[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-ROI":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":7,"page_size":8,"items":9},"ROI","tag",[4],1,100,[10],{"id":11,"title":12,"url":13,"summary":14,"summary_zh":15,"content":16,"source_name":17,"source_url":15,"published_at":18,"category":19,"cover_url":15,"hotness":20,"is_selected":21,"score":22,"score_detail":23,"sources":30,"tags":32,"primary_topic":15,"secondary_topics":38,"classification":39,"search_phrases":67,"slug":70,"view_count":61,"doi":15,"paper":15,"created_at":71},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":24,"substance":25,"depth":26,"authority":27,"freshness":28,"relevant":7,"comment":29},20,22,18,13,9,"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。",[31],{"name":17,"url":13},[33,34,4,35,36,37],"农业无人机","成本分析","劳动力","MDPI Sustainability","宁波大学",[],{"status":40,"locked":21,"version":41,"method":42,"evidence":43},"needs_review","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":48,"reason":66},[45,53,59],{"code":46,"score":47,"supported":48,"title":49,"abstract":51,"tags":52},"A3",11,true,[50],"玉米",[50],[],{"code":54,"score":47,"supported":48,"title":55,"abstract":57,"tags":58},"B2",[56],"柑橘",[56],[],{"code":60,"score":61,"supported":48,"title":62,"abstract":63,"tags":65},"H8",3,[],[64],"可持续农业",[],"多个产业证据相当，需要确认主归属",[68,69],"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"]