Editorial: Convolutional neural networks and deep learning for crop improvement and production

Wanneng Yang; Gregorio Egea; Kioumars Ghamkhar

doi:10.3389/fpls.2022.1079148

Editorial: Convolutional neural networks and deep learning for crop improvement and production

Front Plant Sci. 2022 Nov 18:13:1079148. doi: 10.3389/fpls.2022.1079148. eCollection 2022.

Authors

Wanneng Yang¹, Gregorio Egea², Kioumars Ghamkhar³

Affiliations

¹ National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.
² Area of Agroforestry Engineering, School of Agricultural Engineering, University of Seville, Seville, Spain.
³ Margot Forde Germplasm Centre, Grasslands Research Centre, AgResearch, Palmerston North, New Zealand.

No abstract available

Keywords: artificial intelligence; big data; convolutional neural networks (CNN); crop improvement; crop production; deep learning (DL); object recognition; pattern recognition.

Publication types

Editorial