Microalgae are a type of single-cell autotrophic organisms with the advantages of fast growth rate and strong environmental adaptability. The carbohydrates, proteins, lipids produced by them have been widely used in food, aquaculture, healthcare and other fields. However, the high cost of cultivation has always been one of the difficulties for its industrial development. Light source is a critical environmental factor for microalgae cultivation. Existing studies have reported the effects of wavelength and photoperiod on the growth and metabolism of microalgae. Significantly, the light sources used in these studies almost are arranged on one side, and the uniformity and stability of the light distribution in the algal liquid are easily affected by the algal solution's optical diameter, algal density, and other factors. In this study, the commercial microalgae: C. pyrenoidosa was used as the research object, and the wraparound light emitting diode (LED) lights were adopted as an artificial light source for microalgae cultivation. Six light qualities of white, red, yellow, green, blue, and purple with a light intensity of 100 μmol/(m2·s) were applied to cultivate C. pyrenoidosa for 21 days. The biomass and photosynthetic pigment contents in the algal liquid were monitored daily. The protein, lipid, and carbohydrate contents in C. pyrenoidosa were determined, as well as the composition and abundance of fatty acids at the end of cultivation (D21), thereby to explore the dynamic patterns of six light qualities on the growth and metabolites of C. yrenoidosa. The results revealed that the growth rates of C. yrenoidosa cultured under green and blue LED were significantly higher than the control (white LED) from 7th and 9th day (P<0.05), respectively. At the end of cultivation (D21), the biomass in the green and blue groups increased by 24.4% and 8.0% as compared with the control, respectively. The trends of chlorophyll-a in algal liquid were similar to that of the biomass, but the total pigment of C. yrenoidosa in the green group was significantly lower than that of the purple group (P<0.05), which might be related to the low absorption and utilization rate of purple light by C. yrenoidosa (more photosynthetic pigments need to be synthesized to obtain light energy). Red LED could increase the carbohydrate content by 11.5% and reduce the lipid by 23.8% (P<0.05). Blue LED had the best positive effect on the lipid-accumulation, with a significant increment of 26.2% (P<0.05). Purple LED promoted carbon allocation from carbohydrates to protein synthesis in C. pyrenoidea. Fatty acid analysis indicated that green and purple LED significantly promoted total fatty acids (TFAs) synthesis (20.1% and 18.2%) as compared with the control group (39.5‰) (P<0.05). Moreover, green and blue LED were more conducive to the synthesis of polyunsaturated fatty acids. This study would not only provide an effective light source configuration scheme for the optimal regulation of intracellular carbon allocation of C. pyrenoidosa, but also provide some theoretical reference and technical support for the efficient and high-quality production of C. pyrenoidea.
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New agricultural monitoring technologies based on satellite remote sensing, unmanned aerial vehicle remote sensing, intelligent sensing terminals and IoT network have made significant progress in their respective fields. However, it is difficult for single monitoring ways to meet the comprehensive sensing needs of modern agriculture, so it is urgent to develop a multi-source, multi-scale of space-air-ground cooperative monitoring and intelligent sensing system. The study first introduces some problems in China's agricultural development and points out the necessity of using space-air-ground information fusion technology; then analyzes the traditional sensing information technology of space, air and ground respectively. The applications of satellite remote sensing technology in different fields and the data acquisition and processing methods of UAV remote sensing technology in the subdivision fields of pest and disease detection, phenotype analysis and drought stress detection are summarized. Finally, the key technologies and networking methods of intelligent sensing terminal and IoT networks in ground networks are analyzed, and advantages and disadvantages, key technologies, and future development trends of space, air, ground network are analyzed respectively in detail. Combined with the deficiencies of the existing monitoring technology, this study summarizes the latest research results and applications of space, air and ground sensing technology in the integrated agricultural situation monitoring at home and abroad. Based on above analysis, the key technical problems that have not been solved in the research and application of space-air-ground integrated agricultural information sensing and fusion technology are pointed out. In addition, this study proposes that China's space-air-ground information sensing and fusion technology should develop in the direction of stability, refinement, and systematization, which provides a new perspective for the development and application of multi-source information fusion monitoring technology in the future. This study provides a reference for analyzing the hotspots of space, air and ground information sensing, breaking through the bottleneck of their applications, and grasping the development trend of integrated sensing and fusion technology, so as to enhance development of China's agricultural information perception in the way of three-dimension, preciseness and intelligence.
