this study, we have explored the feasibility of the method for machine vision systems on autonomous vehicles by measuring the visual features and characteristics of the [...] Read more.
The Milky Way is used by nocturnal flying and walking insects for maintaining heading while navigating. In this study, we have explored the feasibility of the method for machine vision systems on autonomous vehicles by measuring the visual features and characteristics of the Milky Way. We also consider the conditions under which the Milky Way is used by insects and the sensory systems that support their detection of the Milky Way. Using a combination of simulated and real Milky Way imagery, we demonstrate that appropriate computer vision methods are capable of reliably and accurately extracting the orientation of the Milky Way under an unobstructed night sky. The technique presented achieves angular accuracy of better then ±2° under moderate light pollution conditions but also demonstrates that higher light pollution levels will adversely effect orientation estimates by systems depending on the Milky Way for navigation. Full article
(This article belongs to the Special Issue Applied Computer Vision in Industry and Agriculture)
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