AI, Sensors and Robotics in Plant Phenotyping and Precision Agriculture, volume III

52K

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93

authors

15

articles

AI, Sensors and Robotics in Plant Phenotyping and Precision Agriculture, volume III

52K

views

93

authors

15

articles

Editors

6

Yongliang Qiao

University of Adelaide

João Valente

Centre for Automation and Robotics, Spanish National Research Council (CSIC)

Yu Jiang

Cornell Agritech, College of Agriculture and Life Sciences, Cornell University

Zhao Zhang

China Agricultural University

Zhou Zhang

University of Wisconsin-Madison

Daobilige Su

College of Engineering, China Agricultural University

Impact

About

This Research Topic is part of the article collection series - AI, Sensors and Robotics in Plant Phenotyping and Precision Agriculture.

Plant production needs to be improved in a sustainable manner to accommodate a rising global population and anticipated climate change. Plant phenotyping plays an essential role in optimizing the genetic potential, plant breeding, and resource deployment in plant production. Recent and comprehensive plant phenotyping emerges from the dynamic and local interaction of phenotypes with the spatially and temporally dynamic environment above and below ground, while assessing complex plant traits such as growth, development, tolerance, resistance, physiology, ecology, yield, and basic quantitative parameters. The integration of smart sensors, big data, artificial intelligence, non-invasive technologies, and information technologies is pivotal in deriving accurate plant-physiological parameters at high throughput for precision agriculture. Notably, the advancement of wearable plant sensors, which are environmentally friendly for long-term use, marks a significant stride toward smart, data-driven agriculture. This technology provides novel solutions to complex challenges in agricultural production and environmental monitoring.

This Research Topic will welcome papers involved in research on plant phenotyping and precision agriculture. The selection of papers for publication will depend on the quality and the rigor of research. Specific topics include, but are not limited to advanced methods for imaging technologies, sensor setups, and data processing in plant phenotyping and precision agriculture:

• Panchromatic, multispectral, and hyperspectral approaches;
• AI algorithms for agriculture;
• Wearable plant sensors;
• High-throughput sensor platforms;
• Robotics for plant phenotyping;
• Field phenotyping and yield estimation;
• Disease and stress detection;
• Data analysis in plant phenotyping;
• Sensors and sensing techniques for phenotyping;
• Aerial plant phenotyping.

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Editors

Yongliang Qiao

University of Adelaide

João Valente

Centre for Automation and Robotics, Spanish National Research Council (CSIC)

Yu Jiang

Cornell Agritech, College of Agriculture and Life Sciences, Cornell University

Zhao Zhang

China Agricultural University

Zhou Zhang

University of Wisconsin-Madison

Daobilige Su

College of Engineering, China Agricultural University

Impact

51,979 Total views

38,293 Article views

11,847 Article downloads

1,839 Topic views

Published In

Frontiers in Plant Science

Sustainable and Intelligent Phytoprotection

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