Vision, Learning, and Robotics: AI for Plants in the 2020s

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Editorial

23 December 2024

Editorial: Vision, learning, and robotics: AI for plants in the 2020s

Zhenghong Yu

,

Luca Iocchi

,

Jeffrey Too Chuan Tan

,

Huabing Zhou

, 1 more and

Hao Lu

644 views

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Editors

6

Zhenghong Yu

Guangdong Polytechnic of Science and Technology

Luca Iocchi

Sapienza University of Rome

Jeffrey Too Chuan Tan

Genovasi University College

Huabing Zhou

Wuhan Institute of Technology

Changcai Yang

Fujian Agriculture and Forestry University

Hao Lu

Huazhong University of Science and Technology

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About

Population growth and climate change have posed significant challenges to crop breeding. The identification of crop agronomic traits is fundamental to breeding, yet currently, the collection of such traits is largely reliant on the subjective judgment of workers or ground test equipment, which is both costly and inefficient. In recent years, the advancement of artificial intelligence (AI) has revolutionized modern agriculture and plant science. AI is a rapidly evolving field with datasets, models, and algorithms constantly changing. It has also been increasingly applied to unmanned aerial vehicles, field robots, and hyperspectral imaging sensors, offering great potential for large-scale crop growth monitoring and precision management, driving the agricultural field from mechanization to automation and intelligence.

This research topic aims to encourage research work that actively embraces new AI ideas/progress and combines these new ideas/technologies with robotics or sensing technologies for applications in plant phenotyping or precision agriculture. We encourage the use of technologies that have seen significant development in the AI community after 2020, such as vision transformers and diffusion models.

Topics of interest for this Research Topic include, but are not limited to:
• Research on the application of Artificial Intelligence in crop breeding, crop growth modeling, and crop phenotyping, including image recognition, expert systems, crop object detection, and other algorithms.
• Analysis of crop traits based on hyperspectral or multispectral sensors.
• Investigation of the integration of Artificial Intelligence with robots and other devices for crop growth monitoring and agricultural operations.
• Exploration of the relationship between crop phenotypes and genotypes using Artificial Intelligence.

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Editors

Zhenghong Yu

Guangdong Polytechnic of Science and Technology

Luca Iocchi

Sapienza University of Rome

Jeffrey Too Chuan Tan

Genovasi University College

Huabing Zhou

Wuhan Institute of Technology

Changcai Yang

Fujian Agriculture and Forestry University

Hao Lu

Huazhong University of Science and Technology

Impact

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Published In

Frontiers in Plant Science

Technical Advances in Plant Science

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