Search Results (107)

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

Accurate path tracking is crucial for greenhouse robots operating in complex environments. However, traditional curve tracking algorithms suffer from low tracking accuracy and large tracking errors. This study aim to develop a high precision greenhouse autonomous following robot, use ANSYS Workbench 19.2 to perform stress and deformation analysis on the robot, then propose a path tracking method based on Linear Quadratic Regulator (LQR) to optimize the pure tracking to ensure high precision curved path tracking for curved tracking, finally perform a comparative simulation analysis in MATLAB R2024a. The structural analysis shows that the maximum equivalent stress is 196 MPa and the maximum deformation is 1.73 mm under a load of 600 kg, which are within the yield limit of 45 steel. Simulation results demonstrate that at a speed of 2 m/s, the conventional Pure Pursuit algorithm incurs a maximum lateral error of 0.3418 m and a heading error of 0.2669 rad under high curvature conditions. By contrast, the LQR–Pure Pursuit algorithm reduces the peak lateral error to 0.0904 m and confines the heading error to approximately 0.0217 rad. Experimental validation yielded an RMSE of 0.018 m for lateral error and 0.016 m for heading error. These findings confirm that the designed robot can sustain its payload under most operating scenarios and that the proposed tracking strategy effectively suppresses deviations and improves path-following accuracy. 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

In the current context, where environmental concerns are gaining increased attention, the transition toward sustainable urban mobility stands out as a necessary and responsible step. Technological advancements over the past decade have brought private autonomous vehicles, particularly those defined by the Society of Automotive Engineers Levels 4 and 5, into focus as promising solutions for mitigating road congestion and reducing greenhouse gas emissions. However, the extent to which Autonomous Vehicles can fulfill this potential depends largely on user acceptance, patterns of use, and their integration within broader green energy and sustainability policies. The present paper aims to develop an integrated conceptual model that links behavioral determinants to environmental outcomes, assessing how individuals’ intention to adopt private autonomous vehicles can contribute to sustainable urban mobility. The model integrates five psychosocial determinants—perceived usefulness, trust in technology, social influence, environmental concern, and perceived behavioral control—with contextual variables such as energy source, infrastructure availability, and public policy. These components interact to predict users’ intention to adopt AVs and their perceived contribution to urban sustainability. Methodologically, the study builds on a narrative synthesis of the literature and proposes a framework applicable to empirical validation through structural equation modeling (SEM). The model draws on established frameworks such as Technology Acceptance Model (TAM), Theory of Planned Behavior, and Unified Theory of Acceptance and Use of Technology, incorporating constructs including perceived usefulness, trust in technology, social influence, environmental concern, and perceived behavioral control, constructs later to be examined in relation to key contextual variables, including the energy source powering Autonomous Vehicles—such as electricity from mixed or renewable grids, hydrogen, or hybrid systems—and the broader policy environment (regulatory frameworks, infrastructure investment, fiscal incentives, and alignment with climate and mobility strategies and others). The research provides relevant directions for public policy and behavioral interventions in support of the development of clean and smart urban transport in the age of automation. Full article

This perspective paper examines the research and development challenges faced by plant factories with artificial lighting (plant factories hereafter). The global and local challenges facing our planet can be divided into the following four categories: (1) food and agriculture; (2) environment and ecosystems; (3) depletion, uneven distribution, and the overuse of nonrenewable resources; and (4) society, economy, and quality of life. All of the aspects of this four-way deadlock problem must be resolved simultaneously, since solving only one of them could exacerbate one or more of the remaining three. In this paper, the role of plant factories in solving the four-way deadlock problem is discussed from the following perspectives: (1) civilization and culture, (2) participatory science, and (3) the integration of biotechnology and the latest nonbiological technology, such as artificial intelligence (AI). The relationship and interactions between the environment and plant ecosystems are easily observed in the plant factories’ cultivation room. Thus, it is easy to analyze their relationship and interactions. The findings from such observations can also be applied to increase the yield in plant factories, with minimum resource inputs. Moreover, if the electricity generated by renewable energy sources is used, it will become an energy-autonomous plant factory. This means that the plant factory can be operated with the minimum contribution of greenhouse gas emissions to global warming and land area use. Full article

