[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-742":3,"related-742":72},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":67,"slug":70,"view_count":20,"doi":8,"paper":8,"created_at":71},742,"卫星—无人机两层组网葡萄园巡检与NDVI自适应路径规划(原标题:Satellite-Enabled Two-Tier UAV Vineyard Inspection with NDVI-Adaptive Path Planning)","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753","提出高空多光谱卫星+低空RGB无人机两层协同葡萄园巡检方案,基于NDVI兴趣点识别引导低空病检任务;引入LEO卫星通信模拟,带宽受限下路由优化可减少任务时间约15%,为丘陵果园精准植保提供端到端解决方案。",null,"## 1. Introduction\n\nViticulture is a high-value agricultural sector with both economic and cultural significance in many regions. Vineyards are exposed to a range of pressures, including disease, water stress, and climatic variability. Efficient monitoring is essential for informing timely interventions and management decisions, while keeping labour costs and effort low. Automated and data-driven monitoring approaches offer an increasingly practical path to vineyard monitoring, combining sensing, communication, and analytics for decision support. Relevant technologies include satellite imagery and communications, drone mapping, multispectral vegetation indices, and computer vision.\n\nSatellite technologies can support crop monitoring both directly, through Earth observation, and indirectly, through communication. Satellite imagery can provide repeated, wide-area observations of vineyard condition and has been used to support precision viticulture by mapping vegetation vigour, water status, spatial variability, and other relevant indicators for improved sustainability and operational efficiency [[1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B1-agriculture-16-01753)]. As data-driven approaches gain traction in agriculture, satellite communication is also becoming increasingly relevant, especially for remote regions where terrestrial connectivity may be unreliable or unavailable [[2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B2-agriculture-16-01753)].\n\nDrones provide a flexible and high-resolution sensing platform for precision agriculture, complementing the wider coverage of satellite imagery with detailed observations for crop mapping, plant-health assessment, irrigation support, yield estimation, spraying, and other monitoring and management tasks [[3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B3-agriculture-16-01753)]. Drone-based multispectral mapping can provide vegetation indices, such as NDVI, which are widely used to quantify vegetation condition, growth, and stress [[4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B4-agriculture-16-01753)]. Previous vineyard-focused research has examined such data for specific applications including water-status variability mapping and disease progression monitoring [[5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B5-agriculture-16-01753),[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B6-agriculture-16-01753)]. Nevertheless, UAV monitoring involves an inherent trade-off between coverage and detail: high-altitude flights can map larger areas efficiently, while low-altitude inspection can provide more detailed visual information but requires additional flight time and produces larger data volumes. At a broader monitoring scale, drone imagery is therefore often considered alongside satellite imagery, combining wide-area observation with finer spatial detail [[7](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B7-agriculture-16-01753)].\n\nComputer vision and machine learning further expand the role of image-based crop monitoring by enabling automated interpretation of RGB, multispectral, and other data. In agriculture, these methods have been applied to a broad range of tasks, including plant and canopy segmentation, stress detection, disease identification, and yield estimation. For disease monitoring specifically, recent reviews highlight the growing use of UAV imagery, deep learning, and remote or proximal sensing tools for early detection and field-scale assessment [[8](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B8-agriculture-16-01753),[9](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B9-agriculture-16-01753),[10](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B10-agriculture-16-01753)]. In grapevines, recent work includes both leaf-level disease classification using convolutional neural networks and field-level disease monitoring using UAV multispectral data [[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B6-agriculture-16-01753),[11](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B11-agriculture-16-01753)].\n\nBuilding on these developments, this work presents a satellite-enabled, two-tier UAV workflow for semi-automated vineyard inspection. The proposed approach combines high-altitude multispectral scanning, identification of points of interest from vegetation index analysis, live flight path adaptation, low-altitude RGB inspection, and vision-based disease detection, classification, and severity estimation. Processing tasks are offloaded to a remote server reached through an emulated satellite communication environment, allowing the UAV-side system to remain lightweight while receiving multispectral analysis results during the mission. These results are used to adapt the flight path and guide selective low-altitude inspection of areas that may indicate plant stress or disease.\n\nUnlike existing approaches that typically address multispectral crop monitoring, close-range disease inspection, communication constraints, or UAV path planning separately, this work integrates these elements into a single adaptive inspection workflow. The main contributions of this paper are as follows:\n\n*   A two-tier UAV inspection architecture combining high-altitude multispectral sensing, NDVI-based smart sampling, low-altitude RGB inspection, and remote image processing.\n\n*   On-the-fly path adaptation for inserting newly identified waypoints into an ongoing UAV mission, together with a comparative evaluation of alternative insertion and route-optimisation strategies.\n\n*   An experimental analysis of how satellite-link bandwidth affects image-transfer latency, waypoint availability, and total mission duration during adaptive inspection.\n\nThe experimental evaluation in [Section 3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#sec3-agriculture-16-01753) focuses on the effect of different path-adaptation strategies and communication bottlenecks on mission efficiency. A detailed evaluation of point-of-interest identification as a smart sampling mechanism for low-altitude image acquisition is beyond the scope of this work and will be addressed in future research. The disease-detection component is included as a downstream analysis module based primarily on established computer vision approaches, and is therefore not benchmarked as a separate contribution in this paper.\n\n## 2. Materials and Methods\n\n### 2.1. System Architecture\n\nThe overall system architecture and information flow are shown in [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#fig_body_display_agriculture-16-01753-f001). The drone captures the images from the vineyard and sends them to the remote server for processing. The system incorporates high-altitude multispectral scanning, dynamic mission adaptation, and computer vision-based disease assessment. It is deployed as two interacting parts:\n\n**Figure 1.** System Architecture and data flow.\n\n1.\nA locally deployed application that interfaces with—and controls—the drone: For missions with a real drone, this is an Android app leveraging an SDK to communicate with the drone and deployed on the drone remote controller; for the tests presented in this work, a simulator was used instead, to facilitate repeat testing, as described in [Section 2.6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#sec2dot6-agriculture-16-01753).