Polymer Substrates for Flexible Sensors: Advances and Contributions to Smart Agriculture
ABSTRACT The advancement of smart agriculture is becoming increasingly dependent on sensor technologies that enable the digitization and optimization of agricultural processes. Traditional sensors, however, are constrained by their inherent rigidity, poor conformability to irregular surfaces, and limited adaptability to harsh agricultural environments. Flexible sensors, particularly those made from polymer‐based materials, offer substantial advantages due to their lightweight, flexible, and highly sensitive properties. These sensors are key to the performance, stability, and environmental resilience required for sustainable smart agriculture applications. This review provides a comprehensive overview of polymer‐based flexible sensors, focusing on the types of polymer substrates, interface engineering methods, and their applications in agriculture. We discuss how these sensors address agricultural challenges, including their integration with machine learning (ML) to improve data analysis and decision‐making. The review also highlights the ongoing challenges and future opportunities for enhancing the performance of these sensors, including material stability, interface optimization, and large‐scale implementation. Ultimately, this work aims to provide valuable insights and technical guidance for researchers and engineers developing high‐performance, sustainable flexible sensors to advance the frontiers of smart agriculture.
Authors
- Peiwen Liu (ORCID: https://orcid.org/0000-0003-2398-8798)
- Dingding Yao (ORCID: https://orcid.org/0000-0002-5489-8181)
- Wenxiu Ma (ORCID: https://orcid.org/0000-0002-8355-1793)
- Adnan Raza Altaf (ORCID: https://orcid.org/0000-0002-4723-2672)
- Kai Zhang (ORCID: https://orcid.org/0000-0002-5783-946X)
- Yixin Wang (ORCID: https://orcid.org/0009-0004-0875-2538)
- Yihui Li (ORCID: https://orcid.org/0000-0002-9596-0412)
- Qun Song (ORCID: https://orcid.org/0000-0002-7409-8237)
- Afshan Sohail
- Yaohui Chen
- Zhangzheng Xiao
- Shanjun Li
- Zheng Yan
Institutions
- Huazhong Agricultural University (CN)
- LMU Klinikum (DE)
- Ministry of Agriculture (CA)
- Ministry of Agriculture and Rural Affairs (CN)
- Huazhong University of Science and Technology (CN)
- University of Göttingen (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2026-08-03
- DOI
- https://doi.org/10.1002/adfm.77444
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Fundamental Research Funds for the Central Universities