Search Results (322)

The integration of digital twin technology with robotic automation holds significant promise for advancing sustainable horticulture in controlled environment agriculture. This article presents DigiHortiRobot, a novel AI-driven digital twin architecture tailored for hydroponic greenhouse systems. The proposed framework integrates real-time sensing, predictive modeling, task planning, and dual-arm robotic execution within a modular, IoT-enabled infrastructure. DigiHortiRobot is structured into three progressive implementation phases: (i) monitoring and data acquisition through a multimodal perception system; (ii) decision support and virtual simulation for scenario analysis and intervention planning; and (iii) autonomous execution with feedback-based model refinement. The Physical Layer encompasses crops, infrastructure, and a mobile dual-arm robot; the virtual layer incorporates semantic modeling and simulation environments; and the synchronization layer enables continuous bi-directional communication via a nine-tier IoT architecture inspired by FIWARE standards. A robot task assignment algorithm is introduced to support operational autonomy while maintaining human oversight. The system is designed to optimize horticultural workflows such as seeding and harvesting while allowing farmers to interact remotely through cloud-based interfaces. Compared to previous digital agriculture approaches, DigiHortiRobot enables closed-loop coordination among perception, simulation, and action, supporting real-time task adaptation in dynamic environments. Experimental validation in a hydroponic greenhouse confirmed robust performance in both seeding and harvesting operations, achieving over 90% accuracy in localizing target elements and successfully executing planned tasks. The platform thus provides a strong foundation for future research in predictive control, semantic environment modeling, and scalable deployment of autonomous systems for high-value crop production. Full article

More recently, artificial intelligence (AI)-generated content (AIGC) is fundamentally transforming multiple sectors, including materials discovery, healthcare, education, scientific research, and industrial manufacturing. As for the complexities and challenges of intelligent robot design, AIGC has the potential to offer a new paradigm, assisting in conceptual and technical design, functional module design, and the training of the perception ability to accelerate prototyping. Taking the design of an apple-harvesting robot, for example, we demonstrate a basic framework of the AIGC-assisted robot design methodology, leveraging the generation capabilities of available multimodal large language models, as well as the human intervention to alleviate AI hallucination and hidden risks. Second, we study the enhancement effect on the robot perception system using the generated apple images based on the large vision-language models to expand the actual apple images dataset. Further, an apple-harvesting robot prototype based on an AIGC-aided design is demonstrated and a pick-up experiment in a simulated scene indicates that it achieves a harvesting success rate of 92.2% and good terrain traversability with a maximum climbing angle of 32°. According to the tentative research, although not an autonomous design agent, the AIGC-driven design workflow can alleviate the significant complexities and challenges of intelligent robot design, especially for beginners or young engineers. Full article

This review comprehensively examines the application of deep learning technologies across the entire soybean production chain, encompassing areas such as disease and pest identification, weed detection, crop phenotype recognition, yield prediction, and intelligent operations. By systematically analyzing mainstream deep learning models, optimization strategies (e.g., model lightweighting, transfer learning), and sensor data fusion techniques, the review identifies their roles and performances in complex agricultural environments. It also highlights key challenges including data quality limitations, difficulties in real-world deployment, and the lack of standardized evaluation benchmarks. In response, promising directions such as reinforcement learning, self-supervised learning, interpretable AI, and multi-source data fusion are proposed. Specifically for soybean automation, future advancements are expected in areas such as high-precision disease and weed localization, real-time decision-making for variable-rate spraying and harvesting, and the integration of deep learning with robotics and edge computing to enable autonomous field operations. This review provides valuable insights and future prospects for promoting intelligent, efficient, and sustainable development in soybean production through deep learning. Full article

