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Deep Learning Techniques for Agronomy Applications
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DOI：10.3390/agronomy9030142.png)
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Abstract
En 中文
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
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