[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-845":3,"related-845":67},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":8,"secondary_topics":33,"classification":34,"search_phrases":62,"slug":65,"view_count":56,"doi":8,"paper":8,"created_at":66},845,"MDPI Sustainability:中国农用无人机社会经济可持续性分析——玉米ROI 10.53%、柑橘ROI 7.65%,缓解劳动力短缺而非大规模失业(原标题:Socioeconomic Sustainability of Agricultural Drone Adoption in China)","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596","宁波大学陈欢等评估中国农业无人机的发展规模和经济效应:玉米和柑橘的实证分析表明,无人机辅助农业降低了生产成本;在基准价格下无人机辅助玉米和柑橘生产的ROI分别提升至10.53%和7.65%,但仍较低,说明过度资本介入的风险有限;在全国范围内无人机辅助农业为玉米和柑橘带来估计的成本节约;在劳动力效应方面,农业无人机主要帮助缓解农业劳动力短缺而非产生大规模失业,同时提高生产效率,支持可持续农业发展。",null,"## 1. Introduction\n\nTraditional agricultural practices exhibit suboptimal efficiency, often requiring substantial time and labor while struggling to maintain equilibrium between input and output. Consequently, enhancing efficiency in production and logistics while reducing operational costs has become critically important [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B1-sustainability-18-08596)]. Recently, the rapid expansion of the low-altitude economy has accelerated the integration of emerging technologies—particularly drones and remote sensing—into the agricultural sector, bringing profound transformation to modern agriculture [[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B2-sustainability-18-08596),[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B3-sustainability-18-08596)].\n\nAgricultural drones are intelligent systems operated through remote control or autonomous programming and exhibit three defining characteristics. First, their superior maneuverability and operational flexibility enable them to reach designated areas rapidly and adapt to complex terrain. Second, their high operational efficiency allows rapid deployment in remote or difficult-to-access areas without being constrained by ground transportation. Third, their advanced automation and intelligence, supported by multisensor systems, enable real-time environmental monitoring and autonomous flight-path planning based on predefined objectives and environmental conditions. These characteristics give agricultural drones substantial potential for agricultural applications. Agricultural drones are currently used in many countries for a wide range of operations. Their applications have expanded from orchard pest management to precision pesticide application, irrigation, seeding, and agricultural product transportation across crop cultivation, forestry, livestock farming, and aquaculture. This technological integration improves operational efficiency while reducing labor costs and resource waste. Consequently, global agricultural drone adoption has increased substantially, with [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#fig_body_display_sustainability-18-08596-f001) presenting regional patterns of agricultural drone use from 2018 to 2024.\n\n**Figure 1.** Drone usage in the agricultural sector by region worldwide, 2018–2024. Note: Data on the drone usage in the agricultural sector are preliminary statistics [[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B4-sustainability-18-08596)].\n\n[Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#fig_body_display_sustainability-18-08596-f001) illustrates a fluctuating upward trajectory in global agricultural drone adoption, with notably accelerated growth during 2023–2024. Regarding regional differences in drone use, usage levels in North America and Europe are broadly similar; however, European utilization marginally surpassed North American deployment in 2023 and 2024. Concurrently, the Asia-Pacific region demonstrates progressive expansion of agricultural drone applications, reflecting incremental acceptance of this technology.\n\nTo stimulate new drivers for national economic development and enhance economic vitality, the low-altitude economy has been positioned as a strategic growth engine, charting the course for future development. Since 2024, 27 Chinese provinces (municipalities\u002Fautonomous regions) have referenced the low-altitude economy in their local government work reports. Driven by factors such as policy support, market demand, and capital investment, the low-altitude economy market will maintain rapid expansion. As a crucial component of this sector, civil drones have become a major category of aircraft, capable of collaborating with or replacing human labor in executing various commercial tasks such as inspections and crop protection. In 2023, the industry scale reached 117.43 billion yuan, marking a 32% year-on-year growth. With their “three-high” attributes—high value, high-frequency usage, and high-frequency renewal—drones are increasingly favored by capital, signaling broad development prospects.\n\nFurthermore, in recent years, China’s Ministry of Agriculture and Rural Affairs has implemented measures such as high-standard farmland construction and land trusteeship. These efforts address land ownership and circulation issues, promoting agricultural specialization and large-scale operation, thereby expanding drone applications in agriculture. The construction area and investment in high-standard farmland in China from 2019 to 2024 are illustrated in [Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#fig_body_display_sustainability-18-08596-f002).\n\n**Figure 2.** Development of high-standard farmland in China, 2019–2024. Note: Data on the construction area and investment in high-standard farmland in China from 2019 to 2024 are preliminary statistics.