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Agriculture
Special Issues
Computer Vision and Sensor Networks in Agriculture
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Computer Vision and Sensor Networks in Agriculture

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

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 4847

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: edge-AI; computer vision; agricultural robotics; electro-mechanical systems; controlled environment agriculture; intelligent automation
Dr. Long He

E-Mail Website
Guest Editor
Department of Agricultural and Biological Engineering, The Pennsylvania State University, Biglerville, PA 17037, USA
Interests: sensing and automation; robotics and mechanization; Internet of things; deep learning; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Md Sultan Mahmud

E-Mail Website
Guest Editor
Department of Agricultural and Environmental Sciences, College of Agriculture, Tennessee State University, Nashville, TN 37209, USA
Interests: machine learning; UAV sensing; machine vision; automation; deep learning; LiDAR sensing

Special Issue Information

Dear Colleagues,

Rapid urbanization, population growth, climate change, and depleting natural resources have raised global food security concerns. Recently, agriculture has changed to using smart farming with innovations in artificial intelligence (AI), big data analytics, Internet of Things (IoT), and automation/robotics, which aim to improve productivity and quality. In this context, there has been rapid advancements in sensing technologies, connectivity, and data analytics techniques, leading to more cost-effective and reliable systems for smart agriculture. Some areas of innovation and advancement include multi- and hyper-spectral imaging, thermal imaging, and color and 3D imaging (including RGB-D). In smart farming, the term 'Agriculture 5.0' refers to the merging of AI and IoT, also known as Artificial Intelligence of Things (AIoT). These technologies work in synergy: AI enriches IoT through machine learning and deep-learning-based data analytics capabilities, while IoT enriches AI through data acquisition, connectivity, and exchange. AIoT-based systems are increasingly used for smart agriculture applications such as crop growth monitoring, pest and disease detection, microclimate monitoring and control, crop yield mapping, targeted spraying, smart irrigation, and nutrient management. Powerful computational infrastructure and associated data analytics techniques, including deep learning, also played an instrumental role in improving the robustness and reliability and widening practical applications of sensing technologies in all aspects of production agriculture. 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 sensing, computer vision, data fusion, and integrated sensor networks for smart farming. Topics of interest include but are not limited to the following:

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

Dr. Azlan Zahid
Dr. Long He
Dr. Md Sultan Mahmud
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

  • edge computing
  • Artificial Intelligence of Things
  • wireless sensor networks
  • Internet of things
  • Edge-Cloud collaborative learning
  • fog computing
  • deep learning
  • agricultural robotics
  • smart agriculture

Published Papers (3 papers)