\n\n2.\nA remote server that handles image processing and computer vision tasks: In this work, a server hosted in a VM within the satellite emulation testbed presented in [Section 2.2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#sec2dot2-agriculture-16-01753) was used.\n\nFurthermore, the system comprises three main software modules, each with a distinct input and output, as shown in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#table_body_display_agriculture-16-01753-t001).\n\n**Table 1.** System software modules.\n\nThe flow of information between the modules, during the course of a mission, is as follows:\n\nInitially, a mission is created according to user input, comprising only high-altitude waypoints, placed evenly on a grid so as to cover the whole vineyard. At each high-altitude waypoint, the drone captures a multispectral image. The local application sends the pair of Red and NIR bands to the remote server. There, the POI identification module ([Section 2.3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#sec2dot3-agriculture-16-01753)) receives and processes them, and sends back the coordinates of any POIs identified. The Path adaptation module ([Section 2.4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#sec2dot4-agriculture-16-01753)) inserts these POIs into the existing path, adapting and optimizing it to minimise its total length. When a POI is visited, the drone captures a close-up RGB photo of the grapevine. All RGB close-ups are sent to the remote server after the mission for Disease detection ([Section 2.5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#sec2dot5-agriculture-16-01753)).\n\nThe satellite constellation is transparent and serves the purpose of providing connectivity between the drone and the ground node hosting the remote server.\n\n### 2.2. Satellite Emulation Testbed\n\nThe satellite emulation testbed is capable of emulating a configurable number of satellites in an orbital plane. In this work, it is used to reproduce the behaviour of a Low Earth Orbit (LEO) satellite constellation with Inter-Satellite Links (ISLs), providing the communication environment between the drone-side system and the remote processing server.\n\nThe testbed was developed by DLR for research purposes using Mininet [[12](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B12-agriculture-16-01753)], a network emulator that can create networks of virtual hosts, switches, controllers, and links. In contrast to purely analytical or discrete-event simulators, Mininet networks run real code, including standard Unix\u002FLinux network applications and the Linux kernel network stack. As a result, software developed and tested in Mininet can be transferred to real systems with minimal changes, making it suitable for network virtualisation, performance evaluation, and deployment-oriented testing.\n\nIn the emulated satellite network, ground nodes connect to the nearest visible satellite. Depending on the constellation configuration, communication between two ground nodes may traverse more than one satellite, making ISLs important for end-to-end connectivity. The performance of the communication path depends on the links between ground nodes and satellites, as well as on the ISLs between satellites. For this reason, the testbed supports multiple configurable parameters, including the following:\n\n*   The height of the orbital plane, which affects both the delay between a ground node and the nearest satellite and the duration of satellite visibility with respect to the ground node;\n\n*   The number of satellites between the source and destination ground nodes;\n\n*   Ground-to-satellite link parameters, such as bandwidth and packet loss rate;\n\n*   ISL parameters, including delay, bandwidth, and packet loss rate.\n\nAlthough the testbed can support two orbital planes, only one orbital plane was considered in this work for simplicity. The testbed can also emulate orbits other than LEO, provided that the relevant parameters are selected carefully to avoid unrealistic scenarios. Communication between nodes is based on Internet Protocol (IP) routing, and both Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) are supported.\n\nThe experimental setup is shown in [Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#fig_body_display_agriculture-16-01753-f002). The emulated satellite network runs inside a Virtual Machine (VM) on Ubuntu 22.04, which is accessed through a Virtual Private Network (VPN) connection. The drone-side system connects to this VPN and sends data to the VM. Upon receiving the data packets, the VM forwards them to the satellite emulation framework. Packets are then forwarded through the emulated satellite path, from the nearest satellite toward the gateway or destination ground node. In this work, end-to-end communication was carried out using TCP, and four satellites were modelled along the path between the drone and the ground node. The ground node hosts the Docker container running the remote processing service. After the images are processed, the results are returned through the same emulated communication path in the reverse direction.\n\n**Figure 2.** Experimental testbed configuration.\n\nSatellite emulation is considering a Starlink-like constellation. Hence, the ground nodes have continuous connectivity since at least one satellite is visible. This ensures that the ground nodes can always communicate without the need of a storage mechanism on the ground nodes. Considering that TCP is employed in the transport layer it is essential that the end-to-end connectivity is always present. Otherwise, the docker present on the other end of the constellation will not be readily reachable, which can adversely affect the overall performance.\n\nThe links between satellites and ground nodes, namely the drone and gateway, are modelled based on the orbital height of the satellite constellation and the relative movement between satellites and ground nodes. Link delay is highest when the satellite appears at the horizon of the ground node. It then decreases as the satellite moves toward the point above the ground node, before increasing again until the satellite is no longer visible. Satellite visibility duration also depends on orbital height, with higher orbital planes resulting in longer visibility duration. In the scenario considered in this work, the orbital height was set to 1221 km.\n\n### 2.3. POI Identification\n\n#### 2.3.1. POI Identification Rationale and Objective\n\nScanning an area, or a vineyard in the present case, is a trade-off between flight altitude and effective ground resolution. The ground area captured by a single photo (also known as the footprint) depends on the altitude of flight, as well as the camera’s field of view (FOV). For simplicity and uniformity, in this paper we are considering only photos taken at nadir orientation (i.e., the camera is pointing straight down).\n\nFor a camera with\n\n$F O V_{H} \\times F O V_{V}$\nfield of view and the drone flying at altitude A, the footprint is a rectangle of dimensions\n\n$X \\times Y$\n:\n\n$$\nX & = 2 A tan \\left(\\right. F O V_{H} \u002F 2 \\left.\\right) \\\\ Y & = 2 A tan \\left(\\right. F O V_{V} \u002F 2 \\left.\\right)\n$$\n\n(1)\n\nThe ground sampling distance (GSD) describes the distance between adjacent pixels and is equal to the footprint size divided by the camera resolution. For a camera with a\n\n$W \\times H$\nresolution:\n\n$$\nG S D_{x} & = 2 A \\frac{tan \\left(\\right. F O V_{H} \u002F 2 \\left.\\right)}{W} \\\\ G S D_{y} & = 2 A \\frac{tan \\left(\\right. F O V_{V} \u002F 2 \\left.