\n\n[Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#fig_body_display_sustainability-18-08596-f002) demonstrates the progressive expansion of China’s high-standard farmland construction, with cultivated area increasing annually. Coupled with land trusteeship policies, these initiatives will substantially unleash new agricultural productivity and amplify demand for intelligent machinery. Beyond rising adoption of intelligent machinery like agricultural plant protection drones, payload capacities will increase from 85 kg to over 100 kg to accommodate scaled operations. Driven by the dual policies of national high-standard farmland construction and land trusteeship, agricultural plant protection drones, which conform to the development trend of large-scale and specialized agricultural production, will provide strong support for the development of agricultural scale and specialization in China and promote the high-quality transformation and upgrading of agriculture.\n\nCurrently, drones are increasingly used in agricultural production for field mapping, targeted sowing and fertilization, crop monitoring, pest management, and agricultural product transportation. Compared with traditional manual operations, drones offer clear advantages in operational speed, efficiency, coverage, and precision. For example, drones can adjust flight routes, operating height, and application intensity according to field conditions, thereby improving the timeliness and accuracy of agricultural operations. These characteristics make drones an important technical tool for improving agricultural production efficiency and reducing unnecessary input and labor costs.\n\nNevertheless, the application of drones in agriculture is still developing, and their broader economic and socioeconomic implications have not been fully examined. Existing studies have mainly focused on the technical performance and operational advantages of drones, while relatively limited attention has been paid to their systematic effects on production costs, investment returns, labor demand, and farmers’ access to farmland. In addition, different crops have distinct production processes, cost structures, and operational requirements, which makes it difficult to directly evaluate the cost-saving effects and economic benefits of drone-assisted farming across crop categories. Therefore, a more systematic assessment is needed to clarify the actual role of drones in agricultural production.\n\nBased on this background, this study addresses the following research questions:\n\n(1)\nTo what extent can drone-assisted farming reduce agricultural production costs compared with traditional farming across different crop categories?\n\n(2)\nHow does drone adoption affect the ROI of agricultural production, and does the improved profitability reach a level that may attract excessive commercial capital intervention?\n\n(3)\nDoes the expansion of drone-assisted farming mainly help alleviate agricultural labor shortages, or could it create additional employment pressure and increase the risk of farmers being marginalized in agricultural production?\n\nTo answer these questions, this study systematically evaluates the development scale and economic effects of agricultural drones in China. First, the inventory and growth trend of agricultural drones are quantified to identify the current development scale of drone-assisted farming. Second, a comparative cost accounting framework is constructed to estimate agricultural production costs and ROI under traditional and drone-assisted farming modes. Third, the potential labor substitution capacity of agricultural drones is compared with the decline in China’s primary-industry employment to assess whether drone adoption mainly compensates for labor shortages or creates employment pressure. Through these analyses, this study provides a quantitative basis for understanding the role of drones in improving production efficiency, reducing agricultural costs, and supporting sustainable agricultural development.\n\nThe remainder of this paper is organized as follows. [Section 2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#sec2-sustainability-18-08596) reviews the relevant literature. [Section 3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#sec3-sustainability-18-08596) presents the problem description. [Section 4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#sec4-sustainability-18-08596) introduces the analytical framework and model construction. [Section 5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#sec5-sustainability-18-08596) reports the empirical results and discussion. [Section 6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#sec6-sustainability-18-08596) summarizes the main conclusions and future research directions.\n\n## 2. Literature Review\n\nDrone technology has become an important tool for advancing precision and sustainable agriculture by improving the efficiency, accuracy, and environmental performance of agricultural operations [[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B5-sustainability-18-08596),[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B6-sustainability-18-08596)]. Compared with traditional labor-intensive operations, drone-assisted operations provide notable advantages in terms of cost reduction and operational efficiency. In particular, drones can substantially reduce implementation costs and complete specific agricultural tasks up to 68 times faster than traditional approaches. This advantage enables farmers to cover extensive farmland within a shorter operational window, which is particularly valuable for time-sensitive agricultural activities. Moreover, with their high operational speed, autonomous operating capability, and potential to reduce labor dependence, drones have been increasingly adopted in modern agricultural production and management.\n\n### 2.1. Applications of Drones in Agriculture\n\nIn the agricultural sector, drones have demonstrated extensive applicability due to their integration with advanced technologies, computational capabilities, and onboard sensors. They provide strong support for crop management (e.g., mapping, monitoring, irrigation, and diagnostics), transportation, and early warning systems [[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B7-sustainability-18-08596),[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B8-sustainability-18-08596)]. Furthermore, drones are employed in various specific agricultural activities, including monitoring crop growth, estimating yields, assessing water stress, detecting weeds and pests, as well as precisely and efficiently delivering pesticides and fertilizers [[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B3-sustainability-18-08596)]. These capabilities highlight the critical role of drone technology in advancing modern agriculture, offering substantial economic and social benefits, which have attracted significant research attention globally.