Apple harvesting faces increasing challenges owing to rising labor costs and the limited seasonal workforce availability, highlighting the need for robotic harvesting solutions in precision agriculture. This study presents a 6-DOF robotic arm system designed for harvesting in dwarf hedge-planted orchards, featuring a lightweight perception module, a task-adaptive motion planner, and an adaptive soft gripper. A lightweight approach was introduced by integrating the Faster module within the C2f module of the You Only Look Once (YOLO) v8n architecture to optimize the real-time apple detection efficiency. For motion planning, a Dynamic Temperature Simplified Transition Adaptive Cost Bidirectional Transition-Based Rapidly Exploring Random Tree (DSA-BiTRRT) algorithm was developed, demonstrating significant improvements in the path planning performance. The adaptive soft gripper was evaluated for its detachment and load-bearing capacities. Field experiments revealed that the direct-pull method at 150 mN·m torque outperformed the rotation-pull method at both 100 mN·m and 150 mN·m. A custom control system integrating all components was validated in partially controlled orchards, where obstacle clearance and thinning were conducted to ensure operation safety. Tests conducted on 80 apples showed a 52.5% detachment success rate and a 47.5% overall harvesting success rate, with average detachment and full-cycle times of 7.7 s and 15.3 s per apple, respectively. These results highlight the system’s potential for advancing robotic fruit harvesting and contribute to the ongoing development of autonomous agricultural technologies. Full article

Sophisticated energy-harvesting technologies have swiftly progressed, expanding energy supply distribution and leveraging advancements in self-sustaining electronic devices. Despite substantial advancements in friction nanomotors within the last decade, a considerable technical obstacle remains for their flawless incorporation using printed electronics and autonomous devices. Integrating advanced triboelectric nanogenerator (TENG) technology with the rapidly evolving field of composite material 3D printing with has resulted in the advancement of three-dimensionally printed TENGs. Triboelectric nanogenerators are an important part of the next generation of portable energy harvesting and sensing devices that may be used for energy harvesting and artificial intelligence tasks. This paper systematically analyzes the continual development of 3D-printed TENGs and the integration of composite materials. The authors thoroughly review the latest material combinations of composite materials and 3D printing techniques for TENGs. Furthermore, this paper showcases the latest applications, such as using a TENG device to generate energy for electrical devices and harvesting energy from human motions, tactile sensors, and self-sustaining sensing gloves. This paper discusses the obstacles in constructing composite-material-based 3D-printed TENGs and the concerns linked to research and methods for improving electrical output performance. The paper finishes with an assessment of the issues associated with the evolution of 3D-printed TENGs, along with innovations and potential future directions in the dynamic realm of composite-material-based 3D-printed TENGs. Full article

This study analyzes the recent scientific literature on advanced biocompatible materials for triboelectric nanogenerators (TENGs) in biomedical applications. Focusing on materials like synthetic polymers, carbon-based derivatives, and advanced hybrids, the study interprets findings regarding their triboelectric properties and performance influenced by surface texture and additive manufacturing techniques. Major findings reveal that precise control over surface morphology, enabled by additive manufacturing (AM) is promising for optimizing transferred charge density and maximizing TENG efficiency. The analysis highlights the relevance of these material systems and fabrication strategies for developing self-powered wearable and implantable biomedical devices through enabling biocompatible energy-harvesting components that can operate autonomously without external power, underscoring the need for stringent biocompatibility and performance stability. This work synthesizes current progress, identifying critical material and process design parameters for advancing the field of biocompatible TENGs. Full article

Greenhouse horticulture plays an important role globally by producing nutritious fruits and vegetables with high resource use efficiency. Modern greenhouses are large-scale high-tech production factories that are increasingly data-driven, and where climate and irrigation control are gradually becoming more autonomous. This is enabled by technological developments and driven by shortages in skilled labor and the demand for improved resource use efficiency. In the Autonomous Greenhouse Challenge, it has been shown that controlling greenhouse cultivation can be done efficiently with intelligent algorithms. For an optimal strategy, however, it is essential that control algorithms properly account for crop responses, which requires appropriate sensors, reliable data, and accurate models. This paper presents the results of the 4th Autonomous Greenhouse Challenge, in which international teams developed six intelligent algorithms that fully controlled a dwarf tomato cultivation, a crop that is well-suited for robotic harvesting, but for which little prior cultivation data exists. Nevertheless, the analysis of the experiment showed that all teams managed to obtain a profitable strategy, and the best algorithm resulted a production equivalent to 45 kg/m²/year, higher than in the commercial practice of high-wire cherry tomato growing. The predominant factor was found to be the much higher plant density that can be achieved in the applied growing system. More difficult challenges were found to be related to measuring crop status to determine the harvest moment. Finally, this experiment shows the potential for novel greenhouse cultivation systems that are inherently well-suited for autonomous control, and results in a unique and rich dataset to support future research. Full article

