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Agronomy
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Application of Artificial Neural Networks in Agriculture
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Application of Artificial Neural Networks in 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 (25 February 2022) | Viewed by 12610

Special Issue Editor

Prof. Dr. Dimitrios Moshou

E-Mail Website
Guest Editor
Faculty of Agriculture, Aristotle University of Thessaloniki (A.U.Th.), P.O. 275, 54124 Thessaloniki, Greece
Interests: precision agriculture; machine learning; sensors; mechatronics; neural computing; neuromorphic chips; edge AI; deep learning; self-organizing systems

Special Issue Information

Dear Colleagues,

Artificial neural networks (ANNs) are one of the most well-known tools of Artificial Intelligence. They are very often utilized for the solution of prediction, approximation, and predictive analytics, and they have been in use in agronomy for quite some time already. They are usually found in precision agriculture tools and as parts of decision support systems for farm management. Artificial neural networks are currently replacing the traditional methods of modeling in agronomy. The variety of applications where artificial neural networks have been applied in agricultural operations is quite large. Researchers worldwide have used artificial neural networks to aid agricultural production processes for a while now, managing to enhance efficiency and produce agricultural products of high quality.

This is a joint Special Issue of Agronomy and AI, titled “Application of Artificial Neural Networks in Agriculture” that the objective of this Special Issue is to publish high-quality research and review papers that describe the development and application of artificial neural networks in facilitating the solution of agronomical tasks and current problems in agronomy.

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. 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

  • precision agriculture
  • internet of things
  • sensors
  • mechatronics
  • robotics
  • smart farming
  • neural computing
  • sensor fusion
  • big data
  • decision support systems

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Published Papers (2 papers)

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Research

19 pages, 5769 KiB  
Article
A Reversible Automatic Selection Normalization (RASN) Deep Network for Predicting in the Smart Agriculture System
by Xuebo Jin, Jiashuai Zhang, Jianlei Kong, Tingli Su and Yuting Bai
Agronomy 2022, 12(3), 591; https://doi.org/10.3390/agronomy12030591 - 27 Feb 2022
Cited by 81 | Viewed by 5447
Abstract
Due to the nonlinear modeling capabilities, deep learning prediction networks have become widely used for smart agriculture. Because the sensing data has noise and complex nonlinearity, it is still an open topic to improve its performance. This paper proposes a Reversible Automatic Selection [...] Read more.
Due to the nonlinear modeling capabilities, deep learning prediction networks have become widely used for smart agriculture. Because the sensing data has noise and complex nonlinearity, it is still an open topic to improve its performance. This paper proposes a Reversible Automatic Selection Normalization (RASN) network, integrating the normalization and renormalization layer to evaluate and select the normalization module of the prediction model. The prediction accuracy has been improved effectively by scaling and translating the input with learnable parameters. The application results of the prediction show that the model has good prediction ability and adaptability for the greenhouse in the Smart Agriculture System. Full article
(This article belongs to the Special Issue Application of Artificial Neural Networks in Agriculture)
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26 pages, 13130 KiB  
Article
Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks
by Rolando Miragaia, Francisco Chávez, Josefa Díaz, Antonio Vivas, Maria Henar Prieto and Maria José Moñino
Agronomy 2021, 11(11), 2353; https://doi.org/10.3390/agronomy11112353 - 20 Nov 2021
Cited by 11 | Viewed by 6101
Abstract
Digitization and technological transformation in agriculture is no longer something of the future, but of the present. Many crops are being managed by using sophisticated sensors that allow farmers to know the status of their crops at all times. This modernization of crops [...] Read more.
Digitization and technological transformation in agriculture is no longer something of the future, but of the present. Many crops are being managed by using sophisticated sensors that allow farmers to know the status of their crops at all times. This modernization of crops also allows for better quality harvests as well as significant cost savings. In this study, we present a tool based on Deep Learning that allows us to analyse different varieties of plums using image analysis to identify the variety and its ripeness status. The novelty of the system is the conditions in which the designed algorithm can work. An uncontrolled photographic acquisition method has been implemented. The user can take a photograph with any device, smartphone, camera, etc., directly in the field, regardless of light conditions, focus, etc. The robustness of the system presented allows us to differentiate, with 92.83% effectiveness, three varieties of plums through images taken directly in the field and values above 94% when the ripening stage of each variety is analyzed independently. We have worked with three varieties of plums, Red Beaut, Black Diamond and Angeleno, with different ripening cycles. This has allowed us to obtain a robust classification system that will allow users to differentiate between these varieties and subsequently determine the ripening stage of the particular variety. Full article
(This article belongs to the Special Issue Application of Artificial Neural Networks in Agriculture)
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