Deep Learning Techniques for Agronomy Applications

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.

[1]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[2]  Chi-Hua Chen,et al.  Design and application of augmented reality query-answering system in mobile phone information navigation , 2015, Expert Syst. Appl..

[3]  Hsu-Yang Kung,et al.  Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method , 2016 .

[4]  Wei Kuang Lai,et al.  Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data , 2016 .

[5]  Yonggang Wen,et al.  Multicolumn Bidirectional Long Short-Term Memory for Mobile Devices-Based Human Activity Recognition , 2016, IEEE Internet of Things Journal.

[6]  Chi-Hua Chen,et al.  Combining the Technology Acceptance Model and Uses and Gratifications Theory to examine the usage behavior of an Augmented Reality Tour-sharing Application , 2017, Symmetry.

[7]  Chi-Chun Lo,et al.  An Augmented Reality Question Answering System Based on Ensemble Neural Networks , 2017, IEEE Access.

[8]  Ling Wu,et al.  Method for Mapping Rice Fields in Complex Landscape Areas Based on Pre-Trained Convolutional Neural Network from HJ-1 A/B Data , 2018, ISPRS Int. J. Geo Inf..

[9]  Hui Zhou,et al.  Joint Alternate Small Convolution and Feature Reuse for Hyperspectral Image Classification , 2018, ISPRS Int. J. Geo Inf..

[10]  Adel Hafiane,et al.  Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images , 2018, Comput. Electron. Agric..

[11]  Joon-Hyuk Chang,et al.  Dempster–Shafer Fusion Based on a Deep Boltzmann Machine for Blood Pressure Estimation , 2018 .

[12]  Ying Mei,et al.  An Imputation Method for Missing Data Based on an Extreme Learning Machine Auto-Encoder , 2018, IEEE Access.

[13]  Chi-Hua Chen,et al.  An Arrival Time Prediction Method for Bus System , 2018, IEEE Internet of Things Journal.

[14]  Jeng-Shyang Pan,et al.  α-Fraction First Strategy for Hierarchical Model in Wireless Sensor Networks , 2018 .

[15]  Xiang Cheng,et al.  Mobile Demand Forecasting via Deep Graph-Sequence Spatiotemporal Modeling in Cellular Networks , 2018, IEEE Internet of Things Journal.

[16]  Rong Huang,et al.  Web spam classification method based on deep belief networks , 2018, Expert Syst. Appl..

[17]  Tao Wang,et al.  Automatic Segmentation and Counting of Aphid Nymphs on Leaves Using Convolutional Neural Networks , 2018, Agronomy.

[18]  Zonghai Chen,et al.  Traffic State Estimation of Signalized Intersections Based on Stacked Denoising Auto-Encoder Model , 2018, Wirel. Pers. Commun..

[19]  Rijo Jackson Tom,et al.  IoT based hydroponics system using Deep Neural Networks , 2018, Comput. Electron. Agric..

[20]  Naixue Xiong,et al.  Learning Sparse Representation With Variational Auto-Encoder for Anomaly Detection , 2018, IEEE Access.

[21]  Adel Hafiane,et al.  Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images , 2018, Remote. Sens..

[22]  Yonghui Song,et al.  A New Deep-Q-Learning-Based Transmission Scheduling Mechanism for the Cognitive Internet of Things , 2018, IEEE Internet of Things Journal.

[23]  Hamid Saeed Khan,et al.  Modern Trends in Hyperspectral Image Analysis: A Review , 2018, IEEE Access.

[24]  Alejandro Zunino,et al.  Estimating Body Condition Score in Dairy Cows From Depth Images Using Convolutional Neural Networks, Transfer Learning and Model Ensembling Techniques , 2019, Agronomy.

[25]  Chi-Hua Chen,et al.  The Persuasion Effect of Sociability in the Design and Use of an Augmented Reality Wedding Invitation App , 2019 .

[26]  Yong Shi,et al.  Learning Robust Auto-Encoders With Regularizer for Linearity and Sparsity , 2019, IEEE Access.

[27]  Yongsheng Zhang,et al.  An Improved Complex-Valued Recurrent Neural Network Model for Time-Varying Complex-Valued Sylvester Equation , 2019, IEEE Access.

[28]  Jiun-Jian Liaw,et al.  Development of a Mushroom Growth Measurement System Applying Deep Learning for Image Recognition , 2019, Agronomy.

[29]  Ren Ping Liu,et al.  ResInNet: A Novel Deep Neural Network With Feature Reuse for Internet of Things , 2019, IEEE Internet of Things Journal.

[30]  M. Shamim Hossain,et al.  Multilevel Weighted Feature Fusion Using Convolutional Neural Networks for EEG Motor Imagery Classification , 2019, IEEE Access.

[31]  Shang Gao,et al.  An End-to-End Load Balancer Based on Deep Learning for Vehicular Network Traffic Control , 2019, IEEE Internet of Things Journal.

[32]  Guan Gui,et al.  Echo-State Restricted Boltzmann Machines: A Perspective on Information Compensation , 2019, IEEE Access.

[33]  Wenhu Tang,et al.  Deep Learning for Daily Peak Load Forecasting–A Novel Gated Recurrent Neural Network Combining Dynamic Time Warping , 2019, IEEE Access.

[34]  Richong Zhang,et al.  Recurrent Neural Networks With Finite Memory Length , 2019, IEEE Access.

[35]  Chi-Hsuan Lin,et al.  Research into the E-Learning Model of Agriculture Technology Companies: Analysis by Deep Learning , 2019, Agronomy.

[36]  Wen Wen,et al.  Embedding Logic Rules Into Recurrent Neural Networks , 2019, IEEE Access.

[37]  Muhammad Kamran,et al.  LSTM Neural Network Based Forecasting Model for Wheat Production in Pakistan , 2019, Agronomy.

[38]  Sushma Jain,et al.  Reference evapotranspiration estimation and modeling of the Punjab Northern India using deep learning , 2019, Comput. Electron. Agric..

[39]  Saman Haratizadeh,et al.  Entity representation for pairwise collaborative ranking using restricted Boltzmann machine , 2019, Expert Syst. Appl..

[40]  Yixian Yang,et al.  Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks , 2019, Applied Sciences.

[41]  Sang Hyun Park,et al.  Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network , 2019, IEEE Access.

[42]  Qishan Zhang,et al.  A Mobile Positioning Method Based on Deep Learning Techniques , 2018, Electronics.

[43]  Hang Zhou,et al.  Deep learning based multi-temporal crop classification , 2019, Remote Sensing of Environment.

[44]  Kuangrong Hao,et al.  Using a Vertical-Stream Variational Auto-Encoder to Generate Segment-Based Images and Its Biological Plausibility for Modelling the Visual Pathways , 2019, IEEE Access.

[45]  Jiancheng Luo,et al.  Land parcel-based digital soil mapping of soil nutrient properties in an alluvial-diluvia plain agricultural area in China , 2019, Geoderma.