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Sensor Fusion for Precision Agriculture
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Sensor Fusion for Precision Agriculture

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 9116

Special Issue Editors

Prof. Dr. Sun-Ok Chung

E-Mail Website
Guest Editor
Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
Interests: agricultural machinery; biosystems engineering; smart agriculture; precision agriculture; artificial intelligence; sensor; control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Minzan Li

E-Mail Website
Guest Editor
Research Center for Precision Agriculture, China Agricultural University, Beijing 100083, China
Interests: smart sensors for agriculture; soil and spectral sensors; greenhouse and hydroponic smart control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision agriculture pursues the management of spatial and temporal variability of farm land, and plant and animal growth, and expands the area to protected horticulture and livestock production, as well as open fields. Recently, the sensors have become extensively fused and applied for various purposes, with the adoption of ICT and artificial intelligence. This Special Issue addresses the recent development and application of sensors in precision agriculture.

Prof. Dr. Sun-Ok Chung
Prof. Dr. Minzan Li
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. 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 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

  • soil sensors
  • plant sensors
  • animal sensors
  • environment sensors
  • farm monitoring and management

Published Papers (3 papers)

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Research

14 pages, 6484 KiB  
Article
Development and Laboratory Evaluation of an Online Controlling Algorithm for Precision Tillage
by Yashar Sabouri, Yousef Abbaspour-Gilandeh, Aliakbar Solhjou, Mohammad Shaker, Mariusz Szymanek and Maciej Sprawka
Sensors 2021, 21(16), 5603; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165603 - 20 Aug 2021
Cited by 2 | Viewed by 1860
Abstract
Soil compaction management relies on costly annual deep tillage. Variable-depth tillage or site-specific tillage modifies the physical properties of the soil at the required zones for the growth of crops. In this study, a depth control system was designed for the subsoiler of [...] Read more.
Soil compaction management relies on costly annual deep tillage. Variable-depth tillage or site-specific tillage modifies the physical properties of the soil at the required zones for the growth of crops. In this study, a depth control system was designed for the subsoiler of the tillage at various depths. For this purpose, an algorithm was written to investigate the subsoiler location and soil compaction. A program was also developed to implement this algorithm using Kinco Builder Software to control the subsoiler depth, which was evaluated on the experimental platform. In this study, four compression sensors were used at a distance of 10 cm up to a depth of 40 cm on the blade mounted at the front of the tractor. The data of these sensors were used as the input and compared with the pressure baseline limit (2.07 MPa), and with the priority to select the greater depth, the depth of subsoiler was determined. At all three modes of sensor activation (single, collective, and combined), this system was able to operate the hydraulic system of the tractor and place the subsoiler at the desired depth through the use of the position sensors. Full article
(This article belongs to the Special Issue Sensor Fusion for Precision Agriculture)
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22 pages, 9107 KiB  
Article
Simulation of Design Factors of a Clutch Pack for Power-Shift Transmission for an Agricultural Tractor
by Md. Abu Ayub Siddique, Wan-Soo Kim, Yeon-Soo Kim, Seung-Yun Baek, Seung-Min Baek, Yong-Joo Kim, Seong-Un Park and Chang-Hyun Choi
Sensors 2020, 20(24), 7293; https://0-doi-org.brum.beds.ac.uk/10.3390/s20247293 - 18 Dec 2020
Cited by 4 | Viewed by 3524
Abstract
The objective of this study is the simulation of the most affected design factors and variables of the clutch pack for the power-shift transmission (PST) of a tractor based measured data. The simulation model, the mathematical model of sliding velocity, a moment of [...] Read more.
The objective of this study is the simulation of the most affected design factors and variables of the clutch pack for the power-shift transmission (PST) of a tractor based measured data. The simulation model, the mathematical model of sliding velocity, a moment of inertia, and clutch engagement pressure of clutch pack were developed using the powertrain and configurations of the real PST tractor. In this study, the sensor fusion method was used to precisely measure the proportional valve pressure by test bench, which was applied to the simulation model. The clutch engagement times were found 1.20 s at all temperatures for determined factors. The engagement pressures have a significant difference at various temperatures (25 to 100 °C) of the hydraulic oils after the 1.20 s but the most affected factors were satisfied with the simulation conditions that ensure the clutch engagement on time. Finally, this sensor fusion method is believed to be helpful in realizing precision agriculture through minimization of power loss and maximum energy efficiency of tractors. Full article
(This article belongs to the Special Issue Sensor Fusion for Precision Agriculture)
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18 pages, 4113 KiB  
Article
Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately
by Ning Liu, Guo Zhao and Gang Liu
Sensors 2020, 20(23), 6792; https://0-doi-org.brum.beds.ac.uk/10.3390/s20236792 - 27 Nov 2020
Cited by 16 | Viewed by 2506
Abstract
In this study, an effective method for accurately detecting Pb(II) concentration was developed by coupling square wave anodic stripping voltammetry (SWASV) with support vector regression (SVR) based on a bismuth-film modified electrode. The interference of different Cu2+ contents on the SWASV signals [...] Read more.
In this study, an effective method for accurately detecting Pb(II) concentration was developed by coupling square wave anodic stripping voltammetry (SWASV) with support vector regression (SVR) based on a bismuth-film modified electrode. The interference of different Cu2+ contents on the SWASV signals of Pb2+ was investigated, and a nonlinear relationship between Pb2+ concentration and the peak currents of Pb2+ and Cu2+ was determined. Thus, an SVR model with two inputs (i.e., peak currents of Pb2+ and Cu2+) and one output (i.e., Pb2+ concentration) was trained to quantify the above nonlinear relationship. The SWASV measurement conditions and the SVR parameters were optimized. In addition, the SVR mode, multiple linear regression model, and direct calibration mode were compared to verify the detection performance by using the determination coefficient (R2) and root-mean-square error (RMSE). Results showed that the SVR model with R2 and RMSE of the test dataset of 0.9942 and 1.1204 μg/L, respectively, had better detection accuracy than other models. Lastly, real soil samples were applied to validate the practicality and accuracy of the developed method for the detection of Pb2+ with approximately equal detection results to the atomic absorption spectroscopy method and a satisfactory average recovery rate of 98.70%. This paper provided a new method for accurately detecting the concentration of heavy metals (HMs) under the interference of non-target HMs for environmental monitoring. Full article
(This article belongs to the Special Issue Sensor Fusion for Precision Agriculture)
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