\\right)}{H}\n$$\n\n(2)\n\nFor a given drone model, the camera specifications are constant and GSD is reduced to a function of altitude, which can be derived from either the horizontal or the vertical GSD and resolution, equivalently. For the camera of a modern commercial drone (in this work, experiments were conducted using the DJI Mavic 3M (DJI, Shenzhen, China)), with a vertical resolution of 3956 pixels and a corresponding vertical FOV of 48°, the altitude required to achieve a desired GSD is:\n\n$$\nA = \\frac{1}{2} \\frac{tan \\left(\\right. 48 \u002F 2 \\left.\\right)}{3956} G S D \\approx 4443 \\cdot G S D\n$$\n\n(3)\n\nGrapevine leaves average about 20 cm in size, while discolouration spots that can point to disease are often about 1 cm. In disease detection datasets such as [[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B13-agriculture-16-01753),[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B14-agriculture-16-01753),[15](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B15-agriculture-16-01753)], discolouration spots that signify disease are usually 7–20 pixels wide. Hence, targeting a similar resolution and assuming that spots would be clearly identifiable if they are at least 10 pixels wide, at a GSD of no more than 0.001 m per pixel would be required, pointing at a maximum altitude of about 4.4 m for disease detection. This GSD is also in line with relevant research on unhealthy leaf detection in the wild [[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B16-agriculture-16-01753)]. This corresponds to a footprint roughly 5.2 m × 3.9 m, or an area of 20 m 2, meaning that even a relatively small vineyard of 3–4 hectares would require thousands of photos to scan exhaustively.\n\nNon-exhaustive scans, or sampling, can provide insights regarding disease presence while keeping the number of images, the drone flight time, and the energy expenditure manageable. POI identification’s goal is to maximise the efficiency of low-altitude image sampling by identifying the most interesting, from a plant-health perspective, points in the vineyard.\n\n#### 2.3.2. Approach\n\nPrevious research has shown that NDVI bears a strong correlation with plant health and can be used to identify relevant risks [[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B6-agriculture-16-01753),[17](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B17-agriculture-16-01753)]. Crucially, NDVI-based insights do not rely on specific details but on the average value of a plant or vineyard area, and hence can be acquired with a greater GSD (e.g., 0.02–0.05 m per pixel), at higher altitudes. Accounting for the comparatively lower resolution of multispectral cameras, this corresponds to an altitude of 45–110 m.\n\nHence, NDVI images constructed from high-altitude multispectral photos can provide insights regarding plant health. In this work, such photos are used to drive a smart sampling scheme, in which spots are identified as points of interest (POIs) based on their NDVI value, and are marked for low-altitude image acquisition.\n\n#### 2.3.3. NDVI Calculation and Correction\n\nThe NDVI is computed from the Red and Near Infrared (NIR) bands:\n\n$$\nN D V I = \\frac{N I R - R e d}{N I R + R e d}\n$$\n\n(4)\n\nWhile the NIR and Red bands are acquired as part of the multispectral photo, they each have slightly different fields of view, due to the placement of the sensors on the drone. In addition, both images have slight vignetting distortion. Before computing the NDVI, band images must be undistorted and aligned:\n\nTo compensate for vignetting distortion, the vignetting polynomial coefficients are extracted from each image’s metadata and used. For alignment, Oriented FAST and Rotated BRIEF (ORB) [[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B18-agriculture-16-01753)] is used to detect keypoints and compute compact binary descriptors in the Red and NIR images. ORB was selected because it combines low computational cost with robustness to the small rotational differences between the two bands, making it suitable for repeated processing of high-resolution multispectral images, while its binary descriptors can be matched efficiently using Hamming distance. A brute-force matcher compares the descriptors between the two bands, with cross-checking applied to retain only mutual best matches. The matches are sorted by distance, and the best 50 correspondences are used to estimate an affine transformation that aligns the NIR band to the Red band.\n\n#### 2.3.4. Grapevine Row Identification and Segmentation\n\nFrom each NDVI image depicting a patch of the vineyard, individual segments of plants, corresponding to potential POIs, must be identified and the mean NDVI of each must be calculated:\n\nFirstly, the NDVI images show rows of grapevines as well as soil. To separate the grapevines from the background, a binary mask is applied to NDVI with a threshold of 0.35. This threshold is in line with previous research, which suggests thresholds between 0.35 and 0.4, depending on plant development [[19](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1753#B19-agriculture-16-01753)]. Grapevine rows are identified as continuous areas in the binary mask. Finally, each row is divided into smaller segments. Segmentation is performed along each row’s long axis, by lines perpendicular ","MDPI Agriculture 16(16):1753 2026-08-15","2026-08-15T00:00:00Z","论文",10,false,62,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,18,16,4,1,"论文提出卫星-无人机两层组网巡检与NDVI自适应路径规划，方法新颖，数据详实，对智慧农业有参考价值，但时效性稍差。",[24],{"name":10,"url":6},[26,27,28,29,30],"路径规划","智慧种植","葡萄园","NDVI","卫星-UAV",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,50,57,62],{"code":32,"score":17,"supported":45,"title":46,"abstract":48,"tags":49},true,[47],"葡萄",[47],[28],{"code":51,"score":52,"supported":45,"title":53,"abstract":54,"tags":56},"F4",3,[],[55],"植保",[],{"code":58,"score":21,"supported":14,"title":59,"abstract":60,"tags":61},"F5",[],[],[26],{"code":63,"score":21,"supported":14,"title":64,"abstract":65,"tags":66},"I3",[],[],[29],[68,69],"智慧种植 路径规划 葡萄园 卫星-UAV","智慧种植 路径规划","智慧种植路径规划葡萄园卫星-UAV-742","2026-08-19T23:57:32.281962Z",{"total":73,"page":21,"page_size":73,"items":74},6,[75,144,186,266,306,377],{"id":76,"title":77,"url":78,"summary":79,"summary_zh":80,"content":8,"source_name":81,"source_url":78,"published_at":82,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":83,"sources":87,"tags":89,"primary_topic":94,"secondary_topics":95,"classification":96,"search_phrases":113,"slug":116,"view_count":52,"doi":117,"paper":118,"created_at":143},1374,"From sky to ground: Enabling autonomous vineyard traversal with UAV-derived navigation maps","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112326","From sky to ground: Enabling autonomous vineyard traversal with UAV-derived navigation maps。Computers and Electronics in Agriculture","从天到地：利用无人机生成导航地图实现葡萄园自主遍历。