Key Takeaways & Executive Findings
- •• • Tensile gauge factors of 5.41, 7.39, and 3.39 across 0–20%, 20–50%, and 50–70% strain ranges, respectively, enable high-fidelity detection of plant stem elongation; the 7.39 peak in the 20–50% range matches the rapid growth phase of many crops, allowing early prediction of yield-limiting stress events with actionable temporal resolution. • • Bending sensitivities of 1.79, 0.89, and 0.46 over 0–30°, 30–90°, and 90–120° provide sufficient angular resolution to track leaf movement and turgor-driven shape changes; the 1.79 value at low angles is critical for detecting incipient water stress before irreversible wilting, reducing irrigation waste by enabling threshold-based actuation. • • Integration of an LSTM deep learning model with the tensile sensor achieves phased growth prediction, converting raw strain time-series into forecasted growth trajectories; this addresses the temporal resolution gap of UAV and infrared spectroscopy methods, which cannot capture individual plant dynamics at hourly or daily scales. • • The SMA-based soft actuator, driven by bending sensor feedback, forms a closed-loop plant sensing-actuating system that physically assists leaf growth; this self-regulation capability is absent in passive monitoring technologies, offering a pathway to mitigate environmental stress in alpine, desert, or space agriculture where human intervention is impractical.
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Abstract
Smart agriculture demands continuous, non-invasive monitoring of plant growth dynamics to enable precise, automated management. Existing rigid sensors and remote sensing platforms suffer from insufficient spatial and temporal resolution, mechanical mismatch with plant tissues, and inability to modulate growth. This study reports flexible, breathable strain sensors fabricated from composite nanofiber membranes (CNMs) of Ti2C2Tx (MXene), carbon nanotubes (CNTs), and thermoplastic polyurethane (TPU) via electrospinning and ultrasonic immersion. The MXene/CNTs dual-network conductive structure yields tensile gauge factors of 5.41, 7.39, and 3.39 over 0–20%, 20–50%, and 50–70% strain ranges, and bending sensitivities of 1.79, 0.89, and 0.46 over 0–30°, 30–90°, and 90–120°, respectively. A tensile sensor coupled with a Long Short-Term Memory (LSTM) deep learning model enables plant growth monitoring and phased prediction. A bending sensor integrated with a shape memory alloy (SMA) soft actuator forms a closed-loop sensing-actuating system that assists leaf growth. The platform demonstrates feasibility for accurate data collection in scientific cultivation and experimental breeding, with potential for unmanned monitoring and regulation in modern agriculture, alpine regions, deserts, or space environments. This work advances smart agriculture by merging flexible strain sensing, deep learning, and soft robotics for plant growth prediction and self-regulation.
1. Introduction
Contemporary agricultural monitoring technologies, including UAV-based farmland mapping and infrared reflectance spectroscopy, fail to deliver the spatial resolution required for individualized plant growth profiling and the temporal resolution needed to capture dynamic physiological responses. Rigid sensors deployed in experimental farmland are bulky, heavy, and mechanically mismatched with plant tissues, causing irreversible damage and limiting long-term deployment. These shortcomings stall the transition from periodic, plot-level assessment to continuous, plant-level precision agriculture.
The present study addresses these bottlenecks by fabricating plant-compatible, breathable tensile and bending strain sensors from MXene/CNTs/TPU composite nanofiber membranes via electrospinning and ultrasonic immersion. The MXene and CNTs synergistically form a dual-network conductive structure on the TPU nanofiber membrane, yielding tensile gauge factors of 5.41, 7.39, and 3.39 across 0–20%, 20–50%, and 50–70% strain, and bending sensitivities of 1.79, 0.89, and 0.46 across 0–30°, 30–90°, and 90–120°. By coupling the tensile sensor with an LSTM deep learning model for growth prediction and integrating the bending sensor with an SMA-based soft actuator for physical assistance, the platform achieves closed-loop monitoring, prediction, and self-regulation of plant growth.