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Research

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17 pages, 8955 KiB  
Article
Research on the Method of Identifying the Severity of Wheat Stripe Rust Based on Machine Vision
by Ruonan Gao, Fengxiang Jin, Min Ji and Yanan Zuo
Agriculture 2023, 13(12), 2187; https://doi.org/10.3390/agriculture13122187 - 22 Nov 2023
Cited by 1 | Viewed by 887
Abstract
Wheat stripe rust poses a serious threat to the quality and yield of wheat crops. Typically, the occurrence data of wheat stripe rust is characterized by small sample sizes, and the current research on severity identification lacks high-precision methods for small sample data. [...] Read more.
Wheat stripe rust poses a serious threat to the quality and yield of wheat crops. Typically, the occurrence data of wheat stripe rust is characterized by small sample sizes, and the current research on severity identification lacks high-precision methods for small sample data. Additionally, the irregular edges of wheat stripe rust lesions make it challenging to draw samples. In this study, we propose a method for wheat stripe rust severity identification that combines SLIC superpixel segmentation and a random forest algorithm. This method first employs SLIC to segment subregions of wheat stripe rust, automatically constructs and augments a dataset of wheat stripe rust samples based on the segmented patches. Then, a random forest model is used to classify the segmented subregion images, achieving fine-grained extraction of wheat stripe rust lesions. By merging the extracted subregion images and using pixel statistics, the percentage of lesion area is calculated, ultimately enabling the identification of the severity of wheat stripe rust. The results show that our method outperforms unsupervised classification algorithms such as watershed segmentation and K-Means clustering in terms of lesion extraction when using the segmented subregion dataset of wheat stripe rust. Compared to the K-Means segmentation method, the mean squared error is reduced by 1.2815, and compared to the watershed segmentation method, it is reduced by 2.0421. When compared to human visual inspection as the ground truth, the perceptual loss for lesion area extraction is 0.064. This method provides a new approach for the intelligent extraction of wheat stripe rust lesion areas and fading green areas, offering important theoretical reference for the precise prevention and control of wheat stripe rust. Full article
(This article belongs to the Special Issue Computer Vision and Sensor Networks in Agriculture)
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22 pages, 1794 KiB  
Article
Smart Temperature and Humidity Control in Pig House by Improved Three-Way K-Means
by Haopu Li, Haoming Li, Bugao Li, Jiayuan Shao, Yanbo Song and Zhenyu Liu
Agriculture 2023, 13(10), 2020; https://doi.org/10.3390/agriculture13102020 - 18 Oct 2023
Cited by 1 | Viewed by 1395
Abstract
Efficiently managing temperature and humidity in a pig house is crucial for enhancing animal welfare. This research endeavors to develop an intelligent temperature and humidity control system grounded in a three-way decision and clustering algorithm. To establish and validate the effectiveness of this [...] Read more.
Efficiently managing temperature and humidity in a pig house is crucial for enhancing animal welfare. This research endeavors to develop an intelligent temperature and humidity control system grounded in a three-way decision and clustering algorithm. To establish and validate the effectiveness of this intelligent system, experiments were conducted to compare its performance against a naturally ventilated pig house without any control system. Additionally, comparisons were made with a threshold-based control system to evaluate the duration of temperature anomalies. The experimental findings demonstrate a substantial improvement in temperature regulation within the experimental pig house. Over a 24 h period, the minimum temperature increased by 4 °C, while the maximum temperature decreased by 8 °C, approaching the desired range. Moreover, the average air humidity decreased from 73.4% to 68.2%. In summary, this study presents a precision-driven intelligent control strategy for optimizing temperature and humidity management in pig housing facilities. Full article
(This article belongs to the Special Issue Computer Vision and Sensor Networks in Agriculture)
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Review

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14 pages, 10053 KiB  
Review
A Methodological Review of Fluorescence Imaging for Quality Assessment of Agricultural Products
by Abdul Momin, Naoshi Kondo, Dimas Firmanda Al Riza, Yuichi Ogawa and David Obenland
Agriculture 2023, 13(7), 1433; https://doi.org/10.3390/agriculture13071433 - 20 Jul 2023
Cited by 2 | Viewed by 1597
Abstract
Currently, optical imaging techniques are extensively employed to automatically sort agricultural products based on various quality parameters such as size, shape, color, ripeness, sugar content, and acidity. This methodological review article examined different machine vision techniques, with a specific focus on exploring the [...] Read more.
Currently, optical imaging techniques are extensively employed to automatically sort agricultural products based on various quality parameters such as size, shape, color, ripeness, sugar content, and acidity. This methodological review article examined different machine vision techniques, with a specific focus on exploring the potential of fluorescence imaging for non-destructive assessment of agricultural product quality attributes. The article discussed the concepts and methodology of fluorescence, providing a comprehensive understanding of fluorescence spectroscopy and offering a logical approach to determine the optimal wavelength for constructing an optimized fluorescence imaging system. Furthermore, the article showcased the application of fluorescence imaging in detecting peel defects in a diverse range of citrus as an example of this imaging modality. Additionally, the article outlined potential areas for future investigation into fluorescence imaging applications for the quality assessment of agricultural products. Full article
(This article belongs to the Special Issue Computer Vision and Sensor Networks in Agriculture)
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