[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-514":3,"related-514":69},{"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":63,"slug":66,"view_count":67,"doi":8,"paper":8,"created_at":68},514,"Advances in Perception, Autonomous Operation, and Collaborative Systems for Smart Orchard Robots","https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046","Rui Ye与Mingxiong Ou(江苏大学农业工程学院、江苏省农业装备与智能化高技术重点实验室)发表的智能果园机器人综述,聚焦在具有挑战性果园场景中运行的自主机器人系统需求,全面概述了环境感知与语义认知、自主导航与环境建模、智能任务执行、协同机器人系统等关键技术。系统讨论了水果与花蕾检测、枝干与冠层结构感知、多模态传感器融合定位、语义建图、机器人采收控制、变量喷施、精准授粉和行内自主除草管理等方面的最新进展。此外还综述了多机器人协调、机器人-无人机协同、大语言模型(LLM)和视觉-语言模型(VLM)等新兴技术。",null,"## 1. Introduction\n\nOrchard production is one of the most labor-intensive components of agricultural production systems and involves diverse operations, including blossom pollination, pest and disease control, weeding, inspection, harvesting, and transportation. Compared with field-crop production, orchard operations are highly seasonal, have narrow operational windows, involve spatially dispersed targets, and remain heavily dependent on manual labor. In recent years, rising labor costs and a declining agricultural workforce have placed substantial pressure on conventional orchard-management practices. In particular, reliance on manual labor alone may be increasingly insufficient to meet the requirements of large-scale, refined, and highly efficient production in harvesting, spraying, pollination, and weeding. Accordingly, the use of robots, sensors, autonomous navigation, machine vision, and intelligent optimization algorithms to increase orchard automation has become a major direction in the development of smart orchards [[1](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B1-applsci-16-08046),[2](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B2-applsci-16-08046),[3](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B3-applsci-16-08046)].\n\nWith the development of smart and digital agriculture, agricultural robots are evolving from single-purpose mechanized equipment into intelligent systems capable of environmental perception, autonomous mobility, precision execution, and task-level decision-making. Previous studies have indicated that autonomous driving systems, intelligent agricultural robots, and multi-robot systems are increasingly being applied to conventionally manual tasks such as seeding, spraying, and harvesting, while agricultural production is progressively moving toward unmanned and intelligent operation [[4](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B4-applsci-16-08046)]. In orchard environments in particular, robotic technologies have been used for fruit harvesting, flower pollination, variable-rate spraying, intra-row weeding, environmental monitoring, and yield estimation, thereby offering technical pathways to alleviate labor shortages, improve operational efficiency, and reduce resource consumption [[5](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B5-applsci-16-08046),[6](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B6-applsci-16-08046)].\n\nExisting studies have demonstrated a broad range of orchard-robot applications. Harvesting robots mainly address fruit recognition, three-dimensional localization, manipulator path planning, and end-effector control [[7](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B7-applsci-16-08046)]. Spraying robots emphasize target recognition, variable-rate pesticide application, and improved utilization of spray liquid. Pollination robots focus on flower detection, identification of suitable pollination windows, and precise pollen delivery. Weeding robots primarily address weed recognition between rows or individual trees, path tracking, and operational safety [[8](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B8-applsci-16-08046),[9](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B9-applsci-16-08046)]. For example, research on spraying robots for jujube orchards has noted the large scale of China’s jujube industry and the important role of high-density dwarf jujube orchards in Xinjiang. Mid-season management in these orchards still depends heavily on manual labor and is affected by pronounced seasonal and structural labor shortages, creating a clear practical demand for automated orchard robots [[10](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B10-applsci-16-08046)].\n\nAt the same time, the capabilities of a single robot the capabilities of a single robot are no longer sufficient to meet the production requirements of large orchards. In extensive orchards, densely distributed task locations, or geographically distributed orchard systems, a single robot often suffers from low operational efficiency, limited coverage, and long task-completion times. Multi-robot collaboration, robot-UAV collaboration, and cross-orchard task scheduling have therefore emerged as new research directions in smart orchards [[11](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B11-applsci-16-08046),[12](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B12-applsci-16-08046)]. Related studies have shown that agricultural robots and UAVs can be applied to pollination, harvesting, spraying, and monitoring. Ground robots are well suited to close-range precision operations, whereas UAVs are advantageous for aerial spraying and inspection. Coordinated use of the two platforms can exploit their complementary strengths and improve the efficiency of complex agricultural operations [[13](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B13-applsci-16-08046),[14](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B14-applsci-16-08046),[15](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B15-applsci-16-08046)].\n\nConsequently, research on intelligent orchard robots is no longer confined to a single mechanical structure or a single-purpose machine. Instead, it is increasingly oriented toward an integrated technological system comprising multimodal perception, autonomous navigation, precision operation execution, task scheduling, and intelligent decision-making. Achieving robust perception, reliable mobility, accurate execution, and efficient collaboration in complex orchard environments has become a key requirement for advancing orchard robots from experimental validation to practical deployment [[16](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B16-applsci-16-08046),[17](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B17-applsci-16-08046),[18](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B18-applsci-16-08046)].