Deep Learning Techniques for Agronomy Applications

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

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