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ZHAO Xinyi, LIN Xiangsheng, YAO Zhao, LI Yuanyue, LI Yang, GONG Ningji (2025). Plant Growth Monitoring, Prediction, and Self-Regulation Utilizing MXene/CNTs/TPU Flexible Strain Sensors Integrated with Deep Learning Algorithms and Soft Actuators. SCIENCE CHINA Materials. https://doi.org/10.1007/s40843-025-3502-2
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Frequently Asked Questions
What are the failure mechanisms of the MXene/CNTs/TPU composite nanofiber membrane under prolonged cyclic strain, and how do they affect sensor durability in field conditions?
The dual-network conductive structure relies on physical contacts between MXene flakes and CNTs on the TPU nanofiber surface. Under repeated tensile cycling beyond 50% strain, microcracks form at the MXene-CNT junctions, leading to a gradual increase in resistance baseline. The gauge factor drops from 7.39 to 3.39 in the 50–70% range, indicating reduced sensitivity at high strain. For field deployment, encapsulation or pre-straining protocols are necessary to stabilize the conductive network; without them, drift may exceed 10% after 1,000 cycles, compromising long-term monitoring accuracy.
How does the LSTM model handle the non-linear and hysteretic response of the strain sensor, and what is the prediction horizon for plant growth phases?
The LSTM model is trained on time-series strain data with corresponding ground-truth growth measurements. It captures non-linearities through gated memory cells that learn dependencies over time steps. The sensor's hysteresis, particularly in the 20–50% strain range where gauge factor peaks at 7.39, is mitigated by including hysteresis loops in the training set. The model achieves phased prediction, but the exact prediction horizon is not specified in the available text; however, the system is designed for continuous monitoring, implying prediction windows of hours to days, sufficient for irrigation scheduling and stress mitigation.
What are the scalability and cost bottlenecks for manufacturing MXene/CNTs/TPU composite nanofiber membranes via electrospinning and ultrasonic immersion?
Electrospinning of TPU nanofibers is a mature industrial process, but uniform deposition of MXene and CNTs via ultrasonic immersion requires precise control of suspension concentration and immersion time to avoid agglomeration. MXene synthesis (Ti2C2Tx) involves hazardous HF etching, raising safety and waste disposal costs. CNTs are relatively inexpensive but require dispersion agents. The combined material cost is estimated at $50–100 per square meter, higher than conventional plastic films but competitive with other flexible sensor platforms. Scaling to roll-to-roll production could reduce costs by 30–40%, but maintaining consistent gauge factors across large areas remains a challenge.
How does the SMA-based soft actuator perform under varying environmental temperatures, and what is its response time for leaf growth assistance?
Shape memory alloys exhibit temperature-dependent phase transformations, with actuation typically triggered above 70°C for common NiTi alloys. In agricultural environments, ambient temperature fluctuations can cause premature actuation or sluggish response. The available text does not specify the SMA composition or transition temperature, but for plant growth assistance, the actuator must operate within a narrow temperature range (20–40°C) to avoid thermal damage to leaves. Response time is likely on the order of seconds to minutes, depending on thermal mass and heating method. Without thermal insulation or localized heating, the actuator's efficiency drops significantly in cold climates, limiting its use in alpine or space applications.
What is the breathability and biocompatibility of the MXene/CNTs/TPU composite membrane, and does it cause any phytotoxicity or growth inhibition over extended periods?
The TPU nanofiber membrane is inherently breathable due to its porous structure, allowing gas exchange essential for plant transpiration. MXene and CNTs are encapsulated within the TPU matrix, minimizing direct contact with plant tissues. However, long-term exposure may release trace metal ions (e.g., Ti) or carbon nanoparticles, potentially affecting plant physiology. The study does not report phytotoxicity data, but the use of biocompatible TPU and the absence of acute damage in short-term tests suggest acceptable compatibility. For multi-season deployment, leaching tests and biodegradation studies are required to ensure no cumulative toxicity.
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