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.

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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

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article

Polymer Substrates for Flexible Sensors: Advances and Contributions to Smart Agriculture

Peiwen Liu, Dingding Yao, Wenxiu Ma, Adnan Raza Altaf et al.
Advanced Functional Materials
Smart Agriculture and AI
article

Polymer Substrates for Flexible Sensors: Advances and Contributions to Smart Agriculture

Peiwen Liu, Dingding Yao, Wenxiu Ma, Adnan Raza Altaf, Kai Zhang, Yixin Wang, Yihui Li, Qun Song, Afshan Sohail, Yaohui Chen, Zhangzheng Xiao, Shanjun Li, Zheng Yan
article en

Abstract

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.

Advanced Functional Materials
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)
Fundamental Research Funds for the Central Universities
Zero hunger
Openalex Percentile: Top 11%
Smart Agriculture and AI
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