Current Lycium barbarum L. vibration harvesting equipment exhibits low levels of intelligence and precision, often resulting in a trade-off between efficiency and fruit damage. This study proposed a ripe fruit region detection model, YOLO-RFR, specifically for precision vibration harvesting of L. barbarum. First, the ADown downsampling module was introduced to replace part of the conventional convolution layers. Then, the C3k2-AP module, inspired by the asymmetric padding strategy, was designed to replace the C3k2 module. Additionally, the GCHead detection head was constructed using group convolution. Finally, the EMA-Slide Loss function was developed to optimize the classification performance by combining the slide weighting function with Exponential Moving Average (EMA). The experimental results showed that the model achieved precision, recall, and mAP of 93.7%, 92.0%, and 97.0%, respectively, representing improvements of 4.0%, 4.4%, and 2.6% over the baseline. The parameter, floating-point operations (FLOPs), and model size were 1.7 M, 4.1 G, and 3.8 MB, respectively, corresponding to decreases of 34.6%, 34.9%, and 30.9% compared with the baseline. To further validate its practical feasibility, the improved model was deployed on an NVIDIA Jetson AGX Xavier embedded device, achieving an inference speed of 163 fps with TensorRT acceleration. In conclusion, the YOLO-RFR model demonstrated excellent performance in detection accuracy, model lightweighting, and deployment on embedded devices, providing strong technical support for the precision vibration harvesting of L. barbarum.

Unmanned aerial vehicle (UAV) wind field airflow is the main factor affecting spraying width and operational effect. Under the soybean and maize compound planting mode, the width of the spray droplets of UAV aerial spraying is easily sprayed on other crops around the target crop by mistake due to the influence of the wind field. In order to solve this problem, a narrow-width spraying UAV equipped with an airflow guidance device was developed in this study, which can achieve precise spraying on target crops within a narrow operation width and reduce the impact of droplet drift on surrounding non-target crops. The wind field of the flight platform was simulated via simulation software, and the wind field distribution characteristics corresponding to three sizes of the guidance device were analyzed. It was verified that the guidance device has a segmentation effect on the wind field, and the optimal size of the guidance device was determined as 0.92 m accordingly. Meanwhile, the installation position and quantity of nozzles were determined to be set at the position with the minimum airflow disturbance. The wind speed at measuring points with different angles on four diameters at five heights under the UAV hovering state was tested through bench tests, to further verify the simulation results and appropriately adjust the nozzle position according to the measured wind speed. Outdoor flight spraying tests were carried out, and the results showed that when the 0.92 m guidance device was applied at a flight altitude of 1.00 m, the effective spray width was only 1.46 m according to the droplet deposition density on the coated paper of three test collection belts. Under the maize-soybean composite planting pattern, the field spraying test yielded results of 18.0%-30.0% deposition rate in the maize area and 0.1%-1.6% in the soybean area with maize as the operational target. A predictive effect of wind field simulation on the installation position of nozzles where droplets suffer the least wind field disturbance under aerial spraying conditions was confirmed. The UAV wind field can be effectively segmented by the airflow guidance device, and the diameter of the droplet-laden airflow column can be reduced, thus realizing narrow-width spraying.

China has emerged as the world’s largest producer of scientific publications and a dominant force across high-impact research indicators. Yet, this extraordinary expansion has not translated into Nobel-level breakthroughs. This commentary examines the structural, institutional, and cultural factors underpinning this “Nobel paradox.” China’s research ecosystem is optimized for rapid scaling, publication productivity, and alignment with national policy cycles, but these strengths also generate incentives that discourage high-risk, conceptually disruptive inquiry. Comparative analysis with Japan and the United States reveals that environments producing Nobel-winning discoveries typically feature long-term stability, investigator autonomy, tolerance for failure, and mechanisms that empower early-career scientists. In China, hierarchical authorship norms, metric-driven evaluations, and risk-averse grant structures hinder the emergence of transformative ideas, despite the abundance of talent and resources. The commentary outlines reforms, such as decoupling assessment from publication metrics, creating safe harbors for high-risk research, and strengthening career pathways, that could enable China to convert its scientific capacity into world-changing discovery.