《农业计算机与电子》","Computers and Electronics in Agriculture","2026-09-02T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":84,"freshness":85,"relevant":21,"comment":86},14,2,"研究提出利用无人机生成导航地图实现葡萄园自主行走，方法新颖，对精准农业有参考价值，但发表于一年前，时效性低。",[88],{"name":81,"url":78},[90,91,92,28,93],"智慧农业","无人机","农业人工智能","导航地图",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":97},{"candidates":98,"agricultural_evidence":45},[99,103,108],{"code":32,"score":20,"supported":45,"title":100,"abstract":101,"tags":102},[],[47],[28],{"code":104,"score":21,"supported":14,"title":105,"abstract":106,"tags":107},"I1",[],[],[90],{"code":109,"score":21,"supported":14,"title":110,"abstract":111,"tags":112},"I2",[],[],[92],[114,115],"农业人工智能 导航地图 智慧农业 无人机","农业人工智能 导航地图","农业人工智能导航地图智慧农业无人机-1374","10.1016\u002Fj.compag.2026.112326",{"doi":117,"openalex_id":119,"authors":120,"venue":81,"cited_by_count":135,"oa_url":78,"card":136,"direction":140,"ingested_from":142},"W7205016788",[121,123,126,129,132],{"name":122,"orcid":8},"Jonathan Cox",{"name":124,"orcid":125},"James R. Heselden","https:\u002F\u002Forcid.org\u002F0000-0001-6494-4981",{"name":127,"orcid":128},"Rajitha de Silva","https:\u002F\u002Forcid.org\u002F0000-0001-6404-1715",{"name":130,"orcid":131},"Marc Hanheide","https:\u002F\u002Forcid.org\u002F0000-0001-7728-1849",{"name":133,"orcid":134},"Riccardo Polvara","https:\u002F\u002Forcid.org\u002F0000-0001-8318-7269",0,{"tldr":137,"method":138,"finding":139,"direction":140,"opportunity":141},"利用无人机生成导航地图，实现葡萄园地面机器人自主遍历。","无人机图像生成导航地图，结合地面机器人路径规划。","无人机地图可有效支持地面自主导航，提高遍历效率。","智慧农业 \u002F 农业物联网","可探索无人机与地面机器人协同的实时地图更新，以及复杂地形下的鲁棒导航算法。","openalex","2026-09-02T23:30:02.292898Z",{"id":145,"title":146,"url":147,"summary":148,"summary_zh":8,"content":149,"source_name":150,"source_url":8,"published_at":151,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":135,"score_detail":152,"sources":154,"tags":156,"primary_topic":159,"secondary_topics":160,"classification":161,"search_phrases":179,"slug":182,"view_count":183,"doi":184,"paper":8,"created_at":185},111,"Global-Local Integrated Path Planning Strategy for Agricultural Robots in Complex Hilly Orchard Environments","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1866736\u002Ffull","Li Tian, Xingjia Pan提出一种面向复杂丘陵果园环境的农业机器人全局-局部一体化路径规划策略,有效解决农业机器人在复杂三维地形下的导航与障碍物避让问题。","About us\nAll journals\nAll articles\nSubmit manuscript\nSubmit data\nSearch\nLogin\nFrontiers in Sustainable Food Systems\nSections\nArticles\nResearch Topics\nEditorial board\nAbout journal\nPublished in\n\nFrontiers in Sustainable Food Systems\n\nAgricultural and Food Economics\n4.1\nimpact factor\n6\ncitescore\n\nPart of a Research Topic\n\nStrengthening Community Governance for Resilient and Sustainable Food Futures\n\n15k views\n14 articles\nEditor & Reviewers\n\nEdited by\n\nIVAN ANTONIO GARCIA-MONTALVO\n\nReviewed by\n\nJessa Frida Festijo\n\nOliver Eboy\n\nOutline\n\nAbstract\n\n1 Introduction\n\n2 Literature review\n\n3 Research design\n\n4 Analysis of the online fresh produce market\n\n5 Market research and analysis\n\n6 Conclusions and recommendations\n\nData availability statement\n\nEthics statement\n\nAuthor contributions\n\nFunding\n\nConflict of interest\n\nGenerative AI statement\n\nPublisher’s note\n\nSupplementary material\n\nReferences\n\nFigures and Tables\n\nFigure 1\n\nView in article\n\nFigure 2\n\nView in article\n\nFigure 3\n\nView in article\n\nFigure 4\n\nView in article\n\nFigure 5\n\nView in article\n\nFigure 6\n\nView in article\n\nFigure 7\n\nView in article\n\nFigure 8\n\nView in article\n\nFigure 9\n\nView in article\n\nFigure 10\n\nView in article\n\nTable 1\n\nSatisfaction reliability test table.\n\nView in article\n\nTable 2\n\nSignificance and reliability test table.\n\nView in article\n\nTable 3\n\nKMO and Bartlett’s test of sphericity.\n\nView in article\n\nTable 4\n\nKMO and Bartlett’s test of sphericity.\n\nView in article\n\nTable 5\n\nThematic feature keywords.\n\nView in article\n\nTable 6\n\nWeight allocation table for fresh food theme.\n\nView in article\n\nTable 7\n\nSelf-adjoint fuzzy matrix table.\n\nView in article\n\nTable 8\n\nCorresponding numbers for online fresh food platforms.\n\nView in article\n\nTable 9\n\nImproved specification weighting matrix.\n\nView in article\n\nTable 10\n\nResults of the improved intuitive fuzzy TOPSIS evaluation.\n\nView in article\n\nTable 11\n\nProduct data characteristics and descriptive statistics.\n\nView in article\nORIGINAL RESEARCH article\n\nFront. Sustain. Food Syst., 02 July 2026\n\nSec. Agricultural and Food Economics\n\nVolume 10 - 2026 | https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1866736\n\nFresh food retail satisfaction and sales forecasts: digital integration for sustainability in Guiyang, China\n\nChangpeng Yang 1†\n\nK\nH\n\nKeyan He 2†\n\nF\nH\n\nFu Hong 3†\n\nY\nZ\n\nYufeng Zhu 4\n\nLiming Chen 1*\n\n1. School of Tourism and Resource Environment, Qiannan Normal University for Nationalities, Duyun, China\n\n2. Party School of the Libo County Committee of the Communist Party of China, Libo, Guizhou, China\n\nSee more\n\nArticle metrics\n\nView details\n\n1,4k\n\nViews\n\n118\n\nDownloads\n\nAbstract\n\nIntroduction: \n\nDriven by the digital economy, China’s fresh food retail sector is accelerating its transition toward omnichannel integration. However, existing research has largely focused on first-tier cities in eastern China, paying insufficient attention to the heterogeneity of underdeveloped regions in southwestern China, and lacking a systematic analytical framework that integrates multi-source data and multiple methodologies.\n\nMethods: \n\nThis study takes six mainstream online fresh food platforms in Guiyang, China (including the local platform ‘Guihuihui’) as its research subjects. By integrating two rounds of stratified sampling questionnaire surveys (1,744 valid responses) with 16,321 online review data points, we constructed a multi-model integrated analytical framework comprising an LDA topic model, an improved intuitive fuzzy TOPSIS method, and the AT-DeepAR deep learning model. This framework was used to systematically investigate the mechanisms underlying the evolution of fresh food retail formats, the factors influencing consumer satisfaction, and market sales forecasting.\n\nResults: \n\nThe study found that: (1) transaction costs and service quality are the core drivers of digital transformation in fresh food retail, with consumers placing the greatest emphasis on delivery speed and shopping experience; (2) platform satisfaction rankings are as follows: Duoduo Grocery > Meituan Youxuan > JD NOW > Freshippo > Guihuihui > Dingdong Maicai, with national platforms enjoying significantly higher brand recognition than local platforms; (3) the AT-DeepAR model, incorporating an attention mechanism, can accurately capture the seasonal fluctuations in fresh produce sales, with predicted values closely matching actual figures (within a 95% confidence interval). It forecasts that the average monthly sales of fresh produce platforms in Guiyang will steadily increase to approximately 340 million yuan by 2025.\n\nDiscussion: \n\nAt the theoretical level, this paper fills a research gap on fresh food e-commerce in southwestern China by establishing an integrated analytical paradigm combining questionnaire data, text mining, and deep learning. At the practical level, it provides empirical evidence to support differentiated competition, transaction cost optimization, and the digital upgrading of the regional fresh food industry for local platforms in Guiyang. It holds significant practical value for promoting the sustainable development of fresh food retail, enhancing the efficiency of urban–rural food distribution, and facilitating the low-carbon transition of supply chains.\n\n1 Introduction\n\nThe rapid development of internet technology and shifts in consumer behavior have made online shopping an integral part of daily life. Against this backdrop, online fresh food shopping has emerged and is growing rapidly. As a key province in south-western China, Guizhou has seen sustained growth in the demand for fresh produce in recent years, driven by both urbanization and consumption upgrades (Guizhou Provincial Bureau of Statistics, Guizhou Provincial Office of the Leading Group for the Seventh National Population Census, 2021). Fueled by the pandemic, more consumers have turned to ‘online shopping’, presenting numerous challenges to traditional fresh food retail models, which suffer from low delivery efficiency, high costs and a lack of personalized service.\n\nIn recent years, the evolution of the fresh food retail sector has become increasingly evident. Traditional farmers’ markets and supermarkets have gradually shifted from offline to online operations, while new retail models such as front-end warehouses (e.g., Freshippo, Dingdong Maicai) and community group-buying platforms (e.g., Meituan Youxuan, Duoduo Grocery) have rapidly emerged. These platforms meet consumers’ demands for timeliness and convenience through an ‘online ordering + offline delivery’ model, making the integrated online-offline retail model increasingly prominent.