\n\nOrchard environments combine semi-structured and unstructured characteristics. Fruit trees are generally planted in rows and therefore exhibit a degree of spatial regularity; however, canopy morphology, foliage density, fruit distribution, terrain undulation, weed cover, and working-lane width remain highly variable. These environmental characteristics create substantial challenges for orchard robots in perception, localization, navigation, and operation execution [[19](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B19-applsci-16-08046)]. In contrast to indoor or standardized industrial environments, orchard robots must not only identify operation targets but also understand tree structure, traversable areas, obstacle locations, and the spatial relationships between operation targets and robotic actuators.\n\nFirst, complex orchard environments impose major challenges on perception systems. Fruits, flowers, branches, trunks, leaves, and weeds vary considerably in color, morphology, and spatial distribution and are frequently affected by occlusion, overlap, illumination variation, and background interference. For harvesting robots, fruit-localization errors directly affect grasping success and fruit-damage rates. For pollination robots, the accuracy of flower recognition and pollination-position control influences pollination performance. For weeding and spraying robots, accurate recognition of trunks, weeds, canopies, and target plants is essential for operational safety and resource-use efficiency [[20](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B20-applsci-16-08046),[21](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B21-applsci-16-08046)]. Studies on semantic mapping have shown that conventional simultaneous localization and mapping (SLAM) can provide relatively accurate geometric maps, but geometric information alone is insufficient to support autonomous orchard operations. Robots must also understand semantic information, such as trees, terrain, and traversable regions, within the map. Second, orchard localization and navigation are challenged by unstable global navigation satellite system (GNSS) signals, sensor degradation, and dynamic environmental changes [[22](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B22-applsci-16-08046)]. GNSS performs well in open farmland, but canopy occlusion, multipath interference, and terrain shielding in orchards can reduce positioning accuracy or cause signal loss. Inertial navigation can provide continuous short-term pose estimates but accumulates drift. Vision-based navigation provides rich environmental information but is vulnerable to illumination, weather, and motion blur. Two-dimensional LiDAR is relatively insensitive to lighting but captures only planar information and may provide insufficient geometry in complex canopies and undulating terrain [[23](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B23-applsci-16-08046)]. To address these issues, current studies increasingly employ multi-sensor fusion involving LiDAR, RGB-D cameras, inertial measurement units (IMUs), wheel odometry, and adaptive Monte Carlo localization (AMCL) to improve localization stability and path-tracking performance in GNSS-constrained orchard environments.\n\nOrchard operations also impose stringent requirements on navigation and execution accuracy. Spraying, pollination, weeding, and harvesting cannot be completed merely by reaching a target area; rather, robots must operate stably along specified paths and at prescribed distances and attitudes [[24](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B24-applsci-16-08046)]. For example, a spraying robot in a high-density jujube orchard must follow predefined operation paths with high-precision cruising and point-specific stopping. Studies have implemented spray-route navigation using LiDAR SLAM, IMU-based inertial navigation, extended Kalman filtering (EKF), AMCL localization, A* path planning, and dynamic window approach (DWA)-based local obstacle avoidance [[25](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B25-applsci-16-08046)]. Field experiments reported navigation-error indices for straight-line travel, point stopping, turning, and S-shaped path tracking, demonstrating explicit requirements for lateral-deviation and heading-angle control in practical orchard operation. Meanwhile, orchard-robot tasks are expanding from single-robot, single-task operation toward multi-robot, multi-task, and multi-scenario systems. Although multi-robot systems can improve operational efficiency, they introduce complex challenges involving task allocation, path conflicts, energy constraints, load balancing, and operation-time limits. Existing studies have emphasized the inherent coupling between task allocation and path planning in agricultural multi-robot operations. If task allocation is considered without accounting for obstacles, terrain constraints, and dynamic interactions among robots, the resulting solution may be difficult to execute effectively in real environments [[26](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B26-applsci-16-08046)]. Joint optimization of task allocation and path planning is therefore an important direction for improving the feasibility and robustness of collaborative agricultural-robot operations.\n\nCollaborative scheduling becomes even more complex in distributed or multi-orchard scenarios. Orchards may be geographically dispersed and differ in area, task density, and environmental conditions. In addition, robot and UAV operations may be subject to precedence constraints. For example, in the task-allocation problem involving pollination robots and spraying UAVs across multiple orchards, robots must complete pollination before UAV spraying begins, and this precedence requirement markedly increases scheduling complexity [[27](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B27-applsci-16-08046),[28](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#B28-applsci-16-08046)]. The simultaneous involvement of multiple agents, multiple orchards, heterogeneous task densities, and heterogeneous platforms further strengthens the combinatorial-optimization nature of these problems.