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Special Issue "Sensors in Agriculture 2021–2022"

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

Deadline for manuscript submissions: 30 January 2023 | Viewed by 1878

Special Issue Editor

Dr. Dimitrios Moshou
E-Mail Website
Guest Editor
Head of Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki (A.U.Th.), P.O. 275, 54124 Thessaloniki, Greece
Interests: mountain biking; youth mountain biking; injury surveillance system; youth sports; COVID-19; national interscholastic cycling association; sports epidemiology; injury prevention
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Special Issue Information

Dear Colleagues,

Agriculture requires technical solutions for increasing production while reducing environmental impact by reducing the application of agrochemicals and increasing the use of environmentally friendly management practices. A benefit of this is the reduction of production costs. Sensor technologies produce tools to achieve the abovementioned goals. The explosive technological advances and developments in recent years have facilitated the attainment of these objectives removing many barriers for their implementation, including the reservations expressed by farmers. Precision agriculture is an emerging area where sensor-based technologies play an important role.

Farmers, researchers, and technical manufacturers are combining their efforts to find efficient solutions, improvements in production, and reductions in costs. This Special Issue aims to bring together recent research and developments concerning novel sensors and their applications in agriculture. Sensors in agriculture are based on the requirements of farmers, according to the farming operations that need to be addressed. Papers addressing sensor development for a wide range of agricultural tasks, including, but not limited to, recent research and developments in the following areas are expected:

  • Optical sensors: Hyperspectral, multispectral, fluorescence, and thermal sensing
  • Sensors for crop health status determination
  • Sensors for crop phenotyping, germination, emergence, and determination of the different growth stages of crops
  • Sensors for the detection of microorganism and pest management
  • Airborne sensors (UAV)
  • Multisensor systems, sensor fusion
  • Non-destructive soil sensing
  • Yield estimation and prediction
  • Detection and identification of crops and weeds
  • Sensors for the detection of fruits
  • Sensors for fruit quality determination
  • Sensors for weed control
  • Volatile components detection, electronic noses, and tongues
  • Sensors for robot navigation, localization and mapping, and environmental awareness
  • Sensors for robotic applications in crop management
  • Sensors for positioning, navigation and obstacle detection
  • Sensor networks in agriculture, wearable sensors, the Internet of Things
  • Low energy, disposable, and energy harvesting sensors in agriculture
  • Deep learning from sensor data in agriculture

This special issue is renewal, you could find more information by following link: https://www.mdpi.com/journal/sensors/special_issues/Sensors_Agriculture_2020

Prof. Dr. Dimitrios Moshou
Guest Editor

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 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.

Published Papers (1 paper)

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Research

Article
Estimation of Spatial-Temporal Distribution of Grazing Intensity Based on Sheep Trajectory Data
Sensors 2022, 22(4), 1469; https://doi.org/10.3390/s22041469 - 14 Feb 2022
Cited by 1 | Viewed by 762
Abstract
In the arid grasslands of northern China, unreasonable grazing methods can reduce the water content and species numbers of grassland vegetation. This project uses solar-powered GPS collars to obtain track data for sheep grazing. In order to eliminate the trajectory data of the [...] Read more.
In the arid grasslands of northern China, unreasonable grazing methods can reduce the water content and species numbers of grassland vegetation. This project uses solar-powered GPS collars to obtain track data for sheep grazing. In order to eliminate the trajectory data of the rest area and the drinking area, the kernel density analysis method was used to cluster the trajectory point data. At the same time, the vegetation index of the experimental area, including elevation, slope and aspect data, was obtained through satellite remote sensing images. Therefore, using trajectory data and remote sensing image data to establish a neural network model of grazing intensity of sheep, the accuracy of the model could be high. The results showed that the best input parameters of the model were the combination of vegetation index, sheep weight, duration, moving distance and ambient temperature, where the coefficient of determination R2=0.97, and the mean square error MSE = 0.73. The error of grazing intensity obtained by the model is the smallest, and the spatial-temporal distribution of grazing intensity can reflect the actual situation of grazing intensity in different locations. Monitoring the grazing behavior of sheep in real time and obtaining the spatial-temporal distribution of their grazing intensity can provide a basis for scientific grazing. Full article
(This article belongs to the Special Issue Sensors in Agriculture 2021–2022)
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