Special Issue "Deep Learning Techniques for Agronomy Applications"

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Innovative Cropping Systems".

Deadline for manuscript submissions: 30 December 2018

Special Issue Editors

Guest Editor
Dr. Chi-Hua Chen

1. Telecommunication Laboratories, Department of Information Management, National Kaohsiung University of Science and Technology, Taiwan
2. Department of Industrial Engineering and Engineering Management, Department of Information Management, National Kaohsiung University of Science and Technology, Taiwan
Website | E-Mail
Interests: internet of things; data mining; cloud computing; cellular network; intelligent transportation system; network security; augmented reality
Guest Editor
Dr. Hsu-Yang Kung

Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 912, Taiwan
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Interests: machine learning; agronomy applications; mobile communications
Guest Editor
Dr. Feng-Jang Hwang

School of Mathematical and Physical Sciences, University of Technology Sydney, Australia
Website | E-Mail
Interests: information processing and data science; computational intelligence

Special Issue Information

Dear Colleagues,

In recent years, the techniques of deep learning have become more and more popular for various applications in agronomy. These techniques can be used to support the prediction and prevention of pest disasters, drought disasters, flooding disasters, typhoon disasters, cold damages, and other agricultural disasters. Furthermore, crop growth models can be also built using these techniques. For instance, supervised learning techniques (e.g., neural network (NN), convolutional neural network (CNN), recurrent neural network (RNN), and ensemble neural networks (ENN)) can be used to forecast weather information and crop growth for improving crop quantities and reducing disaster damages. Furthermore, unsupervised learning techniques (e.g., auto-encoder (AE), de-noise auto-encoder (DAE), restricted Boltzmann machine (RBM), deep belief network (DBN), and deep Boltzmann machine (RBM)) can be used to represent data and reduce dimensions for regulation and overfitting prevention. Therefore, the combination of supervised learning and unsupervised learning techniques can provide a precise estimation and prediction for agronomy applications.

This Special Issue, named “Deep Learning Techniques for Agronomy Applications”, in Agronomy will solicit papers on various disciplines of agronomy applications, but are not limited to:

  • The Prediction of Crop Growth
  • The Prediction and Prevention of Pest Disasters
  • The Prediction and Prevention of Drought Disasters
  • The Prediction and Prevention of Flooding Disasters
  • The Prediction and Prevention of Typhoon Disasters
  • The Prediction and Prevention of Cold Damages
  • The Prediction and Prevention of Agricultural Disasters
  • The Prediction of Crop Quantities
  • Agronomy Applications Based on Deep Learning
  • Agronomy Applications Based on Machine Learning

Best regards,

Dr. Chi-Hua Chen
Dr. Hsu-Yang Kung
Dr. Feng-Jang Hwang
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 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. 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 1000 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

  • deep learning for agronomy applications
  • crop growth prediction; pest disaster prediction
  • drought disaster prediction
  • flooding disaster prediction
  • typhoon disaster prediction
  • cold damage prediction

Published Papers (1 paper)

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Research

Open AccessArticle Automatic Segmentation and Counting of Aphid Nymphs on Leaves Using Convolutional Neural Networks
Agronomy 2018, 8(8), 129; https://doi.org/10.3390/agronomy8080129
Received: 13 July 2018 / Revised: 21 July 2018 / Accepted: 24 July 2018 / Published: 25 July 2018
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Abstract
The presence of pests is one of the main problems in crop production, and obtaining reliable statistics of pest infestation is essential for pest management. Detection of pests should be automated because human monitoring of pests is time-consuming and error-prone. Aphids are among
[...] Read more.
The presence of pests is one of the main problems in crop production, and obtaining reliable statistics of pest infestation is essential for pest management. Detection of pests should be automated because human monitoring of pests is time-consuming and error-prone. Aphids are among the most destructive pests in greenhouses and they reproduce quickly. Automatic detection of aphid nymphs on leaves (especially on the lower surface) using image analysis is a challenging problem due to color similarity and complicated background. In this study, we propose a method for segmentation and counting of aphid nymphs on leaves using convolutional neural networks. Digital images of pakchoi leaves at different aphid infestation stages were obtained, and corresponding pixel-level binary mask annotated. In the test, segmentation results by the proposed method achieved high overlap with annotation by human experts (Dice coefficient of 0.8207). Automatic counting based on segmentation showed high precision (0.9563) and recall (0.9650). The correlation between aphid nymph count by the proposed method and manual counting was high (R2 = 0.99). The proposed method is generic and can be applied for other species of pests. Full article
(This article belongs to the Special Issue Deep Learning Techniques for Agronomy Applications)
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