Search Results (42)

Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article

Current tomato harvesters rely primarily on external color as the sole indicator of ripeness. However, this approach often results in premature harvesting, leading to insufficient lycopene accumulation and a suboptimal nutritional content for human consumption. Such limitations are especially critical in controlled-environment agriculture (CEA) systems, where maximizing fruit quality and nutrient density is essential for both the yield and consumer health. To address that challenge, this study introduces a novel, multimodal harvest readiness framework tailored to nutrient film technology (NFT)-based smart farms. The proposed approach integrates plant-level stress diagnostics and fruit-level phenotyping using wearable biosensors, AI-assisted computer vision, and non-invasive physiological sensing. Key physiological markers—including the volatile organic compound (VOC) methanol, phytohormones salicylic acid (SA) and indole-3-acetic acid (IAA), and nutrients nitrate and ammonium concentrations—are combined with phenomic traits such as fruit color (a*), size, chlorophyll index (rGb), and water status. The innovation lies in a four-stage decision-making pipeline that filters physiologically stressed plants before selecting ripened fruits based on internal and external quality indicators. Experimental validation across four plant conditions (control, water-stressed, light-stressed, and wounded) demonstrated the efficacy of VOC and hormone sensors in identifying optimal harvest candidates. Additionally, the integration of low-cost electrochemical ion sensors provides scalable nutrient monitoring within NFT systems. This research delivers a robust, sensor-driven framework for autonomous, data-informed harvesting decisions in smart indoor agriculture. By fusing real-time physiological feedback with AI-enhanced phenotyping, the system advances precision harvest timing, improves fruit nutritional quality, and sets the foundation for resilient, feedback-controlled farming platforms suited to meeting global food security and sustainability demands. Full article

Tomatoes (Solanum lycopersicum) and potatoes (Solanum tuberosum) are vital staple crops. They are prone to diseases from pathogens like Ralstonia and Fusarium, which cause significant agricultural losses. Detecting volatile organic compounds (VOCs) emitted by plants under stress offers a promising approach for advanced monitoring of crop health. This study examines sensing materials for wearable plant sensors targeting VOCs as biomarkers under abiotic and biotic stress. Key questions addressed include the specific VOC emission profiles of potato and tomato cultivars, how materials and sensing mechanisms influence sensor performance, and material considerations for agricultural use. The analysis reveals cultivar-specific VOC profiles under stress, challenging the identification of universal biomarkers for specific diseases. Through a literature review, this study reviews VOC responses to fungi, bacteria, and viruses, and compares non-composite and hybrid chemiresistive and electrochemical sensors based on sensitivity, selectivity, detection limits, response time, robustness, cost-effectiveness, and biocompatibility. A superstructure bridging materials science, plant pathology, AI, data science, and manufacturing is proposed, emphasizing three strategies: sensitivity, flexibility, and sustainability. This study identifies recent research trends that involve developing biodegradable wearable sensors for precision agriculture, leveraging flexible biocompatible materials, multi-parameter monitoring, self-healing properties, 3D-printed designs, advanced nanomaterials, and energy-harvesting technologies. Full article

Work-related musculoskeletal disorders (WMSDs) are common in the agricultural industry due to repetitive tasks, like plant transplanting, which involve sustained bending, squatting, and awkward postures. This study uses wearable sensors to evaluate human biomechanics during simulated transplanting and assesses the potential of collaborative robot (cobot) assistance to reduce physical strain. Sixteen participants performed transplanting tasks under manual and cobot-assisted conditions. Kinematic and electromyographic (EMG) data were collected using Xsens motion capture and Trigno EMG systems. Cobot assistance significantly reduced the segment velocity and acceleration in key spinal regions (L5/S1, L1/T12, T1/C7), indicating lower dynamic spinal loading. It also altered muscle activation, decreasing biceps brachii use while increasing activation in stabilizing muscles such as the flexor carpi radialis, brachioradialis, and upper trapezius. Task duration decreased by 59.46%, suggesting improved efficiency. These findings highlight cobots’ potential to enhance ergonomic outcomes by encouraging controlled movements and reducing postural stress. However, the shift in muscle activation underscores the need for task-specific cobot tuning. This research supports the use of integrated IMU and EMG systems to inform cobot design and enable real-time biomechanical monitoring in labor-intensive settings. Full article