To enable efficient and low-cost automated apple harvesting, this study presented a multi-class instance segmentation model, SCAL (Star-CAA-LADH), which utilizes a single RGB sensor for image acquisition. The model achieves accurate segmentation of fruits, fruit-bearing branches, and main branches using only a single RGB image, providing comprehensive visual inputs for robotic harvesting. A Star-CAA module was proposed by integrating Star operation with a Context-Anchored Attention mechanism (CAA), enhancing directional sensitivity and multi-scale feature perception. The Backbone and Neck networks were equipped with hierarchically structured SCA-T/F modules to improve the fusion of high- and low-level features, resulting in more continuous masks and sharper boundaries. In the Head network, a Segment_LADH module was employed to optimize classification, bounding box regression, and mask generation, thereby improving segmentation accuracy for small and adherent targets. To enhance robustness in adverse weather conditions, a Chain-of-Thought Prompted Adaptive Enhancer (CPA) module was integrated, thereby increasing model resilience in degraded environments. Experimental results demonstrate that SCAL achieves 94.9% AP_M and 95.1% mAP_M, outperforming YOLOv11s by 6.6% and 4.6%, respectively. Under multi-weather testing conditions, the CPA-SCAL variant consistently outperforms other comparison models in accuracy. After INT8 quantization, the model size was reduced to 14.5 MB, with an inference speed of 47.2 frames per second (fps) on the NVIDIA Jetson AGX Xavier. Experiments conducted in simulated orchard environments validate the effectiveness and generalization capabilities of the SCAL model, demonstrating its suitability as an efficient and comprehensive visual solution for intelligent harvesting in complex agricultural settings.

China plants approximately 20.3 million hm² of winter wheat annually. During the recent one-month harvesting period, hundreds of thousands of combine harvesters participated in wheat harvesting from south to north. However, the total number of active harvesters remains a challenge, restricting government policy-making and industry analysis. This study proposed a nonparametric bootstrap estimation model based on big data to dynamically infer the total number of active agricultural machines by analyzing the spatio-temporal trajectories of harvesters. Through Monte Carlo simulation experiments, the performance of four nonparametric bootstrap methods was systematically evaluated from dimensions such as bias, mean squared error, and coverage probability. The results show that the bias-corrected and accelerated bootstrap method (BCa) performs best and was selected as the 95% confidence interval estimation method. The 95% confidence intervals for the total number of active harvesters in 2021, 2022, and 2023 are [447 223, 456 387], [441 708, 447 625], and [436 873, 440 608], respectively, providing a quantitative basis for regulatory supervision and capacity planning in the agricultural machinery industry.

The low-altitude economy has gained significant attraction in recent years, particularly in the context of smart agriculture. It is very necessary for innovative solutions to enhance agricultural productivity and sustainability, due to the more pressing populations and scarce arable land. The low-altitude economy can be expected to offer a promising avenue for these challenges. This review was focused on the frontiers and hotspots in the agricultural application of the low-altitude economy in rural areas. A literature analysis was employed to analyze the journal articles and global patents from 2000 to 2024. The keyword co-occurrence networks and citation relationships were determined to identify the research hotspots and directions. Low-altitude technologies (particularly unmanned aerial vehicles, UAVs) also shared key applications in agricultural information acquisition, plant protection, land resource management, environmental monitoring, and rural logistics. The UAVs equipped with the advanced sensors were used to collect real-time data on crop health and growth, soil conditions, and weather patterns, in order to optimize irrigation, fertilization, and pest control. Precision spraying also reduced the chemical usage. While the land resource management benefited from the accurate monitoring. Environmental and disaster monitoring with UAVs also enabled swift responses to floods, droughts, and wildfires. Additionally, the UAVs also transformed into agricultural logistics, particularly for the efficient transport solutions in remote areas. Several technological challenges were given to fully realize the immense potential of the low-altitude economy. One of the primary challenges was to specially design advanced UAVs in diverse and harsh agricultural environments. The better performance was required for the flight stability, battery life, and payload capacity. Flight control systems were also required for safe and efficient operations in the crowded airspaces. The data accuracy and reliability were further enhanced to refine the onboard mission payloads and auxiliary equipment, such as the high-resolution cameras and multispectral sensors. Information perception and precision operation were critical to real-time data processing and decision-making. The air-ground collaborative control systems were essential to integrate the low-altitude operations with the existing agricultural infrastructure. The successful implementation of the low-altitude economy in agriculture was dependent heavily on the decision-making on the regulatory frameworks. Furthermore, the legal systems and industry standards were established to manage the low-altitude airspace for safety, efficiency, and accountability in Europe and the United States. These frameworks also provided valuable implications for other countries and regions, including China. Fine-grained management was selected to promote the pilot of low-altitude airspace opening in smart agriculture; Streamline certification was also utilized to optimize the airworthiness approval procedures for the low-altitude aircraft; Regional low-altitude economy was also enhanced to construct the industrial clustering and the testing bases. Future research can be focused on lightweight sensors and intelligent algorithms. An "air-space-ground" monitoring network can be expected to integrate and optimize the dynamic airspace. Further applications can also be expanded into carbon sink monitoring, biological breeding, and disaster emergency response. This finding can provide theoretical and practical references to accelerate the low-altitude economy in rural areas. The great contribution can also be gained to the agricultural modernization and rural revitalization.

