Deep Learning Techniques for Agronomy Applications
2019
Chi-Hua Chen | Hsu-Yang Kung | Feng-Jang Hwang
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.
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