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Sensors and Digital Technologies for Smart Agriculture

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 1476

Special Issue Editor


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Guest Editor
Department of Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
Interests: automation and electronics in agriculture; sensors; wireless sensor networks; precision agriculture; artificial intelligence; machine learning; smart sensors; edge computing and reinforcement learning; smart agriculture; Internet of Things; embedded intelligence; technology governance; Agriculture 4.0
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Special Issue Information

Dear Colleagues,

Sustainability is defined as the process of maintaining the vital changes in a balanced environment, where the exploitation of resources is presumed for humanitarian needs and aspirations. In this regard, institutional changes, the orientation of technological development and the direction of public and private investments must all be in harmony and advance current and future potential to meet human needs. Sustainability, based on the principles of Systems Thinking, is usually defined through the three interconnected pillars of the environment, economics, and society, with sub-domains of sustainable development considering political, cultural, and technological aspects.

We are pleased to invite you to this Special Issue, where we will try to set the proper roadmap of agricultural digitalization and avoid many wrong efforts realised in the business intelligence sector.

Knowledge, the principal production input applied through a digitized biosphere, is a present stage necessity for fast deployment of research new results in a fast-changing bio-technological environment. Smart farming is a development that emphasizes the use of information and communication technology in the cyber–physical, deep-knowledge-based farm management cycle. The sustainable development of agriculture to meet future vital needs of humanity has been recognized as a foremost priority by all political parties and governments around the world. To avoid any contradiction between sustainability and development, all the domains (mentioned previously) must be well sited in the planning stage.

All in all, digitalization depends on a successful infrastructure design to provide an effective data aggregation tool while the end results rest on system intelligence and wisdom that is contingent upon cropping knowledge evolution casting an ever-evolving eco-system.

The world agriculture is undergoing an irreversible transformation due to climate change worsening resource supplies while green production intensification is a necessity. It is empowered by wireless Internet of Things applications, becoming smarter via the collection of data and intelligent information retrieval.

In this Special Issue, original research articles and reviews are welcome. Research areas may include the following:

  1. Smart sensors and other devices, from Earth to space (IOT), produce big data that provide unparalleled knowledge mediated decision-making mechanisms, improving all sustainability domains.
  2. Big Data is expected to have a great impact on smart farming and the whole supply chain.
  3. Technology such as:
    a. Infrastructure open hardware and software platforms;
    b. Communications and data standards, security and data categorization as private or open (sharing), in a hypermarket environment;
    c. Farm-level competent hardware and knowledge applications

Prof. Dr. Nick Sigrimis
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 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

  • sensor
  • digital agriculture

Published Papers (2 papers)

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Research

18 pages, 10059 KiB  
Article
Study on the Improvement of Droplet Penetration Effect by Nozzle Tilt Angle under the Influence of Orthogonal Side Wind
by Daozong Sun, Junyutai Hu, Xinghan Huang, Wenhao Luo, Shuran Song and Xiuyun Xue
Sensors 2024, 24(9), 2685; https://doi.org/10.3390/s24092685 - 24 Apr 2024
Viewed by 314
Abstract
This study investigates the impact of varying side wind velocities and nozzle inclination angles on droplet penetration during plant protection spraying operations, focusing on citrus trees. Experiments were conducted across four wind speed levels (0, 1, 2, 3 m/s) perpendicular to the nozzle [...] Read more.
This study investigates the impact of varying side wind velocities and nozzle inclination angles on droplet penetration during plant protection spraying operations, focusing on citrus trees. Experiments were conducted across four wind speed levels (0, 1, 2, 3 m/s) perpendicular to the nozzle direction and seven nozzle inclination levels (0°, 8°, 15°, 23°, 30°, 38°, 45°) to evaluate droplet distribution under different spraying parameters. A baseline condition with 0 m/s wind speed and a 0° nozzle angle served as the control. Utilizing Computational Fluid Dynamics (CFD) and regression analysis techniques in conjunction with field trials, the droplet penetration was analyzed. Results indicate that at constant wind speeds, adjusting the nozzle inclination angle against the direction of the side wind can significantly enhance droplet deposition in the canopy, with a 23° inclination providing the optimal increase in deposition volume, averaging a change of +16.705 μL/cm2. Multivariate nonlinear regression analysis revealed that both wind speed and nozzle inclination angle significantly affect the droplet penetration ratio, demonstrating a correlation between these factors, with wind speed exerting a greater impact than nozzle angle. Increasing the nozzle inclination angle at higher wind speeds improves the penetration ratio, with the optimal parameters being a 23° angle and 3 m/s wind speed, showing a 12.6% improvement over the control. The model fitted for the impact of nozzle angle and wind speed on droplet penetration was validated through field experiments, identifying optimal angles for enhancing penetration at wind speeds of 1, 2, and 3 m/s as 8°, 17°, and 25°, respectively. This research provides insights for improving droplet penetration techniques in plant protection operations. Full article
(This article belongs to the Special Issue Sensors and Digital Technologies for Smart Agriculture)
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18 pages, 4529 KiB  
Article
Enhancing Leaf Area Index Estimation for Maize with Tower-Based Multi-Angular Spectral Observations
by Lieshen Yan, Xinjie Liu, Xia Jing, Liying Geng, Tao Che and Liangyun Liu
Sensors 2023, 23(22), 9121; https://doi.org/10.3390/s23229121 - 11 Nov 2023
Viewed by 852
Abstract
The leaf area index (LAI) played a crucial role in ecological, hydrological, and climate models. The normalized difference vegetation index (NDVI) has been a widely used tool for LAI estimation. However, the NDVI quickly saturates in dense vegetation and is susceptible to soil [...] Read more.
The leaf area index (LAI) played a crucial role in ecological, hydrological, and climate models. The normalized difference vegetation index (NDVI) has been a widely used tool for LAI estimation. However, the NDVI quickly saturates in dense vegetation and is susceptible to soil background interference in sparse vegetation. We proposed a multi-angular NDVI (MAVI) to enhance LAI estimation using tower-based multi-angular observations, aiming to minimize the interference of soil background and saturation effects. Our methodology involved collecting continuous tower-based multi-angular reflectance and the LAI over a three-year period in maize cropland. Then we proposed the MAVI based on an analysis of how canopy reflectance varies with solar zenith angle (SZA). Finally, we quantitatively evaluated the MAVI’s performance in LAI retrieval by comparing it to eight other vegetation indices (VIs). Statistical tests revealed that the MAVI exhibited an improved curvilinear relationship with the LAI when the NDVI is corrected using multi-angular observations (R2 = 0.945, RMSE = 0.345, rRMSE = 0.147). Furthermore, the MAVI-based model effectively mitigated soil background effects in sparse vegetation (R2 = 0.934, RMSE = 0.155, rRMSE = 0.157). Our findings demonstrated the utility of tower-based multi-angular spectral observations in LAI retrieval, having the potential to provide continuous data for validating space-borne LAI products. This research significantly expanded the potential applications of multi-angular observations. Full article
(This article belongs to the Special Issue Sensors and Digital Technologies for Smart Agriculture)
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