Edited and Reviewed by: Ruslan Kalendar, University of Helsinki, Finland
*Correspondence: Zhenghong Yu,
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With the growth of the global population and increasing demand for food, agricultural production is under significant pressure. At the same time, climate change and resource constraints exacerbate these challenges, further heightening the need for sustainable agricultural practices. To address these complex issues, the field of plant science is undergoing a technological revolution. The rapid advancement of artificial intelligence (AI), computer vision, and robotics is redefining how plants are studied and agricultural practices are managed. From high-throughput phenotyping to precision agriculture and real-time monitoring, these technologies are dramatically improving efficiency and accuracy, laying a foundation for more resilient and sustainable agricultural systems. This Research Topic brings together pioneering studies to demonstrate how AI is advancing plant science and providing innovative solutions for modern agriculture.
The articles in this Research Topic showcase innovations across multiple fields. These contributions can be summarized into five key areas, each highlighting significant advancements in the study and application of plant science.
High-throughput phenotyping is a critical component of precision agriculture. By incorporating advanced deep learning models, researchers have significantly enhanced the efficiency and accuracy of crop phenotyping. For instance,
Agricultural automation is transforming traditional farming practices.
Plant disease detection remains a critical area of agricultural research.
Using machine learning to predict plant growth and pruning behavior provides new tools for agricultural decision-making.
Improving food safety and quality is a primary goal of modern agricultural research.
This Research Topic highlights several important trends while reflecting on persistent challenges in the field. Firstly, lightweight AI models for on-site deployment are becoming increasingly mainstream. These models maintain high accuracy despite limited computational resources, as demonstrated by
To advance plant science and achieve sustainable agriculture, future research should focus on the following directions: (1) Developing open-access datasets and affordable hardware to lower the barriers to AI adoption; (2) Optimizing lightweight models to enhance their robustness and scalability for smallholder farms and diverse agricultural environments; (3) Integrating satellite imagery, drones, and ground-based sensors to create a multi-layered crop monitoring system; (4) Exploring the long-term impacts of robotics and AI on agricultural ecosystems, particularly in terms of environmental sustainability and economic equity. These directions will provide new momentum for achieving precision agriculture.
This Research Topic demonstrates how artificial intelligence, machine learning, and robotics can address critical challenges in modern agriculture by enhancing efficiency and sustainability. The studies not only reveal diverse applications of these technologies in plant science but also lay a foundation for future agricultural innovations. As technology continues to evolve, these breakthroughs will offer new solutions for global food security and ecological conservation.
ZY: Writing – original draft, Writing – review & editing. LI: Conceptualization, Writing – review & editing. JT: Investigation, Writing – review & editing. HZ: Supervision, Writing – review & editing. CY: Validation, Writing – review & editing. HL: Supervision, Writing – review & editing.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
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