The rapidly evolving field of functional yarns has garnered substantial research attention due to their exceptional potential in enabling next-generation electronic textiles for wearable health monitoring, human–machine interfaces, and soft robotics. Despite notable advancements, the development of yarn-based strain sensors that simultaneously achieve high flexibility, stretchability, superior comfort, extended operational stability, and exceptional electrical performance remains a critical challenge, hindered by material limitations and structural design constraints. Here, we present a bioinspired, hierarchically structured core-sheath yarn sensor (CSSYS) engineered through an efficient dip-coating process, which synergistically integrates the two-dimensional conductive MXene nanosheets and one-dimensional silver nanowires (AgNWs). Furthermore, the sensor is encapsulated using a yarn-based protective layer, which not only preserves its inherent flexibility and wearability but also effectively mitigates oxidative degradation of the sensitive materials, thereby significantly enhancing long-term durability. Drawing inspiration from the natural architecture of plant stems—where the inner core provides structural integrity while a flexible outer sheath ensures adaptive protection—the CSSYS exhibits outstanding mechanical and electrical performance, including an ultralow strain detection limit (0.05%), an ultrahigh gauge factor (up to 744.45), rapid response kinetics (80 ms), a broad sensing range (0–230% strain), and exceptional cyclic stability (>20,000 cycles). These remarkable characteristics enable the CSSYS to precisely capture a broad spectrum of physiological signals, ranging from subtle arterial pulsations and respiratory rhythms to large-scale joint movements, demonstrating its immense potential for next-generation wearable health monitoring systems. Full article

Today, innovative engineering solutions, including IoT devices, enable the precise monitoring of plant health and the early detection of diseases. However, the lifespan of IoT devices used for the real-time monitoring of environmental or plant parameters in precision agriculture is typically only a few months, from planting to harvest. This short lifespan creates challenges in managing the e-waste generated by smart agriculture. One potential solution to reduce the volume and environmental impact of e-waste is to use more environmentally friendly and biodegradable materials to replace the non-degradable components (substrates) currently used in the structure of IoT devices. In this study, we estimate the electromagnetic properties at 2565 MHz of the leaves from three widely grown crops: winter wheat, corn, and sunflower. We found that winter wheat and sunflower leaves have values of the real part of relative permittivity ranging from about 33 to 69 (wheat) and 13 to 32 (sunflower), respectively, while corn exhibits a value of about 33.5. Our research indicates that the position of a leaf on the plant stem and its distance from the soil significantly affect the relative permittivity of winter wheat and sunflower. These relationships, however, are not evident in the electromagnetic properties of corn leaves. Full article

The increasing demand for smart agriculture has led to the development of agricultural sensor technology. Wearable sensors show great potential for monitoring the physiological and surrounding environmental information for plants due to their high flexibility, biocompatibility, and scalability. However, wearable sensors for plants face several challenges that hinder their large-scale practical application. In this review, we summarize the current research status of wearable plant sensors by analyzing the classification, working principles, sensor materials, and structural design and discussing the multifunctional applications. More importantly, we comment on the challenges the wearable plant sensors face and provide our perspectives on further improving the sensitivity, reliability, and stability of wearable plant sensors for future smart agriculture. Full article

To face the increasing requirement for grains as the global population continues to grow, improving both crop yield and quality has become essential. Plant health directly impacts crop quality and yield, making the development of plant health-monitoring technologies essential. Variable sensing technologies for outdoor/indoor farming based on different working principles have emerged as important tools for monitoring plants and their microclimates. These technologies can detect factors such as plant water content, volatile organic compounds (VOCs), and hormones released by plants, as well as environmental conditions like humidity, temperature, wind speed, and light intensity. To achieve comprehensive plant health monitoring for multidimensional assessment, multimodal sensors have been developed. Non-invasive monitoring approaches are also gaining attention, leveraging biocompatible and flexible sensors for plant monitoring without interference with its natural growth. Furthermore, wireless data transmission is crucial for real-time monitoring and efficient farm management. Reliable power supplies for these systems are vital to ensure continuous operation. By combining wearable sensors with intelligent data analysis and remote monitoring, modern agriculture can achieve refined management, resource optimization, and sustainable production, offering innovative solutions to global food security and environmental challenges. Full article