Triboelectric nanogenerators (TENGs) hold significant potential for decentralized energy harvesting; however, their dependence on rotational mechanical energy often limits their ability to harness ubiquitous horizontal motion in real-world applications. Here, a single horizontal linear-to-rotational triboelectric nanogenerator (SHLR-TENG) is presented, designed to efficiently convert linear motion into rotational energy using a robust gear system, enabling a high voltage and reliable full cycle of alternating current (AC). The device features a radially patterned disk with triboelectric layers composed of polyimide. The SHLR-TENG achieves a peak-to-peak voltage of 1420 V, a short-circuit current of 117 µA, and an average power output of 41.5 mW, with a surface charge density of 110 µC/m². Moreover, it demonstrates a power density per unit volume of 371.2 W·m⁻³·Hz⁻¹. The device retains 80% efficiency after 1.5 million cycles, demonstrating substantial durability under mechanical stress. These properties enable the SHLR-TENG to directly power commercial LEDs and low-power circuits without the need for energy storage. This study presents an innovative approach to sustainable energy generation by integrating horizontal motion harvesting with rotational energy conversion. The compact and scalable design of the SHLR-TENG, coupled with its resilience to humidity (20–90% RH) and temperature fluctuations (10–70 °C), positions it as a promising next-generation energy source for Internet of Things (IoT) devices and autonomous systems. Full article

The continuous and reliable operation of autonomous systems is important for farm management decision making, whether such systems perform crop monitoring using imaging systems or crop handling in pruning and harvesting applications using robotic manipulators. Autonomous systems, including robotic ground vehicles, drones, and tractors, are major research efforts of precision crop management. However, these systems may be less effective or require specific customizations for planting systems in low tunnels, high tunnels, or other environmentally controlled enclosures. In this work, a compact and lightweight crop management platform is developed that uses overhead power cables for continuous operation over row crops, requiring less human intervention and independent of the ground terrain conditions. The platform does not carry batteries onboard for its operation, but rather pulls power from overhead cables, which it also uses to navigate over crop rows. It is developed to be modular, with the top section consisting of mobility and power delivery and the bottom section addressing a custom task, such as incorporating additional sensors for crop monitoring or manipulators for crop handling. This prototype illustrates the infrastructure, locomotive mechanism, and sample usage of the system (crop imaging) in the application of low-tunnel-covered strawberries; however, there is potential for other row crop systems with regularly spaced support structures to adopt this platform as well. Full article

Stretchable display technology has rapidly evolved, enabling a new generation of flexible electronics with applications ranging from wearable healthcare and smart textiles to implantable biomedical devices and soft robotics. This review systematically presents recent advances in stretchable displays, focusing on intrinsic stretchable materials, wavy surface engineering, and hybrid integration strategies. The paper highlights critical breakthroughs in device architectures, energy-autonomous systems, durable encapsulation techniques, and the integration of artificial intelligence, which collectively address challenges in mechanical reliability, optical performance, and operational sustainability. Particular emphasis is placed on the development of high-resolution displays that maintain brightness and color fidelity under mechanical strain, and energy harvesting systems that facilitate self-powered operation. Durable encapsulation methods ensuring long-term stability against environmental factors such as moisture and oxygen are also examined. The fusion of stretchable electronics with AI offers transformative opportunities for intelligent sensing and adaptive human–machine interfaces. Despite significant progress, issues related to large-scale manufacturing, device miniaturization, and the trade-offs between stretchability and device performance remain. This review concludes by discussing future research directions aimed at overcoming these challenges and advancing multifunctional, robust, and scalable stretchable display systems poised to revolutionize flexible electronics applications. Full article