\n\nIn the field of environmental monitoring and crop management, drone mapping technology offers unprecedented precision and spatial resolution. Tsouros et al. [[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B9-sustainability-18-08596)] provided a comprehensive overview of agricultural drone applications, particularly emphasizing their capacity to generate maps with spatial resolutions as high as 0.5 cm\u002Fpixel. Such high-resolution data enables detailed analysis of field variations, which is crucial for implementing differentiated crop management strategies. Adão et al. [[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B10-sustainability-18-08596)] investigated methods for analyzing individual grapevines across different temporal scales using drones equipped with multispectral sensors. By systematically integrating RGB and multispectral imagery, they evaluated the spatial and temporal dynamics of vineyards. Their findings indicate that this approach can accurately assess the health status and growth vigor of individual grapevines, demonstrating potential applications in differentiated vineyard management and optimized viticultural practices. Gamboa-Cruzado et al. [[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B11-sustainability-18-08596)] systematically reviewed the application of drones in smart agriculture and found that Unmanned Aerial Vehicle (UAV)-based remote sensing has become increasingly important for crop monitoring, yield estimation, pest and disease detection, and water management, thereby supporting more precise, data-driven, and sustainable agricultural decision-making.\n\nIn the field of pest and disease monitoring and control, drone technology has demonstrated significant advancements in plant health assessment and early disease detection. De Abreu and van Deventer [[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B12-sustainability-18-08596)] conducted a comprehensive evaluation of the application of artificial intelligence (AI) and the Internet of Things (IoT) in agriculture, with particular emphasis on the potential of integrating drones into IoT systems for early disease detection. Their findings indicate that applying AI techniques to drone image analysis significantly enhances disease detection accuracy compared to traditional modes, providing critical technological support for AI-powered solutions and precision in agricultural production while helping reduce operational costs. Bouguettaya et al. [[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B13-sustainability-18-08596)] investigated the application of deep learning techniques for analyzing drone-captured imagery to identify plant diseases. Their review examined recent developments in computer vision technologies based on deep learning algorithms when integrated with drone platforms for crop health assessment, revealing the substantial potential of combining neural networks with aerial image analysis to improve the accuracy and efficiency of disease detection. Yang et al. [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B14-sustainability-18-08596)] reviewed the application of multimodal deep learning in sustainable plant care and emphasized that the integration of spectral, thermal, textural, environmental, and UAV-based data can substantially improve the accuracy and robustness of crop disease diagnosis, thereby enabling more timely and precise plant protection decisions.\n\n### 2.2. Applications of Drones in Logistics\n\nDrones are increasingly being adopted in the logistics sector due to their exceptional flexibility, cost-effectiveness, and environmental sustainability [[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B15-sustainability-18-08596),[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B16-sustainability-18-08596),[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B17-sustainability-18-08596)]. They demonstrate significant potential for delivering goods and services across various industries and public sectors, playing an important role in product transportation, last-mile delivery [[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B18-sustainability-18-08596),[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B19-sustainability-18-08596),[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B20-sustainability-18-08596)], and emergency logistics scenarios [[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B21-sustainability-18-08596)]. The technology has established a substantial research foundation supporting these applications.\n\nMoshref-Javadi et al. [[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B22-sustainability-18-08596)] proposed a Truck–Drone Delivery System (TDDS) in 2020 to enhance last-mile delivery efficiency and reduce customer waiting times. Their multimodal distribution framework demonstrated that coordinated truck–drone operations could establish customer-centric TDDS networks, showing significant reductions in customer waiting time compared to truck-only systems. Furthermore, SAVANIU et al. [[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B23-sustainability-18-08596)] developed a drone transportation system specifically designed for small and medium-sized agricultural products. This system incorporated micro-winch assistance to achieve cost-minimized product delivery with reduced carbon emissions.