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Special Issue "Smart Agricultural Applications with Internet of Things"

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

Deadline for manuscript submissions: 29 February 2020.

Special Issue Editors

Guest Editor
Prof. Dr. Lei Shu Website E-Mail
Nanjing Agricultural University, China;School of Engineering, College of Science, University of Lincoln, Lincoln LN6 7TS, UK
Interests: wireless sensor networks; multimedia communication; middleware; security
Guest Editor
Dr. Sook Yoon Website E-Mail
Department of Computer Engineering, Mokpo National University, Mokpo, Korea
Interests: image processing and computer vision; object recognition; machine learning; biometrics
Guest Editor
Dr. Edmond Nurellari Website E-Mail
School of Engineering, College of Science, University of Lincoln, Lincoln LN6 7TS, UK
Interests: distributed signal processing; signal processing on graphs; resource allocations and distributed decisions in wireless sensor networks
Guest Editor
Dr. Kai Huang Website E-Mail
Nanjing Agricultural University, China
Interests: agricultural Internet of things

Special Issue Information

Dear Colleague,

The third wave of the world’s information industry is coming. The Internet of things and smart agricultural technology have penetrated into various fields of agriculture, and a large number of new technologies, new products, new applications, and new models have emerged. As the focus of the government and enterprises in the agricultural field, the agricultural Internet of things and smart agricultural technology have made great progress.

In order to promote the development and application of smart agriculture and to better serve the modernization of agriculture and rural areas, it is necessary to improve the level of quantification, intelligence, and scientificization of agricultural production; promote the formation and development of industrial systems related to smart agriculture; and to modernize traditional agriculture, which will provide strong support for the transformation and upgrading of agriculture.

This Special Issue aims at providing a forum to present the latest advances on smart agriculture. Authors from both academia and agriculture are welcome to submit their original papers. Topics of interest include, but are not limited to, the following:

  • Agricultural artificial intelligence;
  • Agricultural blockchain;
  • Agricultural cloud computing and big data;
  • Agricultural Internet of things;
  • Agricultural knowledge engineering;
  • Agricultural remote sensing;
  • Agricultural robots and intelligent equipment;
  • Agricultural system simulation;
  • Precision agriculture.

The best papers from TeCrop 2019 (http://jlloret.webs.upv.es/tecrop2019/index.html) will be selected for the special issue.

Prof. Dr. Lei Shu
Dr. Sook Yoon
Dr. Edmond Nurellari
Dr. Kai Huang
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 papers will be 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 1800 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.

Published Papers (1 paper)

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Research

Open AccessArticle
Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology
Sensors 2019, 19(18), 3918; https://doi.org/10.3390/s19183918 - 11 Sep 2019
Abstract
An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were [...] Read more.
An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of 0.094   s in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of 0.113   s . The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of 140   ×   260 pixels could achieve high-precision vision navigation while the course deviation angle was not more than 7.5 ° . The maximum tractor speed of the optimal template and global template were 51.41   km / h and 27.47   km / h , respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect. Full article
(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)
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