Agricultural activities in Japan have been limited to the aging and diminishing labor force, as well as the small plots of cultivated land in mountainous regions. Therefore, smart agriculture has been launched to ensure food security in Japan. A systematic framework has been provided to support the digitization of forestry and aquaculture, including the basic Law and national strategy. Numerous technological innovations have also involved machinery automation, information systems, rural network transformation, agricultural aviation, and plant factories. Modern agriculture has allowed the 1 404 000 core farmers and the majority of the elderly population to achieve nearly 130 million people self-sufficient in the major food. Among them, the Japan Agricultural Association has played a pivotal role in this transformation, covering the various processes at multiple levels and departments. These fundamental facilitates have provided crucial support to implement smart agriculture. This study aims to analyze the trajectory and efficacy of smart agriculture in Japan. The valuable insights were then obtained for the several suggestions. Firstly, it was very necessary to innovate the agricultural system and mechanism, in order to create the channels for the seamless circulation of information, manpower, and resources. The agricultural environment was sustained to leverage the strengths of diverse entities, such as local governments, universities, and enterprises. Long-term empirical research was performed on smart agricultural technology in rural revitalization. These insights should be adapted to the local conditions in the agricultural production, sales, promotion, and management from the Japanese Farmers' Association's experience. The findings can greatly contribute to the revitalization of rural areas in China. In turn, the trajectory of rural modernization can be accelerated to position as a formidable force in the realm of smart agriculture. These lessons can also be incorporated into sustainable and technologically advanced agricultural systems.

Over the past 30 years, there has been continuous progress in global science and technology. However, many agricultural products still heavily rely on traditional methods of manual and mechanical harvesting, facing challenges such as high costs and low efficiency. To address these challenges, researchers have developed various harvesting robots to handle diverse tasks in complex farm environments. This study analyzed pertinent papers on harvesting robots retrieved from the Web of Science (WOS) core database and the China National Knowledge Infrastructure (CNKI) database, spanning the years 1993 to 2022. Using specialized software such as CiteSpace and VOSviewer, a bibliometric analysis was conducted to examine the research progress and hotspots in the field of harvesting robots. The analysis of 517 English papers indicated a continuous expansion in the research scale of harvesting robots. Furthermore, the research history can be divided into three distinct periods. Currently, research on harvesting robots is experiencing a rapid growth phase, with the number of related papers steadily increasing each year. In the year 2022 alone, 151 English papers were published. This growth is attributed to close collaborations among different countries/regions, institutions, and authors. China, the United States, and Japan play crucial roles in the research of harvesting robots. Notably, China has published 326 English papers, ranking first globally. Through analysis, it was also found that Chinese papers focused on harvesting robots earlier, thereby promoting the development of agricultural robots. Additionally, bibliometric analysis revealed that the research hotspots of harvesting robots mainly include system and structure design, object recognition and localization, and multi-robot coordination, among others. In the future, development trends of harvesting robots will focus on: 1) diversifying robot types, 2) expanding application scenarios,3) enhancing overall performance to reduce losses, and 4) reducing manufacturing costs. In conclusion, through a comprehensive bibliometric analysis, this study has provided valuable insights to advance the automation of harvesting.