\n\n### 2.3. Impact of Drones on Production Costs\n\nSupply chain and production costs primarily consist of expenses related to transportation, warehousing, and order processing. Research indicates that the implementation of drone technology represents an effective strategy for reducing these costs [[24](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B24-sustainability-18-08596)]. Ha et al. [[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B25-sustainability-18-08596)] demonstrated that enterprises could optimize supply chain management and logistics operations through drone deployment, noting that their battery-powered operation not only reduces carbon emission costs but also demonstrates superior operational efficiency compared to freight trucks. Regarding transportation costs, Wang et al. [[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B26-sustainability-18-08596)] established that drones possess both speed advantages and lower unit costs in delivery operations [[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B27-sustainability-18-08596)]. For instance, Shavarani et al. [[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B28-sustainability-18-08596)] estimated that aerial drone delivery systems could yield annual savings of up to $50 million. Dorling et al. [[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B29-sustainability-18-08596)] emphasized that drones incur lower maintenance costs than traditional transport vehicles (e.g., trucks) and their autonomous operation capability further reduces labor expenses. Chiang et al. [[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B30-sustainability-18-08596)] revealed that UAV-based last-mile delivery can reduce energy consumption, carbon emissions, and delivery costs, demonstrating both economic and environmental benefits [[31](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B31-sustainability-18-08596)]. Furthermore, drones enhance supply chain management and logistics efficiency by ensuring timely deliveries, thereby avoiding delay-related additional costs. Given that drone fleet management costs are significantly lower than those of ground transportation fleets, Yurek and Ozmutlu [[32](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8596#B32-sustainability-18-08596)] highlighted that global retail giants such as Amazon and Alibaba are actively investing in last-mile drone delivery systems to enable direct warehouse-to-customer shipments and reduce overall distribution costs.\n\n### 2.4. Contributions\n\nThe contributions in this paper are threefold:\n\nFirst, although existing studies have examined the application of drones in agricultural production and logistics, relatively limited attention has been paid to quantifying the scale of drone adoption in agriculture, which is a fundamental basis for understanding its role in sustainable agricultural development. This paper addresses this gap by estimating the application scale of agricultural drones and providing a quantitative foundation for evaluating their role in modern agricul","MDPI Sustainability 18(16):8596 2026年8月21日","2026-08-21T00:00:00Z","论文",10,false,82,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},20,22,18,13,9,1,"论文提供中国农用无人机采用的社会经济可持续性分析，含具体ROI数据，结论缓解劳动力短缺而非大规模失业，具有行业参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"农业无人机","成本分析","ROI","劳动力","MDPI Sustainability","宁波大学",[],{"status":35,"locked":14,"version":36,"method":37,"evidence":38},"needs_review","2026-10-22.2","rules",{"candidates":39,"agricultural_evidence":43,"reason":61},[40,48,54],{"code":41,"score":42,"supported":43,"title":44,"abstract":46,"tags":47},"A3",11,true,[45],"玉米",[45],[],{"code":49,"score":42,"supported":43,"title":50,"abstract":52,"tags":53},"B2",[51],"柑橘",[51],[],{"code":55,"score":56,"supported":43,"title":57,"abstract":58,"tags":60},"H8",3,[],[59],"可持续农业",[],"多个产业证据相当，需要确认主归属",[63,64],"农业无人机 宁波大学 成本分析 劳动力","农业无人机 宁波大学","农业无人机宁波大学成本分析劳动力-845","2026-08-22T00:06:00.979922Z",{"total":68,"page":22,"page_size":68,"items":69},6,[70,154,210,307,364,434],{"id":71,"title":72,"url":73,"summary":74,"summary_zh":75,"content":8,"source_name":76,"source_url":73,"published_at":77,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":78,"score_detail":79,"sources":84,"tags":86,"primary_topic":91,"secondary_topics":97,"classification":98,"search_phrases":115,"slug":118,"view_count":119,"doi":120,"paper":121,"created_at":153},5153,"Real-Time UAV scheduling under hazardous weather conditions using an integrated fuzzy logic and machine learning framework","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43926-026-00489-2","The use of Unmanned Aerial Vehicles (UAVs) in precision agriculture, specifically palm farm management, is perpetually challenged by dynamic and extreme weather. From sudden wind gusts and thermal stress to destructive regional events like sandstorms and flash floods, these hazards severely constrict operational windows. This paper proposes a robust, hybrid scheduling framework that integrates fuzzy logic inference with an ensemble of machine learning classifiers to enhance real-time UAV flight reliability. The primary novelty of this research lies not in a new mathematical paradigm, but in its specialized application: the holistic integration of region-specific agricultural hazards into a real-time, decision-fusion architecture. The framework classifies takeoff viability into three categories (SAFE, CAUTION, and UNSAFE) automatically issuing alerts to trigger preventative measures and preserve hardware. By leveraging both short-term farm datasets and a decade of historical weather records, the fuzzy logic component reacts to rapid environmental shifts while establishing safety baselines. Simultaneously, tree-based ensemble models (Gradient Boosting, XGBoost, LightGBM, and CatBoost) analyze non-linear atmospheric interactions, achieving up to 99.67% accuracy in predicting safe windows. Comprehensive sensitivity analyses, including evaluations without SMOTE and seasonal temporal holdout validations, confirm the model’s robustness against class imbalance and unseen extremes. Empirical benchmarking demonstrates the framework's superiority over existing hybrid approaches, highlighting the necessity of combining adaptive fuzzy reasoning with deep historical learning for smart farming.","无人机（UAV）在精准农业尤其是棕榈农场管理中的应用，始终受到动态极端天气的困扰。