Automation in agricultural machinery, underpinned by the integration of advanced technologies, is revolutionizing sustainable farming practices. Key enabling technologies include multi-source positioning fusion (e.g., RTK-GNSS/LiDAR), intelligent perception systems utilizing multispectral imaging and deep learning algorithms, adaptive control through modular robotic systems and bio-inspired algorithms, and AI-driven data analytics for resource optimization. These technological advancements manifest in significant applications: autonomous field machinery achieving lateral navigation errors below 6 cm, UAVs enabling targeted agrochemical application, reducing pesticide usage by 40%, and smart greenhouses regulating microclimates with ±0.1 °C precision. Collectively, these innovations enhance productivity, optimize resource utilization (water, fertilizers, energy), and mitigate critical labor shortages. However, persistent challenges include technological heterogeneity across diverse agricultural environments, high implementation costs, limitations in adaptability to dynamic field conditions, and adoption barriers, particularly in developing regions. Future progress necessitates prioritizing the development of lightweight edge computing solutions, multi-energy complementary systems (integrating solar, wind, hydropower), distributed collaborative control frameworks, and AI-optimized swarm operations. To democratize these technologies globally, this review synthesizes the evolution of technology and interdisciplinary synergies, concluding with prioritized strategies for advancing agricultural intelligence to align with the Sustainable Development Goals (SDGs) for zero hunger and responsible production. Full article

It has been widely recognized that replacing chemical fertilizers with organic fertilizers (organic substitution) could significantly increase the long-term productivity of the land and potentially enhance resilience to climate change. Nevertheless, there is limited information on the accurate monitoring of soil greenhouse gas (GHG) fluxes at different levels of organic substitution in rubber plantations. Before accurate estimation of soil GHG fluxes can be made, it is important to investigate diurnal variations and suitable sampling times. In this study, six treatment groups of rubber plantations in the Longjiang Farm of Baisha Li autonomous county, Hainan Island, including the control (CK), conventional fertilizer (NPK), and organic substitution treatments in which organic fertilizer replaced 25% (25%M), 50% (50%M), 75% (75%M), and 100% (100%M) of chemical nitrogen fertilizer were selected as study objectives. The soil GHG fluxes were observed by static chamber-gas chromatography for a whole day (24 h) during both wet and dry seasons. The results showed the following: (1) There was a significant single-peak daily variation of GHGs in rubber plantation soils. (2) The soil GHG fluxes observed from 9:00–12:00 are closer to the daily average fluxes. (3) Organic fertilizer substitution influenced soil CO₂ and N₂O fluxes and had no significant effect on soil CH₄ fluxes. Fluxes of soil CO₂ and N₂O increased firstly and then decreased gradually when the substitution ratios exceeded 50% or 75%. (4) Soil CO₂ and N₂O fluxes were positively correlated with soil temperature and soil moisture, and CH₄ fluxes were negatively correlated with soil temperature and soil moisture in both wet and dry seasons. The study indicated that understanding the daily pattern of GHG changes in rubber forest soils under different levels of organic fertilizer substitution and the optimal observation time could improve the accurate assessment of long-timescale observation studies. Full article

Despite some rudimentary handling vehicles employed in the labor-intensive harvesting and transportation of greenhouse vegetables, research on intelligent uncrewed transport vehicles remains limited. Herein, an uncrewed transport vehicle was designed for greenhouse solanaceous vegetable harvesting. Its overall structure and path planning were tailored to the greenhouse environment, with specially designed components, including the electric crawler chassis, unloading mechanism, and control system. A SLAM system based on fusion of LiDAR and inertial navigation ensures precise positioning and navigation with the help of an overall path planner using an A* algorithm and a 3D scanning constructed local virtual environment. Multi-sensor fusion localization, path planning, and control enable autonomous operation. Experimental studies demonstrated it can automatically move, pause, steer, and unload along predefined trajectories. The driving capacity and range of electric chassis reach the design specifications, whose walking speeds approach set speeds (<5% error). Under various loads, the vehicle closely follows the target path with very small tracking errors. Initial test points showed high localization accuracy at maximum longitudinal and lateral deviations of 9.5 cm and 6.7 cm, while the average value of the lateral deviation of other points below 5 cm. These findings contribute to the advancement of uncrewed transportation technology and equipment in greenhouse applications. Full article

