Special Issue "Applications of Computer Science in Agricultural Engineering"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Engineering".

Deadline for manuscript submissions: 15 November 2021.

Special Issue Editor

Dr. Górnicki Krzysztof
E-Mail Website
Guest Editor
Institute of Mechanical Engineering, Warsaw University of Life Sciences, Nowoursynowska 164 St., 02-787 Warsaw, Poland
Interests: heat and mass transfer; drying; rehydration; modelling; ANN, optimization

Special Issue Information

Dear Colleagues,

This Special Issue will focus on the application of computer science in agricultural engineering. Agricultural engineering is the branch of engineering that deals with the design and exploitation of farm machinery and devices, the location and planning of farm structures, farm drainage, soil management and erosion control, water supply and irrigation, rural electrification, farm product processing and deriving renewable energy from agricultural products. Computer science is necessary in engineering, especially agricultural engineering, to solve current engineering problems. Therefore, we invite papers on applied computer science regarding:

  • Computer simulations;
  • Device and machine design;
  • Device and machine exploitation;
  • Process optimization;
  • System and process modelling;
  • Technical diagnostics.

Dr. Górnicki Krzysztof
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 papers will be 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. Applied Sciences 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 2000 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

  • agricultural engineering
  • agricultural machinery
  • artificial intelligence
  • computer science
  • computer simulation
  • drying
  • irrigation systems
  • process modelling
  • optimization
  • technical diagnostics
  • renewable energy sources

Published Papers (1 paper)

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Research

Article
Plant Diseases Identification through a Discount Momentum Optimizer in Deep Learning
Appl. Sci. 2021, 11(20), 9468; https://doi.org/10.3390/app11209468 - 12 Oct 2021
Viewed by 211
Abstract
Deep learning proves its promising results in various domains. The automatic identification of plant diseases with deep convolutional neural networks attracts a lot of attention at present. This article extends stochastic gradient descent momentum optimizer and presents a discount momentum (DM) deep learning [...] Read more.
Deep learning proves its promising results in various domains. The automatic identification of plant diseases with deep convolutional neural networks attracts a lot of attention at present. This article extends stochastic gradient descent momentum optimizer and presents a discount momentum (DM) deep learning optimizer for plant diseases identification. To examine the recognition and generalization capability of the DM optimizer, we discuss the hyper-parameter tuning and convolutional neural networks models across the plantvillage dataset. We further conduct comparison experiments on popular non-adaptive learning rate methods. The proposed approach achieves an average validation accuracy of no less than 97% for plant diseases prediction on several state-of-the-art deep learning models and holds a low sensitivity to hyper-parameter settings. Experimental results demonstrate that the DM method can bring a higher identification performance, while still maintaining a competitive performance over other non-adaptive learning rate methods in terms of both training speed and generalization. Full article
(This article belongs to the Special Issue Applications of Computer Science in Agricultural Engineering)
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