从突发阵风和热应激到沙尘暴和山洪等破坏性区域事件，这些灾害严重压缩了作业窗口。本文提出了一种稳健的混合调度框架，将模糊逻辑推理与机器学习分类器集成相结合，以提升无人机实时飞行的可靠性。本研究的主要创新点不在于新的数学范式，而在于其专门化应用：将区域特定的农业灾害整体集成到实时决策融合架构中。该框架将起飞可行性分为三类（安全、 caution、不安全），自动发出警报以触发预防措施并保护硬件。通过利用短期农场数据集和十年历史天气记录，模糊逻辑组件能够对快速环境变化作出反应，同时建立安全基线。与此同时，基于树的集成模型（梯度提升、XGBoost、LightGBM和CatBoost）分析非线性大气相互作用，在预测安全窗口方面达到高达99.67%的准确率。全面的敏感性分析，包括无SMOTE的评估和季节性时间留出验证，证实了模型对类别不平衡和未见极端情况的稳健性。实证基准测试表明，该框架优于现有的混合方法，凸显了将自适应模糊推理与深度历史学习相结合对于智慧农业的必要性。","Discover Internet of Things","2026-10-15T00:00:00Z",76,{"impact":80,"substance":81,"depth":19,"authority":20,"freshness":82,"relevant":22,"comment":83},16,21,8,"将模糊逻辑与集成学习融合用于极端天气下的农业无人机实时调度，方法扎实、验证充分，对智慧农业作业安全有实际参考价值。",[85],{"name":76,"url":73},[87,88,27,89,90],"智慧农业","农业人工智能","精准农业","气象预警",{"code":92,"slug":93,"name":94,"path":95,"parent_code":96},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":99,"locked":14,"version":36,"method":37,"evidence":100},"classified",{"candidates":101,"agricultural_evidence":43},[102,109],{"code":92,"score":103,"supported":43,"title":104,"abstract":106,"tags":108},15,[105],"machine learning",[107,105],"机器学习",[88],{"code":110,"score":42,"supported":43,"title":111,"abstract":112,"tags":114},"I1",[],[87,89,113],"precision agriculture",[87,89],[116,117],"无人机 精准农业 极端天气","UAV 棕榈园 调度","无人机精准农业极端天气-5153",0,"10.1007\u002Fs43926-026-00489-2",{"doi":120,"openalex_id":122,"authors":123,"venue":76,"cited_by_count":119,"oa_url":145,"card":146,"direction":150,"ingested_from":152},"W7166891579",[124,127,130,132,134,137,139,142],{"name":125,"orcid":126},"Ahmad B. Alkhodre","https:\u002F\u002Forcid.org\u002F0000-0001-6168-3552",{"name":128,"orcid":129},"Adnan Ahmed Abi Sen","https:\u002F\u002Forcid.org\u002F0000-0003-2960-5807",{"name":131,"orcid":8},"Abdelouahab Abid",{"name":133,"orcid":8},"Nour Mahmoud Bahbouh",{"name":135,"orcid":136},"Adel Ben Mnaouer","https:\u002F\u002Forcid.org\u002F0000-0003-3617-7636",{"name":138,"orcid":8},"Mohamed Benaida",{"name":140,"orcid":141},"Yazed Alsaawy","https:\u002F\u002Forcid.org\u002F0000-0001-5031-3388",{"name":143,"orcid":144},"Hani Almoamari","https:\u002F\u002Forcid.org\u002F0000-0002-3550-8977","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs43926-026-00489-2.pdf",{"tldr":147,"method":148,"finding":149,"direction":150,"opportunity":151},"提出融合模糊逻辑与机器学习分类器的无人机实时调度框架，在恶劣天气下保障精准农业飞行安全。","模糊逻辑推理结合梯度提升、XGBoost、LightGBM、CatBoost集成","框架将起飞可行性分为安全、警告、不安全三级，预测安全窗口准确率最高达99.67%，优于现有混合方法。","智慧农业 \u002F 农业物联网","可将区域特定灾害（沙尘暴、山洪）纳入实时决策融合，并扩展到多机协同调度与边缘端轻量化部署研究。","openalex","2026-10-17T23:30:08.889571Z",{"id":155,"title":156,"url":157,"summary":158,"summary_zh":8,"content":159,"source_name":160,"source_url":8,"published_at":161,"category":162,"cover_url":8,"hotness":13,"is_selected":14,"score":163,"score_detail":164,"sources":168,"tags":170,"primary_topic":174,"secondary_topics":180,"classification":181,"search_phrases":205,"slug":208,"view_count":22,"doi":8,"paper":8,"created_at":209},5031,"智慧棉田成就新疆新一代科技棉农","https:\u002F\u002Fwww.news.cn\u002Flocal\u002F20261015\u002F048734cea69b466eac74966f7b4d0074\u002Fc.html","金秋十月,天山南北棉花吐絮迎来采收。在塔克拉玛干沙漠北缘的尉犁县'超级棉田',采棉机来回穿梭,朵絮归仓。新疆是中国最大优质棉花生产基地,2025年新疆棉花产量占全国92.8%,总产、单产连续32年居全国第一。2021年极飞科技在尉犁县启动'超级棉田'项目,目前管理环节无人化作业率已达75%,栽培管理模式在全疆推广面积已超200万亩。今年更新的X150新型农业无人机可自动配药、加药、充电、规划航线,自主喷药作业。'90后'家庭农场主郭腾承包2.5万亩棉田,日常管护只需50名工人,返乡种棉成为越来越多年轻人的选择。","  新华社乌鲁木齐10月14日电（记者潘莹、宿传义、高晗）金秋十月，天山南北棉花吐絮，迎来采收。在塔克拉玛干沙漠北缘的尉犁县“超级棉田”，伴随着一台台采棉机来回穿梭，洁白的棉花被采摘、压缩、打包，朵絮归仓。\n\n  “前不久，田里用上一款新款农业无人机，不需要飞手操作，棉花采收环节更加智能化。”“超级棉田”项目负责人艾海鹏说。\n\n  新疆是中国最大优质棉花生产基地，2025年新疆棉花产量占全国产量的92.8%，总产、单产连续32年居全国第一。如今，在新疆，这样的智慧化场景正逐渐贯穿棉花生产全过程，并加速从示范走向普及，一批懂技术、善经营、会管理的“新一代棉农”应运而生。\n\n  在新疆巴音郭楞蒙古自治州尉犁县“超级棉田”中，农业无人机在田间喷洒脱叶剂（2026年9月20日摄，无人机照片）。新华社发（汪志鹏 摄）\n\n  尉犁县气候干燥、光热充足，是中国优质商品棉种植基地，棉花种植面积稳定在100多万亩。2021年，在尉犁县支持下，农业机器人企业极飞科技启动“超级棉田”项目，派出艾海鹏、凌磊两位“90后”管理经营3000亩棉田，探索可复制、可推广的无人化农业管理模式。\n\n  两人从解决棉花管理中最耗人工的灌溉难题入手，持续升级无人化、智能化监测管理的新技术、新设备，让“超级棉田”管理模式与大规模棉花种植高度适配，实现产量更高、品质更优。\n\n  “通过多年努力，‘超级棉田’已实现水、肥、药的智能化管理，目前管理环节无人化作业率已达75%。”艾海鹏说。截至2025年底，“超级棉田”的栽培管理模式在全疆推广面积已超200万亩。\n\n  今年，“超级棉田”又更新了一批新装备：耕种环节用上最新的APC4自驾仪，播种机可自动断膜断带，进一步节省人工。最让管理者津津乐道的，当属X150新型农业无人机。这款无人机可自动配药、加药、充电、规划航线、自主喷药作业，适配除草、防虫、防病等一系列田间需求。\n\n  在棉花吐絮关键期，记者在“超级棉田”作业现场看到，艾海鹏在手机上选好模板、点击“立即开始”，机器人便自主起降、补能，既不需要飞手，也不需要遥控器。手机屏上，药箱空满状态和地面药罐余量一目了然。\n\n  “往年打药虽然也用无人机，但还需要飞手控制轨迹、及时补药换电池。现在把飞机放这儿就不用管了。晚上回去睡觉，第二天早上过来，它已经按要求完成了全部植保任务。”艾海鹏说，目前这款新型农业无人机已在全国推广。\n\n  “我们的目的是想要吸引越来越多年轻人回到农业这一行业。一个行业有年轻人加入，才能有朝气，有更大的发展空间。”艾海鹏说。\n\n  在新疆巴音郭楞蒙古自治州尉犁县“超级棉田”中，采棉机在采收棉花（2026年10月14日摄，无人机照片）。新华社发\n\n  在天山北麓的昌吉市佃坝镇东沟村，“90后”家庭农场主郭腾正盯着屏幕，调度巨大的采棉机在棉海里缓缓行进，源源不断吐出打包好的棉包。\n\n  郭腾是土生土长的昌吉人，一家两代人的种棉经历，正是新疆棉花从“靠人力”到“靠科技”转型发展的鲜活缩影。\n\n  他说，父辈种棉是实打实的苦差事：棉田管护加采收要投入大量人工，采收成本高，种植户收入却很有限。\n\n  “如今农村可发展的空间大了。”郭腾介绍，自己毕业后在城里闯荡过一段时间，随着新疆棉田科技含量越来越高，看到新一代棉农正面临机遇无限的职业图景，决定回乡种棉花。\n\n  如今，郭腾的家庭农场已配齐整套自主农机：4台采棉机、6台植保无人机、十余台播种机、3台大马力拖拉机；棉田用上水肥一体化精准滴灌、无人机植保，犁地、播种、打药、采收，全流程机械化。\n\n  “一台采棉机一天能收400至450亩，每亩采收环节油费加人工成本仅60元。”郭腾说，他承包的2.5万亩棉田日常管护只需50名工人，自己在手机上就能查看田间情况、调度农机作业。\n\n  返乡种棉正成为越来越多年轻人的选择。郭腾发现，与他长期合作的团队里，返乡年轻人越来越多。“只要掌握技术，采棉季每月赚5万元不成问题。”他说。\n\n  随着越来越多科技装备、先进技术在新疆广袤棉田推广普及，新疆棉田加速智慧化，更多年轻人在天山南北的棉田里，找到创业舞台。","新华社 2026 年 10 月 15 日","2026-10-14T23:50:00Z","报道",84,{"impact":165,"substance":17,"depth":166,"authority":103,"freshness":82,"relevant":22,"comment":167},24,17,"央媒实地报道，以新疆超级棉田与新一代棉农为样本，呈现农业无人机自主作业与无人化管理模式从示范走向普及的产业级进展，数据与多方信源扎实，具主题聚合价值。",[169],{"name":160,"url":157},[87,27,171,172,173],"棉花","无人化农场","新疆棉田",{"code":175,"slug":176,"name":177,"path":178,"parent_code":179},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[],{"status":99,"locked":14,"version":36,"method":37,"evidence":182},{"candidates":183,"agricultural_evidence":43},[184,189,195,201],{"code":175,"score":185,"supported":43,"title":186,"abstract":187,"tags":188},4,[],[171],[171],{"code":190,"score":56,"supported":43,"title":191,"abstract":192,"tags":194},"A4",[],[193],"花生",[],{"code":196,"score":56,"supported":43,"title":197,"abstract":198,"tags":200},"G2",[],[199],"家庭农场",[],{"code":110,"score":22,"supported":14,"title":202,"abstract":203,"tags":204},[],[],[87],[206,207],"新疆 超级棉田 