PlantRing: A high-throughput wearable sensor system for decoding plant growth, water relations, and innovating irrigation
The integration of flexible electronics with plant science has generated various plant-wearable sensors, yet challenges persist in their application to real-world agriculture, particularly in high-throughput settings. Overcoming the trade-off between sensing sensitivity and range, adapting sensors to a wide range of crop types, and bridging the gap between sensor measurements and biological understandings remain primary obstacles. Here, we introduce PlantRing, an innovative, nano-flexible sensing system designed to address these challenges. PlantRing employs bio-sourced carbonized silk georgette as the strain-sensing material, offering an exceptional detection limit (0.03%-0.17% strain, depending on sensor model), high stretchability (tensile strain up to 100%), and remarkable durability (season-long use). PlantRing effectively monitors plant growth and water status by measuring organ circumference dynamics, performing reliably under harsh conditions, and adapting to a wide range of plant species. Applying PlantRing to study fruit cracking in tomato and watermelon has revealed a novel hydraulic mechanism characterized by genotype-specific excess sap flow within the plant to fruiting branches. Its high-throughput application has enabled large-scale quantification of stomatal sensitivity to soil drought-a long-standing aspiration in plant biology-facilitating the selection of drought-tolerant germplasm. Combining PlantRing with a soybean mutant has led to the discovery of a potential novel function of the circadian clock gene GmLNK2 in stomatal regulation. More practically, integrating PlantRing into feedback irrigation achieves simultaneous water conservation and quality improvement, signifying a paradigm shift from reliance on experience or environmental cues to plant-based feedback control. Collectively, PlantRing represents a groundbreaking tool poised to revolutionize botanical studies, agriculture, and forestry.
Authors
- Yunxiu Liu
- Mei Zou
- Ting Sun (ORCID: https://orcid.org/0000-0002-9721-1861)
- Huasen Wang (ORCID: https://orcid.org/0000-0002-7327-282X)
- Pei Xu (ORCID: https://orcid.org/0000-0002-3719-7316)
- Bi Peng (ORCID: https://orcid.org/0000-0001-7232-0300)
- Qiguang Xie (ORCID: https://orcid.org/0000-0001-7477-1965)
- Yingying Zhang (ORCID: https://orcid.org/0000-0002-3718-9826)
- Zheng Shi (ORCID: https://orcid.org/0000-0002-9696-8299)
- Xiaodong Xu (ORCID: https://orcid.org/0000-0001-9041-3826)
- Wenbo Xu (ORCID: https://orcid.org/0009-0001-0408-4032)
- Rujia Jiang
- Chueh‐Hsuan Lu
- Rui Cheng
Institutions
- Qingdao Agricultural University (CN)
- Henan University (CN)
- Huaiyin Institute of Technology (CN)
- China Jiliang University (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Plant Communications
- Published
- 2025-03-26
- DOI
- https://doi.org/10.1016/j.xplc.2025.101322
- Citations
- 19
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 33.25
Funders
- Department of Agriculture and Rural Affairs of Zhejiang Province