This paper investigates an in situ, non-destructive detection sensor based on flexible wearable technology that can reflect the intensity of plant transpiration. The sensor integrates four components: a flexible substrate, a humidity-sensing element, a temperature-sensing element, and a self-adhesive film. It is capable of accurately and continuously measuring the temperature, humidity, and vapor pressure deficit (VPD) on the leaf surface, thus providing information on plant transpiration. We combined the humidity-sensitive material graphene oxide (GO) with a PDMS-GO-SDS flexible substrate as the humidity-sensing element of the sensor. This element exhibits high sensitivity, fast response, and excellent biocompatibility with plant interfaces. The humidity monitoring sensitivity of the sensor reaches 4456 pF/% RH, while the temperature sensing element has a sensitivity of approximately 3.93 Ω/°C. Additionally, tracking tests were conducted on tomato plants in a natural environment, and the experimental results were consistent with related research findings. This sensor can be used to monitor plant growth during agricultural production and facilitate precise crop management, helping to advance smart agriculture in the Internet of Things (IoT) for plants. Full article

Plant wearable sensors have shown exceptional promise in continuously monitoring plant health. However, the potential adverse effects of these sensors on plant growth remain unclear. This study systematically quantifies wearable sensors’ interference with plant growth using two ornamental species, Peperomia tetraphylla and Epipremnum aureum. We evaluated the impacts of four common disturbances—mechanical pressure, hindrance of gas exchange, hindrance of light acquisition, and mechanical constraint—on leaf growth. Our results indicated that the combination of light hindrance and mechanical constraint demonstrated the most significant interference. When the sensor weight was no greater than 0.6 g and the coverage was no greater than 5% of the leaf area, these four disturbances resulted in slight impacts on leaf growth. Additionally, we fabricated a minimally interfering wearable sensor capable of measuring the air temperature of the microclimate of the plant while maintaining plant growth. This research provides valuable insights into optimizing plant wearable sensors, balancing functionality with minimal plant interference. Full article

In recent years, the rapid development of wireless technologies has led to the widespread adoption of the Internet of Things (IoT) in various fields. One of the fastest-growing segments of IoT is the “smart” wearables sector. In the next few years, the development of flexible plant-wearable devices that can provide vital information about the physiological characteristics of plants will be essential to support the faster growth of precision agriculture. We propose a small (overall size Ø35 mm × 0.8 mm), ultra-lightweight (0.4 g), and elegant-shaped antenna for unobtrusive integration on a plant surface for application in IoT-based precision agriculture at ISM 2.45 GHz band. The radiating element has a design that resembles a dragonfly, making the antenna visually unnoticeable. We used ZZ Plant leaves as the substrate for the antenna and transparent polymer foil for encapsulating the conductive parts, achieving a highly flexible, waterproof, and chemically resistant antenna for application in harsh environments. The obtained results indicate that the antenna is resilient to changes in substrate relative permittivity up to ±20%. It exhibits high radiation efficiency (between 26% and 40%) and omnidirectional patterns across the ISM 2.45 GHz band. Moreover, the measured results align reasonably well with the simulated ones. Full article