\n\nAgainst this background, Guizhou Province launched the “Guihuihui” online fresh produce platform in 2022, integrating high-quality fresh produce resources from Guiyang and its surrounding areas (Zhongwang News, 2022). The platform breaks through the spatial and temporal barriers of traditional retail, centring on the digital reconstruction of the “people-goods-place” model to drive the transformation of supermarket retail formats. Through efficient service and high-quality products, it has rapidly gained market recognition and consumer trust, demonstrating strong growth momentum.\n\nAs a critical link in the food supply chain, the digital transformation and service optimization of fresh food retail have a direct bearing on food security, resource conservation, the reduction of wastage and losses, and the improvement of the quality of life for urban and rural residents. This study focuses on consumer groups in Guiyang, China, conducting an empirical analysis of consumer satisfaction through a questionnaire survey (Hongxin and Yuhui, 2015). It examines online fresh produce platforms in Guiyang, analyses the evolution of the fresh produce retail sector, explores the extent to which these platforms meet consumer needs and their impact on market development, reviews the current state of the fresh produce market, identifies shortcomings, and offers constructive suggestions and references for the market’s future development.\n\n2 Literature review\n\nDriven by the dual forces of the digital economy and consumption upgrades, China’s fresh food retail sector is rapidly shifting from traditional offline models toward diverse business formats such as omnichannel integration, community group buying and forward-depot models. Online fresh food retail has become the mainstream consumption channel due to its convenience, efficiency and value for money. The evolution of fresh food retail formats is the result of the combined influence of transaction costs, digital technology, cold-chain logistics and consumer behavior, whilst consumer satisfaction serves as a core indicator influencing repeat purchases and brand loyalty. Existing research has yielded substantial findings regarding the mechanisms of business model evolution, factors influencing satisfaction, evaluation models and sales forecasting; however, studies have generally focused on first-tier cities in the east and national platforms. There remains a notable lack of integrated research that targets south-western China, focuses on provincial capitals, compares local and national platforms, and combines multi-source data with multiple methodologies. This paper systematically reviews the relevant literature and identifies research gaps, thereby providing a theoretical foundation for subsequent empirical analysis.\n\n2.1 Research on the evolution of fresh produce retail formats\n2.1.1 The evolutionary trajectory of fresh produce retail formats\n\nExisting literature has established a relatively systematic research framework concerning the evolution of fresh produce retail formats. This framework primarily unfolds along three main threads: the iteration of new retail models, the integration of digital technologies, and consumer behavior-driven trends. It provides a theoretical foundation for understanding the transformation of China’s fresh produce distribution system from traditional channels toward an integrated online-offline model characterized by intelligence, efficiency and diverse coexistence. Regarding new retail models and business formats, the digital economy has driven the rapid proliferation of models such as forward-depot logistics, store-warehouse integration and community group buying, which have become the core directions for business format upgrading. Relevant studies, using cases such as Freshippo, have revealed the organizational logic, supply chain coordination and fulfilment efficiency characteristics of new retail models (Chen et al., 2022; Zhang et al., 2024). Omnichannel integration is regarded as a core feature of new retail, capable of significantly enhancing consumer engagement and channel loyalty (Chen et al., 2022) Meanwhile, traditional farmers’ markets have achieved modernisation through semi-formalization, forming a complementary and co-existing relationship with e-commerce platforms (Yao, 2025; Yuan et al., 2026). The retail sector’s overall evolution toward digitalisation, sustainability, platformisation and ecosystem-based models aligns closely with the reality of multi-platform coexistence in Guiyang (Akhtoian, 2023). In terms of digital technology integration, big data, artificial intelligence and supply chain optimization are deeply intertwined, driving the fresh produce retail sector toward precision and intelligence. Blockchain and green low-carbon technologies help reduce coordination costs and enhance supply chain transparency (Chen and Yin, 2023); the integration of online and offline channels significantly improves service quality and customer loyalty (Zhang and Liang, 2023); deep learning models based on LSTM and GWO-LSTM have been widely applied to sales forecasting and pricing optimization (Wang et al., 2024). Innovations in logistics models and carbon reduction practices play a crucial role in ensuring fresh produce quality and stabilizing supply (He and Sun, 2025). On the consumer-driven front, consumption preferences, value perceptions and channel choices directly shape the retail landscape. Research indicates that segmentation of consumption motives, quality demands, freshness preferences and price sensitivity significantly influence channel decisions (Kamal, 2025; Naveuler, 2025; Sarker et al., 2024), whilst consumption upgrades are driving the fresh produce supply chain toward greater health-consciousness, experiential value and immediacy (White, 2022). These findings provide the theoretical foundation for this study’s examination of business format evolution, consumer behavior and service experience in Guiyang. At the same time, digital transformation not only improves operational efficiency but also reduces wastage of fresh produce through accurate forecasting, optimizes delivery routes, and facilitates low-carbon logistics and the sustainable use of resources.\n\n2.1.2 Factors influencing the evolution of fresh food retail formats\n\nExisting research has systematically analyzed the driving mechanisms behind the evolution of fresh food retail formats from multiple perspectives, including digitalisation, consumer behavior, product quality, supply chain efficiency, physical retail environments and the policy landscape. Digital transformation and the O2O model are the core drivers of format upgrading, whilst the optimization of digital processes can reduce wastage and enhance fulfilment efficiency (Ma et al., 2022; Yang et al., 2022; Belwal et al., 2024). Consumer perceived value, service quality and trust in food safety significantly influence purchase intention and channel loyalty (Činčalová et al., 2021; Xing et al., 2024; Thuannadee and Praneetpholkrang, 2025), whilst product safety, traceability and supply chain efficiency constitute key constraints on the sustainable development of business formats (Cheng et al., 2026; Lestari et al., 2024). The physical environment, spatial layout and location selection of brick-and-mortar stores also influence the consumer experience (Maciel et al., 2023). At the macro level, policy regulations, urbanization and consumption upgrading collectively drive the transformation of traditional retail formats (Yuan et al., 2026).