The agricultural low-altitude economy has rapidly emerged as a transformative force in rural modernization, catalyzed by advancements in intelligent technologies and gradual improvements in low-altitude airspace governance. It encompasses a wide array of applications where aerial platforms—particularly unmanned aerial vehicles (UAVs)—play a pivotal role in reshaping agricultural production, environmental monitoring, and rural management systems. Systematically analyzing the development trajectory and implementation pathways of agricultural low-altitude economy holds considerable significance for promoting cross-sector integration, accelerating policy and technological innovation, and enabling the widespread adoption of intelligent solutions across agriculture-related industries.
This review draws upon an extensive body of literature and applies keyword clustering analysis to systematically explore the development of the agricultural low-altitude economy. Based on 13570 peer-reviewed articles retrieved from the Web of Science database between 2000 and 2024, the study reveals a rapid growth trajectory, particularly since 2011, driven by technological breakthroughs in UAVs, sensors, and intelligent analytics. Five representative application domains were identified: smart farming, animal husbandry, forestry, fisheries, and rural governance. Through the integration of bibliometric tools and structured keyword combinations, the study captures both the evolution of research focus and the expansion of technical capabilities. The results demonstrate that agricultural low-altitude economy has progressed from early-stage feasibility validation toward large-scale, multi-functional applications. At the same time, it has catalyzed the development of an emerging industrial framework encompassing equipment manufacturing, aerial service provision, operational support systems, and talent development. These trends highlight the growing maturity and strategic relevance of agricultural low-altitude economy as a technological enabler for modern agriculture. In the context of smart farming, low-altitude technologies are extensively utilized for precision sowing, variable-rate fertilization, real-time crop health monitoring, and pest and disease detection. UAV-based remote sensing facilitates the creation of high-resolution field maps and spatially explicit data layers that support data-driven and site-specific decision-making in modern agricultural management. In smart livestock systems, drones are employed for livestock monitoring, early disease detection, and fence-line inspections, particularly in large, remote pasture areas with limited ground accessibility. Smart forestry applications include forest fire early warning, forest inventory updates, and dynamic monitoring of ecological changes, enabled by low-altitude hyperspectral, LiDAR, and thermal imaging technologies. For smart fisheries, UAVs and amphibious drones support water quality sensing, pond surveillance, and feeding behavior analysis, thereby enhancing aquaculture productivity, animal welfare, and environmental sustainability. In the broader context of smart rural governance, agricultural low-altitude economy technologies assist with infrastructure inspections, land use monitoring, rural logistics coordination, and even public security surveillance, contributing to comprehensive, intelligent rural revitalization. Alongside scenario-based applications, this paper also summarizes the current structure of the agricultural low-altitude economy industry chain, which is preliminarily composed of four key components: manufacturing, flight operations, supporting services, and integrated service platforms. In the manufacturing segment, the development and production of specialized agricultural UAVs, multi-rotor drones, and fixed-wing VTOL aircraft are advancing rapidly, enabling adaptation to various terrains and crop types. The flight operations segment is expanding with increasing market participation, offering aerial spraying, broadcasting, surveying, and inspection services with improved timeliness and precision. Supporting services include airspace coordination, meteorological forecasting, equipment maintenance, and safety supervision. Additionally, comprehensive service systems have emerged, integrating digital platform management, third-party evaluation, data analysis, and agricultural technical advisory services. Talent development and training form a crucial pillar of agricultural low-altitude economy's development. A growing number of vocational institutions and drone enterprises are providing structured training programs for agricultural drone pilots, technicians, and system operators. These programs aim to enhance operational capabilities, ensure safety compliance, and foster human capital that can support the sustainable growth of agricultural low-altitude economy applications across rural regions.
