Convolutional Neural Networks and Deep Learning for Crop Improvement and Production

65.5K

views

91

authors

16

articles

Convolutional Neural Networks and Deep Learning for Crop Improvement and Production

65.5K

views

91

authors

16

articles

Editorial

18 November 2022

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

Wanneng Yang

,

Gregorio Egea

and

Kioumars Ghamkhar

1,170 views

0 citations

Editors

3

Wanneng Yang

Huazhong Agricultural University

Kioumars Ghamkhar

AgResearch Ltd

Gregorio Egea

Higher Technical School of Agricultural Engineering, University of Seville

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Learning about the natural world requires observation and recognizing patterns from which hypotheses and inferences are made to guide various decisions. Learning about plants is no different from the processes of learning about the natural world except that plants are immobile and that makes their observation easier than animals.

Convolutional Neural Networks (CNN) and Deep Learning (DL) are powerful architectures of artificial intelligence that use big data to enable learning by recognition of objects and patterns. The ability of machines to resolve multidimensional data approaches, exceeding the human ability to analyze with minimal error offers a significant advantage that when correctly applied allows for the discovery of important information. The field of plant science offers unlimited opportunities for learning by object and pattern recognitions and hence the application of machine learning and artificial intelligence. Examples of these involve various aspects of phenotyping, diagnostics, species richness, plant morphological and anatomical studies as well as in growth and developmental changes. Beyond learning, CNN and DL offer the potential to automate multiple processes initially relying on human observation for quick and accurate classification and/ or predictions.

This research topic aims to highlight the innovative applications of CNN and DL to facilitate crop improvement and production as well as advances to CNN and DL inspired specifically by the unique data types encountered in Plant Science.

All article types published by Frontiers in Plant Science are welcome.

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Editors

Wanneng Yang

Huazhong Agricultural University

Kioumars Ghamkhar

AgResearch Ltd

Gregorio Egea

Higher Technical School of Agricultural Engineering, University of Seville

Impact

65,517 Total views

51,637 Article views

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Published In

Frontiers in Plant Science

Technical Advances in Plant Science

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