极飞科技","尉犁县 智慧棉田 无人机","新疆超级棉田极飞科技-5031","2026-10-16T00:13:02.993245Z",{"id":211,"title":212,"url":213,"summary":214,"summary_zh":215,"content":8,"source_name":216,"source_url":213,"published_at":217,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":218,"score_detail":219,"sources":222,"tags":224,"primary_topic":227,"secondary_topics":233,"classification":234,"search_phrases":289,"slug":292,"view_count":293,"doi":294,"paper":295,"created_at":306},4350,"Role of Science and Technology in Poverty Alleviation Through Smart Agriculture Process in Nigeria","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijcsmt.vol.11.no3.2025.pg.158.165","A smart Agriculture, is also known as precision agriculture which is rapidly growing field that involves the integration of technology and data analysis to optimize agriculture practice and increase efficiency in crop production in Nigeria to control the food shortage and scarcity. The smart agricultural it involves the use of sensor, drone satellite imagery and other technologies to monitor crops growth, soil condition, and weather partner. this information can be used to optimized irrigation, fertilization and pest control practice, ultimately leading to higher crop yield and reduce the waste of resource, more also smart agriculture involves the use of data analytic to analyze the collected data and identify pattern and correlations. This information can be used to informed decision about the planting, harvesting and storage. Automation is another key feature of smart agriculture, as it allows for more efficient and cost-effective farming practice, such as automated irrigation system and robotic harvesting equipment. Looking Nigeria in food shortage there are so many benefit of smart agricultural practice in a numerous way which include, increase efficiency, reduce waste and improve sustainability, it can also help to reduce the environmental impact of agriculture by reducing the use of pesticide and fertilizer, as well as reducing greenhouse gas emission through more efficiency farming practice. So in a nutshell smart agriculture present a high significant opportunity to improve the sustainability and productivity of agriculture. It can help farmers to make more inform decisions, reduce waste, and increase efficiency ultimately leading to a more sustainability and profitable agriculture sector and boost the economy growth of Nigeria.","智慧农业，又称精准农业，是一个快速发展的领域，涉及技术与数据分析的整合，以优化尼日利亚的农业实践并提高作物生产效率，从而控制粮食短缺和匮乏。智慧农业涉及利用传感器、无人机、卫星影像及其他技术来监测作物生长、土壤状况和天气模式。这些信息可用于优化灌溉、施肥和病虫害防治实践，最终实现更高的作物产量并减少资源浪费。此外，智慧农业还涉及利用数据分析来分析所收集的数据并识别模式和相关性。这些信息可用于为种植、收获和储存方面的决策提供依据。自动化是智慧农业的另一个关键特征，因为它能够实现更高效且更具成本效益的农业实践，例如自动化灌溉系统和机器人收割设备。鉴于尼日利亚面临粮食短缺，智慧农业实践在许多方面具有诸多益处，包括提高效率、减少浪费和改善可持续性，它还可以通过减少农药和化肥的使用来帮助降低农业对环境的影响，并通过更高效的农业实践减少温室气体排放。总而言之，智慧农业为改善农业的可持续性和生产力提供了重大机遇。它可以帮助农民做出更明智的决策、减少浪费并提高效率，最终形成更具可持续性和盈利性的农业部门，并促进尼日利亚的经济增长。","INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY E-ISSN","2026-10-05T00:00:00Z",31,{"impact":82,"substance":68,"depth":220,"authority":185,"freshness":82,"relevant":22,"comment":221},5,"主题相关但为泛泛综述，缺乏新数据与方法创新，信源权威性低，不宜进入每日精选。",[223],{"name":216,"url":213},[87,225,27,89,226],"农业遥感","尼日利亚农业",{"code":228,"slug":229,"name":230,"path":231,"parent_code":232},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[],{"status":99,"locked":14,"version":36,"method":37,"evidence":235},{"candidates":236,"agricultural_evidence":43},[237,243,250,257,264,271,278,284],{"code":110,"score":238,"supported":43,"title":239,"abstract":241,"tags":242},19,[240],"smart agriculture",[87,89,240,113],[87,89],{"code":228,"score":21,"supported":43,"title":244,"abstract":245,"tags":249},[],[246,247,248],"施肥","fertilizer","fertilization",[],{"code":251,"score":68,"supported":43,"title":252,"abstract":253,"tags":256},"B7",[],[254,255],"温室","greenhouse",[],{"code":258,"score":68,"supported":43,"title":259,"abstract":260,"tags":263},"F4",[],[261,262],"农药","pesticide",[],{"code":265,"score":68,"supported":43,"title":266,"abstract":267,"tags":270},"H2",[],[268,269],"土壤","soil",[],{"code":272,"score":68,"supported":43,"title":273,"abstract":274,"tags":277},"H3",[],[275,276],"灌溉","irrigation",[],{"code":279,"score":56,"supported":43,"title":280,"abstract":281,"tags":283},"F6",[],[282],"robotic harvesting",[],{"code":285,"score":22,"supported":14,"title":286,"abstract":287,"tags":288},"I3",[],[],[225],[290,291],"尼日利亚 智慧农业 减贫","尼日利亚农业 农业无人机 农业遥感 智慧农业","尼日利亚智慧农业减贫-4350",2,"10.56201\u002Fijcsmt.vol.11.no3.2025.pg.158.165",{"doi":294,"openalex_id":296,"authors":297,"venue":216,"cited_by_count":119,"oa_url":300,"card":301,"direction":150,"ingested_from":152},"W7220743666",[298],{"name":299,"orcid":8},"Aminu Ahmed","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJCSMT\u002FVOL. 11 NO. 3 2025\u002FRole of Science and Technology 158-165.pdf",{"tldr":302,"method":303,"finding":304,"direction":150,"opportunity":305},"综述智慧农业技术如何通过精准农业助力尼日利亚减贫与粮食安全。","文献综述，整合传感器、无人机、卫星影像、数据分析与自动化技术。","智慧农业可提高效率、减少浪费、增强可持续性并促进经济增长。","可针对尼日利亚小农户开展低成本传感器与无人机应用的本土化实证研究。","2026-10-07T23:30:12.855407Z",{"id":308,"title":309,"url":310,"summary":311,"summary_zh":8,"content":312,"source_name":313,"source_url":8,"published_at":314,"category":162,"cover_url":8,"hotness":13,"is_selected":14,"score":315,"score_detail":316,"sources":319,"tags":321,"primary_topic":325,"secondary_topics":330,"classification":331,"search_phrases":359,"slug":362,"view_count":22,"doi":8,"paper":8,"created_at":363},4274,"外媒记者在黑土地上见证农业智变","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7692034460295954980\u002F","9月27-30日国务院新闻办公室组织外媒看中国黑龙江行采访活动，来自美国白俄罗斯哈萨克斯坦古巴新加坡等国近30名中外记者走进哈尔滨佳木斯等地。在佳木斯骥驰拖拉机制造有限公司，300马力油电混动拖拉机搭载北斗终端辅助驾驶系统。北大荒建三江分公司万亩大地号示范区1.5万亩连片稻田实现无人驾驶收割机自主作业。","秋日的三江平原，稻浪翻涌。站在万亩稻田边，来自不同国家的记者举起相机，记录眼前这片黑土地上的金色丰收：远处，收割机沿着田垄缓缓驶过，成熟的稻穗被收入机舱。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002F17c7349cce25484e90fe73ec98301c7a~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791850017&x-signature=a%2BLTKhH97uD3y%2FEDFKLgmJPrUQQ%3D)\n\n 外媒记者在北大荒集团建三江分公司田间拍摄秋收场景。新华社记者张曦月 摄\n\n这里是中国重要的商品粮基地，也是黑龙江现代化大农业发展的缩影。