Deep Learning Techniques for Agronomy Applications

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.

Keywords

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