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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-03-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/9/3/142 |
| _version_ | 1818613809084891136 |
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| author | Chi-Hua Chen Hsu-Yang Kung Feng-Jang Hwang |
| author_facet | Chi-Hua Chen Hsu-Yang Kung Feng-Jang Hwang |
| author_sort | Chi-Hua Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T16:08:01Z |
| format | Article |
| id | doaj.art-d9225de92739435c8cb9193ab3d9656b |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-12-16T16:08:01Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-d9225de92739435c8cb9193ab3d9656b2022-12-21T22:25:18ZengMDPI AGAgronomy2073-43952019-03-019314210.3390/agronomy9030142agronomy9030142Deep Learning Techniques for Agronomy ApplicationsChi-Hua Chen0Hsu-Yang Kung1Feng-Jang Hwang2College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350100, ChinaDepartment of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 91201, TaiwanSchool of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo NSW 2007, AustraliaThis 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.https://www.mdpi.com/2073-4395/9/3/142deep learning for agronomy applicationscrop growth predictionpest disaster predictiondrought disaster predictionflooding disaster predictiontyphoon disaster predictioncold damage prediction |
| spellingShingle | Chi-Hua Chen Hsu-Yang Kung Feng-Jang Hwang Deep Learning Techniques for Agronomy Applications Agronomy deep learning for agronomy applications crop growth prediction pest disaster prediction drought disaster prediction flooding disaster prediction typhoon disaster prediction cold damage prediction |
| title | Deep Learning Techniques for Agronomy Applications |
| title_full | Deep Learning Techniques for Agronomy Applications |
| title_fullStr | Deep Learning Techniques for Agronomy Applications |
| title_full_unstemmed | Deep Learning Techniques for Agronomy Applications |
| title_short | Deep Learning Techniques for Agronomy Applications |
| title_sort | deep learning techniques for agronomy applications |
| topic | deep learning for agronomy applications crop growth prediction pest disaster prediction drought disaster prediction flooding disaster prediction typhoon disaster prediction cold damage prediction |
| url | https://www.mdpi.com/2073-4395/9/3/142 |
| work_keys_str_mv | AT chihuachen deeplearningtechniquesforagronomyapplications AT hsuyangkung deeplearningtechniquesforagronomyapplications AT fengjanghwang deeplearningtechniquesforagronomyapplications |