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Agronomy
Special Issues
Recent Advances in Technologies for Digital Agriculture
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Recent Advances in Technologies for Digital Agriculture

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 1415

Special Issue Editors

Dr. Lorena Parra

E-Mail Website
Guest Editor
Research Institute for Integrated Management of Coastal Areas (IGIC), Universitat Politècnica de València, 46730 Grau de Gandia, Spain
Interests: environmental monitoring; precision agriculture; image processing; crop management; smart cities; physical sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Laura García

E-Mail Website
Guest Editor
Research Institute for Integrated Management of Coastal Areas (IGIC), Universitat Politècnica de València, 46730 Grau de Gandia, Spain
Interests: communication protocols; smart agriculture; algorithms

Special Issue Information

Dear Colleagues,

In the last decade, several technologies have been proposed to enhance the efficiency of agriculture. Most of them are aligned with the digitalization of the primary sector, minimizing its environmental impact. Sensing systems composed of sensors and the use of images are two of the most relevant technologies for digital agriculture.

The aim of this Special Issue is to collect the most up-to-date proposals for digital agriculture based on new technologies or the novel use of a well-known technology. Some potential papers might explore the use of disruptive technologies in current farming systems, evaluate use cases highlighting the most common problems and how to solve them, or show how the sustainability of agriculture has increased by including different technologies. Digitalization can be applied to any part of the food and feed production process, both preharvest and postharvest. The technology might be related to the soil, plants, vehicles, machinery, etc.

In this Special Issue, we aim to exchange knowledge and experiences of any aspects related to the digitalization process in agriculture, including all aspects and perspectives of the used technology and the environment in which the technology is deployed.

Dr. Lorena Parra
Dr. Laura García
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. 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

  • sensors
  • image processing
  • artificial vision
  • AI-based algorithms
  • cloud
  • fog computing
  • edge computing
  • database
  • blockchain
  • wireless sensor networks
  • Internet of Things
  • smart agriculture
  • case study

Published Papers (1 paper)

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Research

13 pages, 3729 KiB  
Article
Quantitative Assessment of Cold Injury in Tea Plants by Terahertz Spectroscopy Method
by Yongzong Lu, Eric Amoah Asante, Hongwei Duan and Yongguang Hu
Agronomy 2023, 13(5), 1376; https://doi.org/10.3390/agronomy13051376 - 15 May 2023
Cited by 2 | Viewed by 991
Abstract
Cold injury (CI) causes irreversible damage to tea plants, which results in decline in the quality of famous teas and huge economic loss. A new, quick, non-destructive method is provided to assess the CI of tea leaf based on terahertz (THz) spectroscopy. Absorbance [...] Read more.
Cold injury (CI) causes irreversible damage to tea plants, which results in decline in the quality of famous teas and huge economic loss. A new, quick, non-destructive method is provided to assess the CI of tea leaf based on terahertz (THz) spectroscopy. Absorbance of the samples was measured with THz spectroscopy in frequency bands from 0.1 to 1.6 THz under low temperature treatments of 4.0, 0, −2.5, −5.0, −7.5, and −10.0 °C. Fast Fourier transformation was explored to decompose the endpoint signal to improve the ratio of signal to air and convert the time-domain spectra to the corresponding frequency-domain spectra. To improve the separation of overlap signals caused by substantial scattering of injured cells in the leaf, two-dimensional correlation spectroscopy (2DCOS) and average intensity (AI) were introduced into the partial least squares regression (PLSR) to build 2DCOS–PLSR and AI–PLSR models. Quantitative assessments of the 2DCOS–PLSR and AI–PLSR models were conducted to evaluate the three models. The assessment results showed that the correlation coefficients of the 2DCOS–PLSR model (R2D) were 0.7873, 0.8305, and 0.9103, respectively. The root mean square errors of the 2DCOS–PLSR model (RMSE2D) were 0.6032, 0.5763, and 0.5221, respectively. For the AI–PLSR model, RAI values were 0.7477, 0.7691, and 0.8974, respectively. RMSEAI values were 0.6038, 0.5962, and 0.5797. The combination of THz spectroscopy with the 2DCOS–PLSR model provided a better benchmark for the input interval selection and improved the accuracy of cold-injury detection results. Full article
(This article belongs to the Special Issue Recent Advances in Technologies for Digital Agriculture)
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