Crop phenotype detection is a precise way to understand and predict the growth of horticultural seedlings in the smart agriculture era to increase the cost-effectiveness and energy efficiency of agricultural production. Crop phenotype detection requires the consideration of plant stature and agricultural devices, like robots and autonomous vehicles, in smart greenhouse ecosystems. However, collecting the imaging dataset is a challenge facing the deep learning detection of plant phenotype given the dynamic changes among leaves and the temporospatial limits of camara sampling. To address this issue, digital cousin is an improvement on digital twins that can be used to create virtual entities of plants through the creation of dynamic 3D structures and plant attributes using RGB image datasets in a simulation environment, using the principles of the variations and interactions of plants in the physical world. Thus, this work presents a two-phase method to obtain the phenotype of horticultural seedling growth. In the first phase, 3D Gaussian splatting is selected to reconstruct and store the 3D model of the plant with 7000 and 30,000 training rounds, enabling the capture of RGB images and the detection of the phenotypes of the seedlings, overcoming temporal and spatial limitations. In the second phase, an improved YOLOv8 model is created to segment and measure the seedlings, and it is modified by adding the LADH, SPPELAN, and Focaler-ECIoU modules. Compared with the original YOLOv8, the precision of our model is 91%, and the loss metric is lower by approximately 0.24. Moreover, a case study of watermelon seedings is examined, and the results of the 3D reconstruction of the seedlings show that our model outperforms classical segmentation algorithms on the main metrics, achieving a 91.0% mAP50 (B) and a 91.3% mAP50 (M). Full article

The global transport sector, a significant contributor to energy consumption and greenhouse gas (GHG) emissions, requires innovative solutions to meet sustainability goals. Artificial intelligence (AI) has emerged as a transformative technology, offering opportunities to enhance energy efficiency and reduce GHG emissions in transport systems. This study provides a comprehensive review of AI’s role in optimizing vehicle energy management, traffic flow, and alternative fuel technologies, such as hydrogen fuel cells and biofuels. It explores AI’s potential to drive advancements in electric and autonomous vehicles, shared mobility, and smart transportation systems. The economic analysis demonstrates the viability of AI-enhanced transport, considering Total Cost of Ownership (TCO) and cost-benefit outcomes. However, challenges such as data quality, computational demands, system integration, and ethical concerns must be addressed to fully harness AI’s potential. The study also highlights the policy implications of AI adoption, underscoring the need for supportive regulatory frameworks and energy policies that promote innovation while ensuring safety and fairness. Full article

Autonomous electric vehicles (AEVs) can potentially revolutionize the transportation landscape, offering a safer, contact-free, easily accessible, and more eco-friendly mode of travel. Prior to the market uptake of AEVs, it is critical to understand the consumer segments that are most likely to adopt these vehicles. Beyond market adoption, it is also important to quantify the impact of AEVs on broader transportation systems and the environment, such as impacts on the annual vehicle miles traveled (VMT) and greenhouse gas (GHG) emissions. In this pilot study, using survey data, a statistical model correlating AEV adoption intention and socioeconomic and built environment attributes was estimated, and a sensitivity analysis was conducted to understand the importance of factors impacting AEV adoption. We found that the market segments range from early adopters who are wealthy, technologically savvy, and relatively young to non-adopters who are more cautious to new technologies. This is followed by a synthetic population microsimulation of market penetration for the San Francisco Bay Area. With five household vehicle replacement scenarios, we assessed the annual VMT and tailpipe carbon dioxide (CO₂) emissions change associated with vehicle replacement. It is found that adopting AEVs can potentially reduce more than 5 megatons of CO₂ yearly, which is approximately 30% of the total CO₂ emitted by internal combustion engine (ICE) cars in the region. Full article

This paper introduce advancements in agricultural robotics in response to the increasing demand for automation in agriculture. Our research aims to develop humancentered agricultural robotic systems designed to enhance efficiency, sustainability, and user experience across diverse farming environments. We focus on essential applications where human labor and experience significantly impact performance, addressing four primary robotic systems, i.e., harvesting robots, intelligent spraying robots, autonomous driving robots for greenhouse operations, and multirobot systems, as a method to expand functionality and improve performance. Each system is designed to operate in unstructured agricultural environments, adapting to specific needs. The harvesting robots address the laborintensive demands of crop collection, while intelligent spraying robots improve precision in pesticide application. Autonomous driving robots ensure reliable navigation within controlled environments, and multirobot systems enhance operational efficiency through optimized collaboration. Through these contributions, this study offers insights into the future of agricultural robotics, emphasizing the transformative potential of integrated, experience-driven intelligent solutions that complement and support human labor in digital agriculture. Full article

