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Open AccessEditorial
Agronomy 2019, 9(3), 142; https://doi.org/10.3390/agronomy9030142

Deep Learning Techniques for Agronomy Applications

1
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350100, China
2
Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
3
School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo NSW 2007, Australia
*
Author to whom correspondence should be addressed.
Received: 14 March 2019 / Accepted: 18 March 2019 / Published: 20 March 2019
(This article belongs to the Special Issue Deep Learning Techniques for Agronomy Applications)
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PDF [199 KB, uploaded 20 March 2019]

Abstract

This editorial introduces the Special Issue, entitled “Deep Learning (DL) Techniques for Agronomy Applications”, of Agronomy. Topics covered in this issue include three main parts: (I) DL-based image recognition techniques for agronomy applications, (II) DL-based time series data analysis techniques for agronomy applications, and (III) behavior and strategy analysis for agronomy applications. Three papers on DL-based image recognition techniques for agronomy applications are as follows: (1) “Automatic segmentation and counting of aphid nymphs on leaves using convolutional neural networks,” by Chen et al.; (2) “Estimating body condition score in dairy cows from depth images using convolutional neural networks, transfer learning, and model ensembling techniques,” by Alvarez et al.; and (3) “Development of a mushroom growth measurement system applying deep learning for image recognition,” by Lu et al. One paper on DL-based time series data analysis techniques for agronomy applications is as follows: “LSTM neural network based forecasting model for wheat production in Pakistan,” by Haider et al. One paper on behavior and strategy analysis for agronomy applications is as follows: “Research into the E-learning model of agriculture technology companies: analysis by deep learning,” by Lin et al. View Full-Text
Keywords: deep learning for agronomy applications; crop growth prediction; pest disaster prediction; drought disaster prediction; flooding disaster prediction; typhoon disaster prediction; cold damage prediction deep learning for agronomy applications; crop growth prediction; pest disaster prediction; drought disaster prediction; flooding disaster prediction; typhoon disaster prediction; cold damage prediction
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Chen, C.-H.; Kung, H.-Y.; Hwang, F.-J. Deep Learning Techniques for Agronomy Applications. Agronomy 2019, 9, 142.

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