Polyurethane (PU) is among the most universal polymers and has been extensively applied in many fields, such as construction, machinery, furniture, clothing, textile, packaging and biomedicine. Traditionally, as the main starting materials for PU, polyols deeply depend on petroleum stock. From the perspective of recycling and environmental friendliness, advanced PU synthesis, using diversified resources as feedstocks, aims to develop versatile products with excellent properties to achieve the transformation from a fossil fuel-driven energy economy to renewable and sustainable ones. This review focuses on the recent development in the synthesis and modification of PU by extracting value-added monomers for polyols from waste polymers and natural bio-based polymers, such as the recycled waste polymers: polyethylene terephthalate (PET), PU and polycarbonate (PC); the biomaterials: vegetable oil, lignin, cashew nut shell liquid and plant straw; and biomacromolecules: polysaccharides and protein. To design these advanced polyurethane formulations, it is essential to understand the structure–property relationships of PU from recycling polyols. In a word, this bottom-up path provides a material recycling approach to PU design for printing and packaging, as well as biomedical, building and wearable electronics applications. Full article

This study utilized a plant-wearable sap flow sensor developed by a multidisciplinary team at Zhejiang University to monitor water distribution patterns in watermelon fruit stalks throughout their developmental stages. The dynamic rules of sap flow at different stages of fruit development were discovered: (1) In the first stage, sap flow into the fruit gradually halts after sunrise due to increased leaf transpiration, followed by a rapid increase post-noon until the next morning, correlating with fruit expansion. (2) In the second stage, the time of inflow sap from noon to night is significantly shortened, while the outflow sap from fruit is observed with the enhancement of leaf transpiration after sunrise, which is consistent with the slow fruit growth at this stage. (3) In the third stage, the sap flow maintains the diurnal pattern. However, the sap flow that inputs the fruit at night is basically equal to the sap flow that outputs the fruit during the day; the fruit phenotype does not change anymore. In addition, a strong correlation between the daily mass growth in fruit and the daily sap flow amount in fruit stalk was identified, validating the sensor’s utility for fruit growth monitoring and yield prediction. Full article

The safeguarding of plant health is vital for optimizing crop growth practices, especially in the face of the biggest challenges of our generation, namely the environmental crisis and the dramatic changes in the climate. Among the many innovative tools developed to address these issues, wearable sensors have recently been proposed for monitoring plant growth and microclimates in a sustainable manner. These systems are composed of flexible matrices with embedded sensing elements, showing promise in revolutionizing plant monitoring without being intrusive. Despite their potential benefits, concerns arise regarding the effects of the long-term coexistence of these devices with the plant surface. Surprisingly, a systematic analysis of their influence on plant physiology is lacking. This study aims to investigate the effect of the color and geometric features of flexible matrices on two key plant physiological functions: photosynthesis and transpiration. Our findings indicate that the negative effects associated with colored substrates, as identified in recent research, can be minimized by holing the matrix surface with a percentage of voids of 15.7%. This approach mitigates interference with light absorption and reduces water loss to a negligible extent, making our work one of the first pioneering efforts in understanding the intricate relationship between plant wearables’ features and plant health. Full article

Advances in biomimetic triboelectric nanogenerators (TENGs) have significant implications for electronic skin (e-skin) and human–machine interaction (HMI). Emphasizing the need to mimic complex functionalities of natural systems, particularly human skin, TENGs leverage triboelectricity and electrostatic induction to bridge the gap in traditional electronic devices’ responsiveness and adaptability. The exploration begins with an overview of TENGs’ operational principles and modes, transitioning into structural and material biomimicry inspired by plant and animal models, proteins, fibers, and hydrogels. Key applications in tactile sensing, motion sensing, and intelligent control within e-skins and HMI systems are highlighted, showcasing TENGs’ potential in revolutionizing wearable technologies and robotic systems. This review also addresses the challenges in performance enhancement, scalability, and system integration of TENGs. It points to future research directions, including optimizing energy conversion efficiency, discovering new materials, and employing micro-nanostructuring techniques for enhanced triboelectric charges and energy conversion. The scalability and cost-effectiveness of TENG production, pivotal for mainstream application, are discussed along with the need for versatile integration with various electronic systems. The review underlines the significance of making bioinspired TENGs more accessible and applicable in everyday technology, focusing on compatibility, user comfort, and durability. Conclusively, it underscores the role of bioinspired TENGs in advancing wearable technology and interactive systems, indicating a bright future for these innovations in practical applications. Full article