\n\n2.2 Theoretical framework and influencing factors of consumer satisfaction with online fresh food purchases\n2.2.1 Theoretical foundations of consumer satisfaction\n\nThe theoretical framework of consumer satisfaction is well-established, providing a solid academic foundation for research into satisfaction with online fresh produce. Cardozo (Cardozo, 1965) pioneered the revelation of the role satisfaction plays in promoting repeat purchases (Cardozo, 1965); the Expectation-Confirmation Theory (ECT) is widely used to explain the mechanisms underlying the formation of satisfaction (Shen et al., 2022). Satisfaction is a multidimensional construct influenced by multiple factors such as product attributes, demographic characteristics and context (Yılmaz and Balcıoğlu, 2026), and its application continues to expand across scenarios including omnichannel retail, service interactions and green consumption (Yaqub et al., 2022; Xu et al., 2022; Wang et al., 2023). In marketing and logistics theory, satisfaction often serves as a core mediating or outcome variable, significantly driving customer loyalty and willingness to continue using a product or service (Kerschbaumer et al., 2022; El Moussaoui et al., 2022; Kovalskii, 2024). This theoretical framework lays the foundation for the use of LDA and the improved intuitive fuzzy TOPSIS method in this study to evaluate satisfaction.\n\n2.2.2 Factors influencing satisfaction with online fresh food\n\nIn the context of online fresh food, satisfaction is driven by multiple factors, including logistics services, product quality, service quality and brand image. Confirmed that logistics services are the most significant factor influencing satisfaction (Guan et al., 2022); product quality, freshness and safety also play a crucial role (Kang and Namkung, 2024); service quality indirectly influences behavioral intention through perceived value (Wang and Theerakittikul, 2022; Thuannadee and Praneetpholkrang, 2025); and brand image has a significant positive effect on satisfaction and purchase intention (Erkmen and Turegun, 2022). These findings provide direct justification for the selection of evaluation indicators in this study, including product source safety, product integrity, delivery service, and after-sales response. Furthermore, from a sustainability perspective, high satisfaction levels help to improve user retention, reduce customer acquisition costs and minimize waste of resources, and are central to the platform’s long-term sustainable operation.\n\n2.3 Research progress on evaluation methods and sales forecasting models for fresh food E-commerce\n2.3.1 Satisfaction evaluation models and methods\n\nTraditional satisfaction evaluation has primarily relied on methods such as IPA and TOPSIS; in recent years, it has been upgraded through the integration of positive text mining and machine learning. Developed a food quality evaluation mode (Valderrama et al., 2021); emphasized the necessity of a multi-dimensional service quality framework (Yuan et al., 2026); and (Mao, 2025) highlighted the superior accuracy and adaptability of intelligent models. With the digital transformation, technologies such as AI-powered recommendations, dynamic pricing and sentiment analysis have been employed to enhance satisfaction (Dai and Liu, 2024; Kumari et al., 2024; Mittal, 2025). Text mining and LDA topic modeling can efficiently extract consumer concerns and sentiment tendencies, thereby enhancing the objectivity of evaluations (Singh and Glińska-Neweś, 2022; Song et al., 2022; Arifin and Purnama, 2023), providing methodological support for the adoption of LDA and the improved intuitive fuzzy TOPSIS in this study.\n\n2.3.2 Deep learning models for sales forecasting\n\nDeep learning has demonstrated superior capabilities in capturing time-series patterns and volatility in sales forecasting. LSTM and its variants are widely used in retail sales forecasting (Hu et al., 2022; Samonte et al., 2022), and the introduction of self-attention mechanisms can further enhance the capture of key information (Kossen et al., 2021). Confirmed the accuracy advantages of deep learning in time series forecasting (Messaoudi et al., 2023); however, research also suggests a risk of overfitting in scenarios involving small sample sizes and high volatility (Peng et al., 2022; Tilshi and Punsisi, 2022). Accordingly, this paper introduces the AT-DeepAR model—which utilizes an attention mechanism to enable accurate sales forecasting, thereby significantly reducing inventory wastage in the fresh produce sector and lowering energy consumption in the cold chain. It serves as a key technological enabler for achieving low-carbon, efficient and sustainable operations in fresh produce retail.\n\n2.4 Research on the sustainable development of fresh food\n\nExisting research provides a solid foundation for the sustainable development of fresh food and highlights the intrinsic links between food safety, environmental sustainability and economic viability. Enhancing the environmental sustainability of food supply chains is crucial to achieving several of the United Nations Sustainable Development Goals (SDGs), particularly Zero Hunger (Goal 2) and Responsible Consumption and Production (Goal 12); They emphasize that food safety measures must be balanced against environmental factors to avoid adverse effects (López-Gálvez et al., 2021). Further research supports this view, highlighting the necessity of establishing a sustainable fresh food cold chain system that balances environmental, social and economic benefits; a systematic review indicates that sustainable practices in this field can enhance market viability and resource utilization efficiency (Liao et al., 2021). Furthermore, innovations in food packaging, such as biodegradable materi","Frontiers in Sustainable Food Systems 2026-07-02","2026-07-02T01:00:00Z",{"impact":135,"substance":135,"depth":135,"authority":135,"freshness":135,"relevant":135,"comment":153},"标题与正文不符，正文为生鲜零售满意度研究，非农业机器人路径规划，不相关。",[155],{"name":150,"url":147},[157,26,158],"农业机器人","丘陵果园",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":162},{"candidates":163,"agricultural_evidence":45},[164,170,175],{"code":32,"score":165,"supported":45,"title":166,"abstract":168,"tags":169},8,[167],"orchard",[],[],{"code":171,"score":20,"supported":45,"title":172,"abstract":173,"tags":174},"F6",[],[157],[157],{"code":58,"score":21,"supported":14,"title":176,"abstract":177,"tags":178},[],[],[26],[180,181],"农业机器人 丘陵果园 路径规划","农业机器人 丘陵果园","农业机器人丘陵果园路径规划-111",11,"10.3389\u002Ffsufs.2026.1866736\u002Ffull","2026-07-31T00:00:56.026421Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":8,"source_name":192,"source_url":189,"published_at":193,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":194,"score_detail":195,"sources":200,"tags":202,"primary_topic":208,"secondary_topics":209,"classification":216,"search_phrases":237,"slug":240,"view_count":135,"doi":241,"paper":242,"created_at":265},5547,"Apple quality and soil biological diversity in long-term organic and IPM apple orchards","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1943487","Introduction While several studies have compared fruit quality in organic and conventional apple production, integrated assessments of fruit quality and soil biological diversity remain limited. Majority of previous studies have been based on relatively short-term experimental comparisons, although responses of soil biota and soil properties often require many years of consistent management to become fully expressed. Methods This study compared yield, fruit quality, and soil biological diversity in long-term organic (ORG) apple orchard and long-term integrated pest management (IPM) orchard under Nordic conditions. The study was conducted in 2022–2024 at the Polli Horticultural Research Centre (Estonia) using the cultivars “Katre”, “Krista,” and “Alesja”. Apple, yield, fruit quality attributes (soluble solids and titratable acids content and their ratio, ascorbic acid content and total polyphenols, total anthocyanins, fruit firmness, fruit colour, fruit mineral elements content), soil chemical parameters, earthworm and springtail communities were analysed. Results and Discussion Higher total and fresh-market yields were observed in the IPM orchard in two of the three cultivars, whereas the proportion of fresh-market fruit did not differ consistently between the two orchards. When averaged across cultivars, apples from ORG orchard exhibited more intense peel coloration. Differences in taste-related traits and antioxidant composition between cultivation systems were cultivar- and year-dependent rather than consistently associated with cultivation system. Total polyphenol content was consistently higher under ORG management only in “Katre,” while “Alesja” exhibited the highest total polyphenol and anthocyanin contents irrespective of cultivation system, highlighting the dominant role of genotype. Soil in the ORG orchard had higher pH and greater phosphorus and calcium contents, supporting a higher proportion of anecic earthworms, particularly Aporrectodea longa . Total earthworm abundance was higher in the IPM orchard, springtail abundance did not differ significantly between systems. These results suggest that the conditions in the long-term ORG were more favourable for ecologically important earthworm groups without consistently affecting internal fruit quality compared to IPM orchard.","引言 尽管已有若干研究比较了有机与常规苹果生产中的果实品质，但结合果实品质与土壤生物多样性的综合评价仍较为有限。以往研究大多基于相对短期的试验比较，而土壤生物区系和土壤性质的响应往往需要多年持续一致的管理才能充分显现。方法 本研究在北欧条件下比较了长期有机（ORG）苹果园与长期综合病虫害管理（IPM）苹果园的产量、果实品质及土壤生物多样性。研究于2022—2024年在爱沙尼亚Polli园艺研究中心开展，供试品种为“Katre”“Krista”和“Alesja”。分析了苹果产量、果实品质指标（可溶性固形物与可滴定酸含量及其比值、抗坏血酸含量、总多酚、总花青素、果实硬度、果实色泽、果实矿质元素含量）、土壤化学参数、蚯蚓和跳虫群落。结果与讨论 在3个品种中的2个品种上，IPM果园的总产量和鲜食市场产量较高，而鲜食市场果实比例在两种果园间并无一致差异。按品种平均，ORG果园苹果果皮色泽更深。栽培系统间口感相关性状和抗氧化成分的差异因品种和年份而异，而非与栽培系统稳定相关。总多酚含量仅在“Katre”中于ORG管理下持续较高，而“Alesja”无论栽培系统如何均表现出最高的总多酚和花青素含量，凸显了基因型的主导作用。ORG果园土壤具有较高的pH以及较高的磷和钙含量，支持了较高比例的内栖型蚯蚓，尤其是长穴居蚯蚓（Aporrectodea longa）。IPM果园蚯蚓总丰度较高，跳虫丰度在系统间无显著差异。这些结果表明，与IPM果园相比，长期ORG的条件更有利于生态上重要的蚯蚓类群，但并未对果实内部品质产生一致影响。","Frontiers in Sustainable Food Systems","2026-10-21T00:00:00Z",66,{"impact":165,"substance":196,"depth":19,"authority":197,"freshness":198,"relevant":21,"comment":199},20,13,9,"北欧长期定位试验对比有机与IPM苹果园的果实品质与土壤生物多样性，结论有新意但属区域尺度研究，公共影响有限。",[201],{"name":192,"url":189},[203,204,205,206,207],"长期定位试验","有机果园","土壤生物多样性","苹果品质","IPM",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[210],{"code":211,"slug":212,"name":213,"path":214,"parent_code":215},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":217},{"candidates":218,"agricultural_evidence":45},[219,226,231],{"code":32,"score":220,"supported":45,"title":221,"abstract":223,"tags":225},17,[222],"apple",[224,167,222],"苹果",[],{"code":211,"score":84,"supported":45,"title":227,"abstract":228,"tags":230},[212],[229,212],"土壤",[],{"code":232,"score":52,"supported":45,"title":233,"abstract":234,"tags":236},"H6",[],[235],"生物多样性",[],[238,239],"爱沙尼亚 Polli 苹果 有机 IPM","苹果园 蚯蚓 跳虫 土壤","爱沙尼亚Polli苹果有机IPM-5547","10.3389\u002Ffsufs.2026.1943487",{"doi":241,"openalex_id":243,"authors":244,"venue":192,"cited_by_count":135,"oa_url":189,"card":259,"direction":263,"ingested_from":142},"W7223104118",[245,248,251,253,256],{"name":246,"orcid":247},"Ulvi Moor","https:\u002F\u002Forcid.org\u002F0000-0002-8808-8494",{"name":249,"orcid":250},"Annely Kuu","https:\u002F\u002Forcid.org\u002F0000-0002-0335-6243",{"name":252,"orcid":8},"K. Kahu",{"name":254,"orcid":255},"P. Põldma","https:\u002F\u002Forcid.org\u002F0000-0002-7906-823X",{"name":257,"orcid":258},"Reelika Rätsep","https:\u002F\u002Forcid.org\u002F0000-0002-8673-9978",{"tldr":260,"method":261,"finding":262,"direction":263,"opportunity":264},"比较长期有机与IPM苹果园的果实品质和土壤生物多样性。","在爱沙尼亚长期试验园测定三个品种的产量、品质及蚯蚓和跳虫群落。","有机园果皮色泽更深、土壤pH和磷钙更高，但果实内在品质差异多随品种和年份变化。","农业绿色发展与碳","可结合长期定位试验，量化土壤动物群落与果实品质的关联机制，并拓展多区域验证。","2026-10-22T11:20:15.557635Z",{"id":267,"title":268,"url":269,"summary":270,"summary_zh":8,"content":271,"source_name":272,"source_url":8,"published_at":273,"category":274,"cover_url":8,"hotness":13,"is_selected":14,"score":275,"score_detail":276,"sources":278,"tags":280,"primary_topic":284,"secondary_topics":285,"classification":286,"search_phrases":301,"slug":304,"view_count":135,"doi":8,"paper":8,"created_at":305},5516,"上海农科院葡萄团队赴银川参加第47届世界葡萄与葡萄酒大会","https:\u002F\u002Fwww.saas.sh.cn\u002Fhzjl\u002Fgjhz\u002Fcontent_57128","10月12日至16日第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员作专题报告，阐明疏穗与环剥处理对果实糖、花青素及脱落酸积累的源库调控机制。团队还观摩酿酒葡萄AI综合作业机器人成果展示，与研发单位围绕葡萄智慧装备与果园智能化管理深入交流。","2026年10月12-16日，第47届世界葡萄与葡萄酒大会在银川举办，智慧农业研究中心葡萄团队负责人奚晓军研究员一行赴会交流。团队围绕“提升葡萄种植生产韧性与可持续发展能力”主题，向全球同行展示最新科研成果。\n\n会上，奚晓军作“Changes in sugar, anthocyanin and ABA accumulation in response to cluster thinning and girdling in ‘Jumeigui’grape”专题报告。报告围绕葡萄疏穗与环剥处理开展研究，阐明源库比对果实糖、花青素及脱落酸积累的调控机制，为葡萄优质栽培技术提供理论支撑，报告内容引发国内外园艺专家广泛研讨。参会期间，团队前往银川国际会展中心观摩酿酒葡萄 AI 综合作业机器人成果展示，详细了解该装备在葡萄园智能作业方面的技术特点与应用前景，并与研发单位围绕葡萄智慧装备研发、果园智能化管理等方向开展深入交流。\n\n本次参会是葡萄团队持续推进科研国际化、对标国际前沿开展研究的重要实践。","上海市农业科学院 2026-10-19","2026-10-19T00:00:00Z","报道",65,{"impact":19,"substance":220,"depth":197,"authority":197,"freshness":73,"relevant":21,"comment":277},"国际学术会议交流并涉及葡萄AI作业机器人观摩，有实质科研内容与行业参考价值，但属团队参会动态，影响面有限。",[279],{"name":272,"url":269},[90,281,47,282,283],"智能农机","栽培技术","国际交流",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":287},{"candidates":288,"agricultural_evidence":45},[289,293,297],{"code":32,"score":17,"supported":45,"title":290,"abstract":291,"tags":292},[47],[47],[47],{"code":104,"score":20,"supported":45,"title":294,"abstract":295,"tags":296},[],[90],[90],{"code":58,"score":21,"supported":14,"title":298,"abstract":299,"tags":300},[],[],[281],[302,303],"上海农科院 葡萄 银川","世界葡萄与葡萄酒大会 奚晓军","上海农科院葡萄银川-5516","2026-10-22T00:07:04.061911Z",{"id":307,"title":308,"url":309,"summary":310,"summary_zh":311,"content":8,"source_name":192,"source_url":309,"published_at":312,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":313,"score_detail":314,"sources":317,"tags":319,"primary_topic":325,"secondary_topics":326,"classification":327,"search_phrases":346,"slug":349,"view_count":135,"doi":350,"paper":351,"created_at":376},5468,"Farm-level technical efficiency in temperate horticulture: evidence from apple, pear, and cherry growers","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1928386","Improving resource-use efficiency is fundamental to enhancing agricultural productivity and promoting sustainable horticultural development, particularly in regions where opportunities for expanding cultivated land and increasing input use are limited. This study evaluates the technical efficiency of apple, pear, and cherry growers in the Kashmir Valley and examines the socioeconomic, institutional, and regional factors associated with farm-level technical efficiency. Primary data were collected from 418 horticultural households during 2023 using a multi-stage sampling approach. Technical efficiency was first estimated separately for apple, pear, and cherry growers using an input-oriented Data Envelopment Analysis (DEA) framework under both Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) assumptions. Scale efficiency was subsequently estimated, while a pooled DEA model was employed to generate farm-level technical efficiency scores for the second-stage analysis. The determinants of technical efficiency were examined using Tobit regression, supported by a fractional logit model as a robustness check, and regional disparities were assessed using Kruskal–Wallis and Mann–Whitney non-parametric tests. The results indicate that apple growers achieved the highest overall technical efficiency, followed by pear and cherry growers. The VRS