\n\n9月27日至30日，国务院新闻办公室组织“外媒看中国·黑龙江行”采访活动，来自美国、白俄罗斯、哈萨克斯坦、古巴、新加坡等国家和地区的近30名中外记者，走进哈尔滨、佳木斯等地，来到农业企业、科技园区和田间地头，探访中国粮食主产区如何以科技赋能农业发展。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F0e8f62b9e35206f37f547e30d165acaa~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791850017&x-signature=hwPGfvdFGcEUrdOXQCYYjJ73X7I%3D)\n\n 外媒记者在北大荒集团建三江分公司采访智慧农业专家。新华社记者张曦月 摄\n\n看智能装备，田间上演“科技革命”\n\n在佳木斯骥驰拖拉机制造有限公司的生产车间，一台台大型拖拉机排列整齐。当听说眼前的300马力油电混动拖拉机搭载北斗终端辅助驾驶系统，不仅能快速判断产品故障，还可以拓展无人驾驶技术应用时，记者们纷纷围上前仔细查看。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fa3dbcb882d091533a608effc8300583f~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791850017&x-signature=thDzlfAGdMlMFychd8MK0gBuNZI%3D)\n\n 外媒记者在佳木斯骥驰拖拉机制造有限公司体验智能农机。新华社记者张曦月 摄\n\n美国记者史砚华坐进驾驶室体验操作，感受智能农机带来的变化。哈萨克斯坦通讯社记者贝利说，这类设备让他感到新奇，由于哈萨克斯坦部分地区农业生产环境与黑龙江相似，这些技术具有应用价值。拉丁美洲通讯社记者迭斯则感叹：“如果这样的设备能够应用到古巴的农业生产中，将给农民带来新的帮助。”\n\n在黑龙江惠达科技股份有限公司，一架农业无人机停在展厅中央，很快吸引了外媒记者的注意。有人拿起手机拍摄，有人围着设备询问用途，还有人关心这些技术是否能够应用到自己的国家。\n\n![Image 4](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F39a8eadc855662861165fc3d7e7a657d~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791850017&x-signature=ceLBqePAvbseYNF%2FC2pNberBxkY%3D)\n\n 外媒记者在黑龙江惠达科技股份有限公司参观农业无人机等智能装备。新华社记者张曦月 摄\n\n看智慧农业，黑土地插上“数字翅膀”\n\n从制造车间走向广袤田野，科技带来的变化更加直观。\n\n在北大荒集团建三江分公司“万亩大地号”示范区，站在观景台俯瞰，1.5万亩连片稻田一望无际。无人驾驶收割机沿着设定路线匀速作业，植保无人机低空巡航，不远处数字农业指挥中心的大屏上，农田墒情、农机轨迹、作业进度实时更新。\n\n![Image 5](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F83b5870477488e02b165ba93614bfc80~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791850017&x-signature=8mocmG3DJQZpzVPMP%2BlDeq%2Fz4sk%3D)\n\n 外媒记者在北大荒集团建三江分公司拍摄智慧农业作业场景。新华社记者张曦月 摄\n\n“如今，农民可以通过手机查看田间情况、管理设备，然后等待收获。”新加坡《海峡时报》记者游润恬说，科技不仅改变了农业生产方式，也让农民拥有更加从容的生活。\n\n白俄罗斯国家广播电视公司驻华代表处主任雅罗斯拉夫·达尼连科关注人工智能技术在农业中的应用。他说，在这里亲眼看到无人驾驶收割机自主作业，更确信这些技术未来将在更多国家应用，这是全球农业发展的共同机遇。\n\n![Image 6](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F0d68cd80a41a2710bec08c08646c28be~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791850017&x-signature=6t2mtpWDdYIVqV6ssP0G2NLJ9qo%3D)\n\n 外媒记者在北大荒集团建三江分公司参观农业科技温室，了解现代设施农业栽培技术。新华社记者张曦月 摄\n\n看产业融合，黑土地长出“幸福经济”\n\n在黑龙江省和粮农业有限公司，外媒记者感受着一粒粮食从田间走向市场的全过程。和粮农业集团董事长、创始人张芳介绍，通过种植、加工、销售等环节延伸产业链，黑土地上的粮食不仅“种得好”，也能够“卖得好”。“黑土优品”品牌建设让更多优质农产品拥有更高辨识度，推动农业资源优势转化为产业优势。\n\n![Image 7](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002F3bfef4f5bd2f5a8d8047346b47023371~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791850017&x-signature=ocJ7ZsVQma41sg0L86XV7ZhaIp8%3D)\n\n 外媒记者在黑龙江省和粮农业有限公司展示特色农产品，了解黑土地优质农产品品牌建设和产业化发展情况。新华社记者张曦月 摄\n\n来到富锦市万亩水稻科技示范园，外媒记者见证了农业与文化、旅游融合的新场景。一幅总面积达12万平方米的巨型3D稻田画铺展在大地之上。每年插秧季，农技团队通过电脑完成图案设计，再利用GPS精准定位，种上不同颜色的景观稻，最终形成“大地为纸、秧苗为墨”的田间艺术。\n\n迭斯站在稻田画前，通过镜头向本国观众介绍这片来自中国东北的农业景象。\n\n![Image 8](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-tjoges91tu\u002Fcf180f46afc98f3f5bca4d96a562aa00~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791850017&x-signature=0po78%2FjNEZZSmI4uFBvtKy6tAR8%3D)\n\n 拉丁美洲通讯社记者在富锦市万亩水稻科技示范园录制视频。新华社记者张曦月 摄\n\n采访结束前，一些外媒记者还体验了“我在龙江有亩田”定制农业模式，通过手机查看一块稻田从播种、管护到收获的全过程，等待来自黑土地的丰收。\n\n“看到农民的笑脸，再看到这些高科技机械，能真切感受到黑土地的日子蒸蒸日上，这里的农民很幸福。”游润恬说。（记者张曦月）","今日头条 新华社 2026-10-02","2026-10-02T11:42:00Z",65,{"impact":19,"substance":317,"depth":317,"authority":317,"freshness":220,"relevant":22,"comment":318},14,"央媒组织的多国外媒采访活动，以第三方视角呈现黑龙江智慧农业与智能农机应用，信源权威、内容具体，但属综合报道且已过4天，时效性一般。",[320],{"name":313,"url":310},[87,322,27,323,324],"智能农机","黑土地保护","定制农业",{"code":326,"slug":327,"name":328,"path":329,"parent_code":232},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery",[],{"status":99,"locked":14,"version":36,"method":37,"evidence":332},{"candidates":333,"agricultural_evidence":43},[334,339,345,350,355],{"code":326,"score":185,"supported":43,"title":335,"abstract":336,"tags":338},[],[337],"收割机",[322],{"code":340,"score":56,"supported":43,"title":341,"abstract":342,"tags":344},"A1",[],[343],"稻田",[],{"code":346,"score":22,"supported":14,"title":347,"abstract":348,"tags":349},"E5",[],[],[324],{"code":351,"score":22,"supported":14,"title":352,"abstract":353,"tags":354},"H1",[],[],[323],{"code":110,"score":22,"supported":14,"title":356,"abstract":357,"tags":358},[],[],[87],[360,361],"北大荒 建三江 智慧农业","佳木斯骥驰 智能拖拉机","北大荒建三江智慧农业-4274","2026-10-06T00:05:02.912242Z",{"id":365,"title":366,"url":367,"summary":368,"summary_zh":369,"content":8,"source_name":370,"source_url":367,"published_at":371,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":372,"score_detail":373,"sources":376,"tags":378,"primary_topic":380,"secondary_topics":385,"classification":386,"search_phrases":417,"slug":420,"view_count":293,"doi":8,"paper":421,"created_at":433},4072,"Безпілотні літальні апарати та Інтернет речей у сільському господарстві: систематичний огляд технологічних напрямів і перспектив розвитку","https:\u002F\u002Fais.khpi.edu.ua\u002Farticle\u002Fview\u002F372716","Relevance. Modern agriculture is undergoing a profound cyber-physical transformation driven by the need to shift from extensive to precision, high-accuracy, and environmentally balanced production methods. The technological foundation of this transition is the integration of unmanned aerial vehicles (UAVs) and wireless Internet of Things (IoT) sensor networks, which together form the concept of Agriculture 5.0. Despite the rapid growth of publications on this topic, the accumulated body of research remains fragmented across directions (computer vision, network technologies, robotics, adoption economics), which complicates the formation of a holistic picture of the field and of priority directions for further research. The subject of this study are methods and technologies of applying UAVs and IoT networks to precision-agriculture tasks. The purpose of the article to systematize and classify current scientific research on the use of UAVs and IoT in agriculture, and to determine the leading technological directions, their quantitative proportions, and promising development vectors. The following results were obtained. A total of 198 scientific publications from 2024-2026 were processed and classified into seven thematic classes: crop health and biological threat monitoring, hardware