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Agronomy
Special Issues
Role of Smart Sensors and Control Systems in Agriculture
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Role of Smart Sensors and Control Systems in Agriculture

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Agricultural Biosystem and Biological Engineering".

Deadline for manuscript submissions: closed (5 February 2022) | Viewed by 4587

Special Issue Editor

Dr. Chandra B. Singh

E-Mail Website
Guest Editor
Centre for Applied Research, Innovation and Entrepreneurship (CARIE), Lethbridge Collge, Lethbridge, AB T1K 1L6, Canada
Interests: IOT; sensing; automation

Special Issue Information

Dear Colleagues,

The world population is expected to reach 9.8 billion by 2050 from the current level of 7.6 billion, which will significantly increase the food demand. New developments in digital technology can help us to meet the increased crop demand in a sustainable way. A smart agriculture production system developed by a technology-driven crop management method that integrates internet of things (IOT), wireless sensing technology, cloud-based monitoring and cloud computing, big data analytics, artificial intelligence (AI), machine learning, mathematical modeling, machine vision, automation, and precision agriculture can significantly increase crop yield with optimum use of natural resources (fertilizers, seeds, nutrients, water, pesticides, and energy), minimize pre/postharvest losses, and increase farm operation efficiency and income. We invite researchers to publish their research work related to the application of IOT, AI, machine learning, smart sensing and automation technologies in areas of field crop production, horticulture, green house production, irrigation, and postharvest storage and handling.

Dr. Chandra B. Singh
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. Agronomy 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

  • smart agriculture
  • IOT
  • precision agriculture
  • artificial intelligence
  • automation
  • robotics
  • remote sensing
  • field crops
  • horticulture
  • irrigation
  • postharvest storage and handling

Published Papers (2 papers)

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Research

22 pages, 12384 KiB  
Article
Driving Style Assessment System for Agricultural Tractors: Design and Experimental Validation
by Federico Dettù, Simone Formentin and Sergio Matteo Savaresi
Agronomy 2022, 12(3), 590; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12030590 - 27 Feb 2022
Cited by 1 | Viewed by 1673
Abstract
The diffusion of electronics and sensors in agricultural vehicles is enabling a revolution in the field, leading—among the rest—to the introduction of advanced driving-assistance systems (ADAS). From this perspective, the three key performance indicators (KPI) in a tractor are indeed the driving safety, [...] Read more.
The diffusion of electronics and sensors in agricultural vehicles is enabling a revolution in the field, leading—among the rest—to the introduction of advanced driving-assistance systems (ADAS). From this perspective, the three key performance indicators (KPI) in a tractor are indeed the driving safety, fuel consumption, and operator comfort. Such indexes describe the way the driver interacts with the vehicle, the environment, and other vehicles, respectively. Therefore, such information would be particularly valuable if promptly provided to the driver, e.g., on a dashboard visualizer, so as to adapt the driving style accordingly. Within this context, we propose an algorithmic solution for the on-line estimation of such KPIs. More specifically, by using an off-the-shelf smart-sensor equipped with an Electronic Control Unit (ECU), the chassis accelerations are first processed to extract physics-inspired features and then used to assess the safety and comfort levels; similarly, the speed profile is used to evaluate the economicity of the driving style. The developed method is based upon a cheap setup, and thus it is industrially amenable for its simplicity and robustness. A sensitivity analysis to establish the best sensor placement is finally carried out, together with an extensive experimental campaign considering offroad, urban, and circuit paths. Full article
(This article belongs to the Special Issue Role of Smart Sensors and Control Systems in Agriculture)
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16 pages, 4472 KiB  
Article
Non-Invasive Monitoring of Berry Ripening Using On-the-Go Hyperspectral Imaging in the Vineyard
by Juan Fernández-Novales, Ignacio Barrio and María Paz Diago
Agronomy 2021, 11(12), 2534; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11122534 - 13 Dec 2021
Cited by 6 | Viewed by 2238
Abstract
Hyperspectral imaging offers enormous potential for measuring grape composition with a high degree of representativity, allowing all exposed grapes from the cluster to be examined non-destructively. On-the-go hyperspectral images were acquired using a push broom hyperspectral camera (400–100 nm) that was mounted in [...] Read more.
Hyperspectral imaging offers enormous potential for measuring grape composition with a high degree of representativity, allowing all exposed grapes from the cluster to be examined non-destructively. On-the-go hyperspectral images were acquired using a push broom hyperspectral camera (400–100 nm) that was mounted in the front part of a motorized platform moving at 5 km/h in a commercial Tempranillo vineyard in La Rioja, Spain. Measurements were collected on three dates during grape ripening in 2018 on the east side of the canopy, which was defoliated in the basal fruiting zone. A total of 144 grape clusters were measured for Total soluble solids (TSS), Titratable acidity (TA), pH, Tartaric and Malic acid, Anthocyanins and Total polyphenols, using standard wet chemistry reference methods, throughout the entire experiment. Partial Least Squares (PLS) regression was used to build calibration, cross validation and prediction models for the grape composition parameters. The best performances returned determination coefficients values of external validation (R2p) of 0.82 for TSS, 0.81 for Titratable acidity, 0.61 for pH, 0.62 for Tartaric acid, 0.84 for Malic acid, 0.88 for Anthocyanins and 0.55 for Total polyphenols. The promising results exposed in this work disclosed a notable methodology on-the-go for the non-destructive, in-field assessment of grape quality composition parameters along the ripening period. Full article
(This article belongs to the Special Issue Role of Smart Sensors and Control Systems in Agriculture)
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