Agricultural low-altitude economy is transitioning from isolated, technology-specific applications to an integrated, platform-oriented ecosystem that blends equipment, data, services, and governance. To fully realize its potential, several strategic directions should be pursued. First, there is a pressing need to refine regulatory frameworks to accommodate the unique operational characteristics of rural low-altitude airspace, including dynamic zoning policies, UAV registration standards, and risk management protocols. Second, sustained investment in core technologies, such as autonomous flight control, multi-modal sensing, and AI-based analytics, are essential for overcoming current technical limitations and unlocking new capabilities. Third, agricultural low-altitude economy's infrastructure backbone should be reinforced through the deployment of distributed drone ports, mobile ground control stations, and secure data networks, particularly in underserved regions. Additionally, establishing a national-scale training and certification framework is critical for ensuring an adequate supply of skilled professionals, while innovative funding models such as public-private partnerships and scenario-based insurance can enhance accessibility and scalability. Pilot projects and demonstration zones should also be expanded to validate and refine agricultural low-altitude economy systems under diverse agroecological and socio-economic conditions. In summary, agricultural low-altitude economy offers strategic leverage for advancing China's goals in smart agriculture, green development, and rural revitalization. With cross-sector collaboration and proactive policy support, agricultural low-altitude economy can evolve into a resilient and inclusive ecosystem that fosters agricultural transformation, boosts productivity, and enhances environmental sustainability.
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.
With the rapid development of robotics technology and the persistently rise of labor costs, the application of robots in facility agriculture is becoming increasingly widespread. These robots can enhance operational efficiency, reduce labor costs, and minimize human errors. However, the complexity and diversity of facility environments, including varying crop layouts and lighting conditions, impose higher demands on robot navigation. Therefore, achieving stable, accurate, and rapid navigation for robots has become a key issue. Advanced sensor technologies and algorithms have been proposed to enhance robots’ adaptability and decision-making capabilities in dynamic environments. This not only elevates the automation level of agricultural production but also contributes to more intelligent agricultural management.
This paper reviews the key technologies of automatic navigation for facility agricultural robots. It details beacon localization, inertial positioning, simultaneous localization and mapping（SLAM）techniques, and sensor fusion methods used in autonomous localization and mapping. Depending on the type of sensors employed, SLAM technology could be subdivided into vision-based, laser-based and fusion systems. Fusion localization is further categorized into data-level, feature-level, and decision-level based on the types and stages of the fused information. The application of SLAM technology and fusion localization in facility agriculture has been increasingly common. Global path planning plays a crucial role in enhancing the operational efficiency and safety of facility aricultural robots. This paper discusses global path planning, classifying it into point-to-point local path planning and global traversal path planning. Furthermore, based on the number of optimization objectives, it was divided into single-objective path planning and multiobjective path planning. In regard to automatic obstacle avoidance technology for robots, the paper discusses sevelral commonly used obstacle avoidance control algorithms commonly used in facility agriculture, including artificial potential field, dynamic window approach and deep learning method. Among them, deep learning methods are often employed for perception and decision-making in obstacle avoidance scenarios.
Currently, the challenges for facility agricultural robot navigation include complex scenarios with significant occlusions, cost constraints, low operational efficiency and the lack of standardized platforms and public datasets. These issues not only affect the practical application effectiveness of robots but also constrain the further advancement of the industry. To address these challenges, future research can focus on developing multi-sensor fusion technologies, applying and optimizing advanced algorithms, investigating and implementing multi-robot collaborative operations and establishing standardized and shared data platforms.
Open Access
Research Article
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Plant height can be used for assessing plant vigor and predicting biomass and yield. Manual measurement of plant height is time-consuming and labor-intensive. We describe a method for measuring maize plant height using an RGB-D camera that captures a color image and depth information of plants under field conditions. The color image was first processed to locate its central area using the S component in HSV color space and the Density-Based Spatial Clustering of Applications with Noise algorithm. Testing showed that the central areas of plants could be accurately located. The point cloud data were then clustered and the plant was extracted based on the located central area. The point cloud data were further processed to generate skeletons, whose end points were detected and used to extract the highest points of the central leaves. Finally, the height differences between the ground and the highest points of the central leaves were calculated to determine plant heights. The coefficients of determination for plant heights manually measured and estimated by the proposed approach were all greater than 0.95. The method can effectively extract the plant from overlapping leaves and estimate its plant height. The proposed method may facilitate maize height measurement and monitoring under field conditions.
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