This study examines how the frequency of an innovative energy harvester is tuned and how it behaves. This harvester transforms thermal energy into mechanical oscillations of two polyvinylidene fluoride (PVDF) piezoelectric beams, which produce electrical energy via a shape memory alloy (SMA) thread. The oscillation frequency is modified by two magnetic weights that are positioned symmetrically on the SMA thread and interact with stationary NdFeB permanent magnets. The SMA thread shifts laterally due to longitudinal thermal contraction and expansion induced by a constant-temperature heater. Temperature gradients above the heater trigger cyclical variations in the length of the SMA thread, leading to autonomous vibrations of the masses in both the vertical and horizontal planes. An experimental apparatus was constructed to analyze the harvester by tracking the motions of the masses and the voltages produced by the piezoelectric beams. Information was gathered regarding the correlation between output voltage and power with the consumer’s load resistance. These outcomes were confirmed using a multiphysics dynamic simulation that incorporated the interconnections among mechanical, thermal, magnetic, and electrical systems. The findings indicate that the use of permanent magnets increases the bending vibration frequency from 8.3 Hz to 9.2 Hz. For a heater maintained at 70 °C, this boosts the output power from 1.9 µW to 8.18 µW. A notable property of the considered energy harvester configuration is its ability to operate at cryogenic temperatures. 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

With the rapid rise in Internet of Things (IoT) and artificial intelligence (AI) technologies, there is an increasing need for portable, wearable, and self-powered flexible sensing devices. In such scenarios, self-powered nanogenerators have emerged as promising energy harvesters capable of converting ambient mechanical stimuli into electrical energy, enabling the development of autonomous flexible sensors and sustainable systems. This review highlights recent advances in nanogenerator technologies—particularly those based on piezoelectric and triboelectric effects—with a focus on soft, flexible, and gel-based polymer materials. Key mechanisms of energy conversion are discussed alongside strategies to enhance performance through material innovation, structural design, and device integration. Special attention is given to the role of gel-type composites, which offer unique advantages such as mechanical tunability, self-healing ability, and biocompatibility, making them highly suitable for next-generation wearable, biomedical, and environmental sensing applications. We also explore the evolving landscape of energy applications, from microscale sensors to large-area systems, and identify critical challenges and opportunities for future research. By synthesizing progress across materials, mechanisms, and application domains, this review aims to guide the rational design of high-performance, sustainable nanogenerators for the next era of energy technologies. Full article

In the context of autonomous environmental monitoring, this study investigates a surface acoustic wave (SAW) sensor designed for selective carbon dioxide (CO₂) detection. The sensor is based on a LiTaO₃ piezoelectric substrate with copper interdigital transducers and a polyetherimide (PEI) layer, chosen for its high electromechanical coupling and strong CO₂ affinity. Finite element simulations were conducted to analyze the resonance frequency response under varying gas concentrations, film thicknesses, pressures, and temperatures. Results demonstrate a linear and sensitive frequency shift, with detection capability starting from 10 ppm. The sensor’s autonomy is ensured by a piezoelectric energy harvester composed of a cantilever beam structure with an attached seismic mass, where mechanical vibrations induce stress in a piezoelectric layer (PZT-5H or PVDF), generating electrical energy via the direct piezoelectric effect. Analytical and numerical analyses were performed to evaluate the influence of excitation frequency, material properties, and optimal load on power output. This integrated configuration offers a compact and energy-independent solution for real-time CO₂ monitoring in low-power or inaccessible environments. Full article

Accurate fruit target identification is crucial for autonomous harvesting robots in complex orchards, where image segmentation using deep learning networks plays a key role. To address the trade-off between segmentation accuracy and inference efficiency, this study proposes LGVM-YOLOv8n, a lightweight instance segmentation model based on YOLOv8n-seg. LGVM is an acronym for lightweight, GSConv, VoVGSCSP, and $MPDIoU$, highlighting the key improvements incorporated into the model. The proposed model integrates three key improvements: (1) the GSConv module, which enhances feature interaction and reduces computational cost; (2) the VoVGSCSP module, which optimizes multi-scale feature representation for small objects; and (3) the $MPDIoU$ loss function, which improves target localization accuracy, particularly for occluded fruits. Experimental results show that LGVM-YOLOv8n reduces computational cost by 9.17%, decreases model weight by 7.89%, and improves inference speed by 16.9% compared to the original YOLOv8n-seg. Additionally, segmentation accuracy under challenging conditions (front-light, back-light, and occlusion) improves by 3.28% to 4.31%. Deployment tests on an edge computing platform demonstrate real-time performance, with inference speed accelerated to 0.084 s per image and frame rate increased to 28.73 FPS. These results validated the model’s robustness and adaptability, providing a practical solution for apple-picking robots in complex orchard environments. Full article