Danxiaorchis yangii, a newly discovered fully mycoheterotrophic orchid. It relies on Lysimachia alfredii and Dufourea spp. for pollination, and environmental factors closely influence the growth and distribution of these pollinators, which in turn directly affects the growth and reproduction of D. yangii. Climate change threatens the suitable habitats for these three species, emphasizing the need to understand D. yangii’s response. This study comprehensively utilized the field distribution of D. yangii and related climatic data, along with future climate predictions from global models, to predict the climate suitability areas of D. yangii under two greenhouse gas emission scenarios (SSP245 and SSP370) using species distribution models (SDMs), which encompassed a random forest (RF) model. Additionally, we selected the optimal ensemble model (OEM) for Dufourea spp. and applied generalized boosted models (GBMs) and RF for L. alfredii in our predictions. The study found that precipitation of the driest quarter plays a pivotal role in determining the distribution of D. yangii, with an optimal range of 159 to 730 mm being most conducive to its growth. Comparative analysis further indicated that precipitation exerts a greater influence on D. yangii than temperature. Historically, D. yangii has been predominantly distributed across Jiangxi, Hunan, Zhejiang, and the Guangxi Zhuang Autonomous Region, with Jiangxi Province containing the largest area of highly suitable habitat, and this distribution largely overlaps with the suitable regions of its pollinators. Full article

Electrostatic spraying is considered an effective means to improve the efficacy of pesticide application and reduce pesticide consumption. However, the effectiveness of electrostatic spraying needs further validation in greenhouse environments, especially in long-range air-assisted spraying scenarios. A waist-shaped charging device has been improved to obtain a maximum charge-to-mass ratio of 4.4 mC/kg at an applied voltage of 6 kV in a laboratory setting, representing an increase of approximately 84.9% compared to a commercial circular charging electrode with a fan-shaped nozzle. A comparative air-assisted spray test between electrostatic deactivation (EDAS) and electrostatic activation (EAAS) was conducted on greenhouse tomato crops using a single hanging track autonomous sprayer equipped with a pair of waist-shaped charging devices. The results showed that EAAS yielded an overall average coverage of 28.4%, representing a significant 10.9% improvement over the 25.6% coverage achieved with EDAS. The overall coefficient of variation (CV) for EDAS and EAAS was 62.0% and 48.0%, respectively. Within these, the CV for the average coverage of the sample set reflecting axial distribution uniformity was 33.4% and 31.4%, respectively. Conversely, the CV for the average coverage of the sample group reflecting radial distribution uniformity was 33.7% and 17.9%, respectively. The results indicate that the waist-shaped charging device possesses remarkable charging capabilities, presenting favorable application prospects for long-range air-assisted spraying in greenhouses. The electrostatic application has a positive effect on enhancing the average coverage and improving the overall distribution uniformity. Notably, it significantly improves the radial distribution uniformity of the air-assisted spray at long range, albeit with limited improvement in the axial distribution uniformity. Full article

Industrial development has implicitly led to the development of new systems that increase the ability to provide services and products in real time. Autonomous mobile robots are considered some of the most important tools that can help both industry and society. These robots offer a certain autonomy that makes them indispensable in industrial activities. However, some elements of these robots are not yet very well outlined, such as their construction, their lifetime and energy consumption, and the environmental impact of their activity. Within the context of European regulations (here, we focus on the Green Deal and the growth in greenhouse gas emissions), any industrial activity must be analyzed and optimized so that it is efficient and does not significantly impact the environment. The added value of this paper is its examination of the activities carried out by mobile robots and the impact of their electronic components on the environment. The proposed analysis employs, as a central point, an analysis of mobile robots from the point of view of their electronic components and the impact of their activity on the environment in terms of energy consumption, as evaluated by calculating the emission of greenhouse gases (GHGs). The way in which the activity of a robot impacts the environment was established throughout the economic flow, as well as by providing possible methods of reducing this impact by optimizing the robot’s activity. The environmental impact of a mobile robot, in regard to its electronic components, will also be analyzed when the period of operation is completed. Full article