engineering, edge computing and spraying, navigation, swarm intelligence and data collection, phenotyping, biomass and yield forecasting, soil science, water balance and precision irrigation, economics, ecology and socio-cultural perception, and specialized agrotechnical operations. For each class, consolidated tables of applied methods and results, as well as diagrams revealing the internal structure of the direction, were constructed. The dominance of navigation\u002Fswarm-control tasks and biological-threat monitoring was identified, along with key technical barriers (limited battery energy density, beyond-visual-line-of-sight flight regulations, and fragmentation of data-exchange standards). Conclusion. The obtained results provide a holistic assessment of the state and structure of the research field on UAV and IoT applications in agriculture and can serve as a basis for shaping further research programs and educational courses in precision agriculture. Future research directions concern improving the energy autonomy of platforms, standardizing data-exchange protocols, and developing edge artificial-intelligence models resilient to complex field conditions.","相关性。现代农业正在经历深刻的网络物理转型，其驱动力来自于从粗放型向精准、高精度和环境均衡型生产方式转变的需求。这一转型的技术基础是无人机（UAV）与无线物联网（IoT）传感器网络的集成，二者共同构成了农业5.0的概念。尽管该主题的出版物迅速增长，但积累的研究成果分散在多个方向（计算机视觉、网络技术、机器人技术、采用经济学），这使得形成该领域的整体图景及未来优先研究方向变得困难。本研究的研究对象是将无人机和物联网网络应用于精准农业任务的方法与技术。本文旨在系统化和分类当前关于无人机和物联网在农业中应用的科学研 究，并确定主导技术方向、其定量比例及有前景的发展路径。获得了以下结果。共处理了2024—2026年的198篇科学出版物，并将其分为七个主题类别：作物健康与生物威胁监测、硬件工程、边缘计算与喷洒、导航、群体智能与数据采集、表型分析、生物量与产量预测、土壤科学、水分平衡与精准灌溉、经济学、生态学与社会文化感知，以及专业农艺操作。针对每个类别，构建了应用方法与结果的汇总表，以及揭示该方向内部结构的图表。识别出导航\u002F群体控制任务和生物威胁监测的主导地位，以及关键技术障碍（电池能量密度有限、超视距飞行法规、数据交换标准的碎片化）。结论。所获得的结果对无人机和物联网在农业中应用的研究领域现状与结构提供了整体评估，可作为制定精准农业后续研究计划和教学课程的基础。未来研究方向涉及提高平台的能量自主性、标准化数据交换协议，以及开发对复杂田间条件具有鲁棒性的边缘人工智能模型。","The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy","2026-10-02T00:00:00Z",78,{"impact":19,"substance":18,"depth":19,"authority":374,"freshness":82,"relevant":22,"comment":375},12,"系统梳理2024-2026年198篇无人机与物联网农业应用文献，分类清晰、结论可靠，对智慧农业研究方向有实质参考价值。",[377],{"name":370,"url":367},[87,88,379,27,89],"农业物联网",{"code":381,"slug":382,"name":383,"path":384,"parent_code":96},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[],{"status":99,"locked":14,"version":36,"method":37,"evidence":387},{"candidates":388,"agricultural_evidence":43},[389,398,402,409,413],{"code":381,"score":13,"supported":43,"title":390,"abstract":391,"tags":397},[],[392,393,394,395,396],"物联网","边缘计算","internet of things","IoT","edge computing",[379],{"code":110,"score":82,"supported":43,"title":399,"abstract":400,"tags":401},[],[89,113],[87,89],{"code":92,"score":403,"supported":43,"title":404,"abstract":405,"tags":408},7,[],[406,407],"计算机视觉","computer vision",[88],{"code":265,"score":68,"supported":43,"title":410,"abstract":411,"tags":412},[],[268,269],[],{"code":272,"score":68,"supported":43,"title":414,"abstract":415,"tags":416},[],[275,276],[],[418,419],"农业人工智能 农业无人机 农业物联网 智慧农业","农业人工智能 农业无人机","农业人工智能农业无人机农业物联网智慧农业-4072",{"doi":8,"openalex_id":422,"authors":423,"venue":370,"cited_by_count":119,"oa_url":367,"card":428,"direction":150,"ingested_from":152},"W7218223913",[424,426],{"name":425,"orcid":8},"Володимир Панченко",{"name":427,"orcid":8},"Георгій Кучук",{"tldr":429,"method":430,"finding":431,"direction":150,"opportunity":432},"系统综述2024-2026年198篇无人机与物联网农业应用文献，划分七类技术方向并指出发展瓶颈。","文献计量与主题分类，分析198篇论文的方法、结果与内部结构。","导航\u002F集群控制与生物威胁监测占主导，瓶颈为电池能量密度、超视距法规与数据标准碎片化。","可聚焦边缘AI模型在复杂田间条件下的鲁棒性、平台能量自治与跨厂商数据交换协议标准化研究。","2026-10-03T23:30:16.357874Z",{"id":435,"title":436,"url":437,"summary":368,"summary_zh":438,"content":8,"source_name":439,"source_url":437,"published_at":371,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":372,"score_detail":440,"sources":442,"tags":444,"primary_topic":445,"secondary_topics":446,"classification":447,"search_phrases":470,"slug":471,"view_count":22,"doi":472,"paper":473,"created_at":484},4071,"UNMANNED AERIAL VEHICLES AND THE INTERNET OF THINGS IN AGRICULTURE: A SYSTEMATIC REVIEW OF TECHNOLOGICAL DIRECTIONS AND DEVELOPMENT PROSPECTS","https:\u002F\u002Fdoi.org\u002F10.20998\u002F2522-9052.2026.4.06","相关性。现代农业正在经历深刻的网络物理转型，其驱动力来自从粗放型生产向精准、高精度和环境均衡型生产方式的转变需求。这一转型的技术基础是无人机（UAV）与无线物联网（IoT）传感器网络的集成，二者共同构成了农业5.0的概念。尽管该主题的出版物迅速增长，但积累的研究成果分散在多个方向（计算机视觉、网络技术、机器人技术、采纳经济学），这使得形成对该领域整体图景及未来优先研究方向的把握变得困难。本研究的研究对象是将无人机和物联网网络应用于精准农业任务的方法与技术。本文旨在系统化梳理和分类当前关于无人机与物联网在农业中应用的科学研 究，并确定主导技术方向、其数量比例及有前景的发展路径。获得以下结果。共处理了2024—2026年的198篇科学出版物，并将其分为七个主题类别：作物健康与生物威胁监测；硬件工程；边缘计算与喷洒；导航、群体智能与数据采集；表型分析、生物量与产量预测；土壤科学、水分平衡与精准灌溉；经济、生态与社会文化感知；以及专业农艺操作。针对每个类别，构建了应用方法与结果的汇总表，以及揭示该方向内部结构的图表。识别出导航\u002F群体控制任务与生物威胁监测的主导地位，以及关键技术瓶颈（电池能量密度有限、超视距飞行法规、数据交换标准碎片化）。结论。所获结果对无人机与物联网在农业中应用研究领域的现状与结构提供了整体评估，可作为制定精准农业后续研究计划和教育课程的基础。未来研究方向涉及提升平台能源自主性、标准化数据交换协议，以及开发对复杂田间条件具有鲁棒性的边缘人工智能模型。","Advanced Information Systems",{"impact":19,"substance":18,"depth":19,"authority":374,"freshness":82,"relevant":22,"comment":441},"系统综述198篇2024-2026年文献，梳理无人机与物联网在农业中的七大技术方向及瓶颈，方法学扎实、信息增量高，对智慧农业研究布局有参考价值。",[443],{"name":439,"url":437},[87,88,379,27,89],{"code":381,"slug":382,"name":383,"path":384,"parent_code":96},[],{"status":99,"locked":14,"version":36,"method":37,"evidence":448},{"candidates":449,"agricultural_evidence":43},[450,454,458,462,466],{"code":381,"score":19,"supported":43,"title":451,"abstract":452,"tags":453},[394],[392,393,394,395,396],[379],{"code":110,"score":82,"supported":43,"title":455,"abstract":456,"tags":457},[],[89,113],[87,89],{"code":92,"score":403,"supported":43,"title":459,"abstract":460,"tags":461},[],[406,407],[88],{"code":265,"score":68,"supported":43,"title":463,"abstract":464,"tags":465},[],[268,269],[],{"code":272,"score":68,"supported":43,"title":467,"abstract":468,"tags":469},[],[275,276],[],[418,419],"农业人工智能农业无人机农业物联网智慧农业-4071","10.20998\u002F2522-9052.2026.4.06",{"doi":472,"openalex_id":474,"authors":475,"venue":439,"cited_by_count":119,"oa_url":437,"card":479,"direction":150,"ingested_from":152},"W7216591198",[476,478],{"name":477,"orcid":8},"Volodymyr 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