estimates reveal substantially higher levels of pure technical efficiency, whereas the scale efficiency analysis suggests that a considerable proportion of technical inefficiency arises from farms operating below their optimal production scale. The second-stage analysis identifies family size, education, irrigation facilities, access to credit, and cold storage infrastructure as significant positive factors associated with farm-level technical efficiency, while significant regional disparities are observed across the Kashmir Valley. The robustness analysis confirms the consistency of the estimated relationships. These findings highlight that improving horticultural performance requires not only efficient farm-level resource management but also stronger institutional support, investments in irrigation and post-harvest infrastructure, improved access to rural credit, and region-specific development strategies. The study contributes to the literature by integrating crop-specific and pooled efficiency analyses with socioeconomic and regional assessments, providing policy-relevant evidence for promoting resource-efficient and sustainable horticultural production.","提高资源利用效率是提升农业生产力、促进园艺产业可持续发展的基础，在耕地扩张和投入增加机会有限的地区尤为如此。本研究评估了克什米尔谷地苹果、梨和樱桃种植者的技术效率，并考察了与农场层面技术效率相关的社会经济、制度和区域因素。研究采用多阶段抽样方法，于2023年从418个园艺农户收集了一手数据。首先，在规模报酬不变（CRS）和规模报酬可变（VRS）两种假设下，运用投入导向的数据包络分析（DEA）框架分别估算苹果、梨和樱桃种植者的技术效率。随后估算规模效率，并采用混合DEA模型生成农场层面技术效率得分用于第二阶段分析。技术效率的决定因素通过Tobit回归进行检验，并以分数logit模型作为稳健性检验加以支持，区域差异则采用Kruskal–Wallis和Mann–Whitney非参数检验进行评估。结果表明，苹果种植者总体技术效率最高，梨和樱桃种植者次之。VRS估计显示纯技术效率水平显著更高，而规模效率分析表明，相当比例的技术无效率源于农场经营规模低于最优生产规模。第二阶段分析识别出家庭规模、教育、灌溉设施、信贷获取和冷库基础设施是与农场层面技术效率显著正相关的因素，同时在克什米尔谷地观察到显著的区域差异。稳健性分析证实了所估计关系的一致性。这些发现表明，提升园艺产业绩效不仅需要有效的农场层面资源管理，还需要更强的制度支持、对灌溉和采后基础设施的投资、改善农村信贷获取以及因地制宜的发展策略。本研究通过将作物特定效率分析与混合效率分析同社会经济和区域评估相结合，为促进资源利用效率提升提供了具有政策相关性的证据。","2026-10-20T00:00:00Z",73,{"impact":17,"substance":315,"depth":18,"authority":197,"freshness":198,"relevant":21,"comment":316},21,"基于418户果农一手数据，用DEA与Tobit回归分析技术效率，方法规范、结论有政策参考价值，但属区域性研究，影响范围有限。",[318],{"name":192,"url":309},[320,321,322,323,324],"可持续农业","园艺产业","苹果种植","技术效率","数据包络分析",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":328},{"candidates":329,"agricultural_evidence":45},[330,334,341],{"code":32,"score":84,"supported":45,"title":331,"abstract":332,"tags":333},[222],[224,222],[],{"code":335,"score":73,"supported":45,"title":336,"abstract":337,"tags":340},"H3",[],[338,339],"灌溉","irrigation",[],{"code":342,"score":21,"supported":14,"title":343,"abstract":344,"tags":345},"H8",[],[],[320],[347,348],"数据包络分析 可持续农业 园艺产业 技术效率","数据包络分析 可持续农业","数据包络分析可持续农业园艺产业技术效率-5468","10.3389\u002Ffsufs.2026.1928386",{"doi":350,"openalex_id":352,"authors":353,"venue":192,"cited_by_count":135,"oa_url":309,"card":370,"direction":374,"ingested_from":142},"W7223025732",[354,356,359,362,365,368],{"name":355,"orcid":8},"Seerat Sajjad",{"name":357,"orcid":358},"Khursheed Hussain Dar","https:\u002F\u002Forcid.org\u002F0000-0002-3925-0758",{"name":360,"orcid":361},"Anam Ul Haq Ganie","https:\u002F\u002Forcid.org\u002F0000-0002-4404-5011",{"name":363,"orcid":364},"Ghadda Mohamed Awad Yousif","https:\u002F\u002Forcid.org\u002F0000-0003-4183-2957",{"name":366,"orcid":367},"Mohd Salim","https:\u002F\u002Forcid.org\u002F0000-0003-0162-8433",{"name":369,"orcid":8},"Mahdy S. Othman",{"tldr":371,"method":372,"finding":373,"direction":374,"opportunity":375},"评估克什米尔谷地苹果、梨、樱桃种植户的技术效率及其影响因素。","418户调查数据，DEA测效率，Tobit回归与分数logit分析影响因素。","苹果效率最高，规模效率不足是主因；灌溉、信贷、冷库等显著提升效率。","数字乡村与农业信息化","可结合遥感与物联网数据构建果园投入产出实时效率监测，弥补自报数据局限。","2026-10-21T23:30:07.145395Z",{"id":378,"title":379,"url":380,"summary":381,"summary_zh":382,"content":8,"source_name":192,"source_url":380,"published_at":312,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":383,"score_detail":384,"sources":387,"tags":389,"primary_topic":394,"secondary_topics":395,"classification":396,"search_phrases":421,"slug":424,"view_count":135,"doi":425,"paper":426,"created_at":440},5464,"From symbolic to substantive: How subsidy design shapes cooperative roles and high-quality rural industrial development?","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1949418","Introduction Government subsidies do not necessarily produce high-quality rural industrial development (HQRID), as different subsidy designs elicit different behavioral responses from cooperatives and smallholders. This study examines how subsidy design shapes the strategies of local governments, cooperatives, and farmers. Methods Drawing on the Dingzhuang Grape Cooperative in Jurong, China, this study builds a tripartite evolutionary game model to examine how creation support and function support shape the strategies of the local government, cooperative, and farmer. Results The results distinguish two contrasting pathways. Creation support tends to induce symbolic compliance, in which cooperatives adopt formal structures without providing training, quality control, or market access, leading the system toward a low-quality equilibrium. Function support aligns cooperative incentives with verifiable performance and encourages substantive services that connect smallholders to higher-value markets. Three conclusions follow. First, the transition to substantive service requires function support that gives cooperatives sufficiently strong incentives. Second, substantive services lower the quality-premium share that farmers require, strengthening the economic case for high-quality production. Third, function support is bounded by fiscal capacity, and excessive performance-linked payments can reverse the transition. Discussion Policy should therefore tie support to measurable gains in farmer income and product quality. The study contributes to induced institutional change theory by specifying that external inducements produce substantive institutions only when rewards are tied to verifiable performance rather than to registration, a boundary condition that explains why supported organizations may remain hollow.","引言 政府补贴并不必然带来高质量的农村产业发展（HQRID），因为不同的补贴设计会引发合作社与小农户不同的行为反应。本研究考察补贴设计如何塑造地方政府、合作社与农户的策略。方法 本研究以中国句容市丁庄葡萄合作社为案例，构建三方演化博弈模型，考察创建支持与功能支持如何塑造地方政府、合作社与农户的策略。结果 结果区分出两条截然不同的路径。创建支持倾向于诱发象征性遵从，即合作社采取正式结构却不提供培训、质量控制或市场准入，使系统趋向低质量均衡。功能支持使合作社激励与可验证绩效相一致，并鼓励将小农户连接至更高价值市场的实质性服务。由此得出三点结论。第一，向实质性服务的转变需要功能支持，使合作社获得足够强的激励。第二，实质性服务降低了农户所需的质量溢价份额，强化了高质量生产的经济合理性。第三，功能支持受财政能力约束，过度的绩效挂钩支付可能逆转这一转变。讨论 因此，政策应将支持与农户收入和产品质量的可测量改善挂钩。本研究对诱致性制度变迁理论作出贡献，明确指出外部诱因只有在奖励与可验证绩效而非与登记注册挂钩时才会产生实质性制度，这一边界条件解释了受支持组织为何可能保持空壳化。",77,{"impact":19,"substance":385,"depth":18,"authority":197,"freshness":165,"relevant":21,"comment":386},22,"以句容葡萄合作社为案例的三方演化博弈研究，区分创建型与功能型补贴对合作社行为的影响，结论对补贴政策设计有实质参考价值。",[388],{"name":192,"url":380},[390,391,392,393],"乡村产业","合作社","小农户","补贴设计",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":397},{"candidates":398,"agricultural_evidence":45},[399,404,410,416],{"code":32,"score":73,"supported":45,"title":400,"abstract":401,"tags":403},[],[47,402],"grape",[],{"code":405,"score":406,"supported":45,"title":407,"abstract":408,"tags":409},"G2",5,[],[392],[391,392],{"code":411,"score":52,"supported":45,"title":412,"abstract":413,"tags":415},"G6",[],[414],"农户收入",[],{"code":417,"score":21,"supported":14,"title":418,"abstract":419,"tags":420},"G4",[],[],[390],[422,423],"句容 丁庄 葡萄合作社","补贴设计 合作社 小农户","句容丁庄葡萄合作社-5464","10.3389\u002Ffsufs.2026.1949418",{"doi":425,"openalex_id":427,"authors":428,"venue":192,"cited_by_count":135,"oa_url":380,"card":435,"direction":374,"ingested_from":142},"W7222997429",[429,431,433],{"name":430,"orcid":8},"Man Huang",{"name":432,"orcid":8},"蒋礼文",{"name":434,"orcid":8},"Jincai Zhuang",{"tldr":436,"method":437,"finding":438,"direction":374,"opportunity":439},"用三方演化博弈分析补贴设计如何影响合作社行为与农村产业高质量发展。","构建政府、合作社、农户三方演化博弈模型，基于句容丁庄葡萄合作社案例。","创建型补贴诱发象征性合规，功能型补贴促成实质性服务并降低农户质量溢价要求。","可将补贴绩效挂钩机制与可验证数据采集结合，研究数字化考核如何抑制合作社空心化。","2026-10-21T23:30:06.732822Z"]