\n\nOverall, autonomous operation in complex orchard environments faces four principal challenges. First, perceptual targets are complex: fruits, flowers, branches, trunks, and weeds are readily affected by occlusion and environmental variation. Second, the navigation environment is difficult: GNSS constraints, uneven terrain, and canopy occlusion reduce localization stability. Third, operation execution requires high precision: robots must perform accurate, efficient, safe, and minimally damaging operations within confined spaces. Fourth, multi-robot collaborative scheduling is highly complex and must simultaneously account for task duration, path cost, load balance, energy constraints, and precedence relationships. Together, these challenges require orchard-robot research to progress from isolated module optimization toward system-level co-design.\n\nTo address the requirements of autonomous agricultural-robot operation in complex orchard environments, this review is organized around the technical chain of environmental perception, autonomous navigation, precision execution, and collaborative decision-making. In contrast to reviews focused on a single robot category or a single operation technology, this article emphasizes the technical interrelationships among the perception, mobility, execution, and decision layers and seeks to capture the overall trend toward systematic development of intelligent orchard robots.\n\nThe review first summarizes environmental-perception and semantic-understanding technologies for orchard robots. This section examines methods for recognizing and localizing representative objects, including fruits, flowers, branches, trunks, weeds, and obstacles, with particular emphasis on applications of machine vision, RGB-D sensing, stereo vision, LiDAR, point-cloud processing, instance segmentation, semantic segmentation, and VLMs in complex orchard scenes. Existing studies show that geometric maps alone cannot satisfy the requirements of autonomous operations. Semantic maps provide high-level information on tree layouts, target instances, and traversable regions and thus constitute an important foundation for autonomous navigation and task execution.\n\nSecond, this review summarizes localization, navigation, and scene-mapping methods for orchard robots. With emphasis on autonomous mobility in GNSS-constrained environments, this section discusses visual navigation, LiDAR navigation, SLAM-based mapping, multi-sensor fusion localization, semantic map construction, path planning, and path-tracking control. Related studies indicate that multi-sensor fusion can partially compensate for the limitations of individual sensors and improve navigation reliability in complex, occluded, and unstructured orchard scenes.\n\nThird, this review summarizes precision-execution technologies for representative orchard operations, including harvesting, spraying, pollination, and weeding. Key issues include operation-target localization, manipulator control, variable-rate spraying, liquid pollination, intra-row weeding, wind compensation, error calibration, and system evaluation. Comparison across tasks shows that precision execution depends not only on actuator performance but also on close coordination among front-end perception accuracy, navigation stability, and operation-control strategies. Finally, multi-robot collaborative scheduling and intelligent decision-making are reviewed, with emphasis on multi-robot task allocation, joint task-path optimization, multi-trip harvesting schedules, robot-UAV collaboration, multi-orchard operation management, and the use of LLMs and VLMs in robotic task planning. Existing research suggests that single-robot systems are insufficient for large-scale orchard operations. Although multi-robot systems can alleviate efficiency bottlenecks, they also require effective solutions to task allocation, path planning, load balancing, and energy constraints. Accordingly, this article aims to provide a clear, comprehensive, and engineering-oriented review framework for intelligent orchard robots and to establish a basis for the subsequent analysis of environmental perception, autonomous navigation, precision operation, and collaborative decision-making. To illustrate the hierarchical structure of the technological system and the relationships among technical pathways and operation applications, [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F16\u002F16\u002F8046#table_body_display_applsci-16-08046-t001) summarizes the core content, representative methods, supported tasks, and primary functions of each technological layer.\n\n**Table 1.** Key technology framework and representative application tasks of intelligent orchard robots.\n\nFor the purpose of this review, a smart orchard robot is defined as an embodied or physically deployed robotic system designed to operate in orchard environments and capable of implementing at least part of an integrated perception–decision–action loop. Such systems may perform autonomous or semi-autonomous functions related to orchard inspection, navigation, harvesting, spraying, pollination, weeding, transportation, or coordinated task execution.\n\nGround mobile robots constitute the primary focus of this review. Aerial robots are included only when they directly perform orchard operations or cooperate with ground robots in sensing, task planning, scheduling, or execution. Fixed sensing platforms are not regarded as robots but are considered enabling infrastructure when their outputs are directly integrated into robotic perception, localization, planning, or control. Agricultural machinery is included only when it incorporates orchard-specific autonomous sensing, localization, decision-making, or operation-control capabilities.\n\nIndividual sensors, perception algorithms, manipulators, end effectors, and control modules are treated as robotic enabling subsystems rather than independent orchard robots. They are included when they have a demonstrated or clearly defined role within a robotic operation pipeline. Similarly, LLMs, VLMs, and other foundation models are considered only when they directly support robot perception, semantic reasoning, task planning, behavior generation, or control. General-purpose artificial-intelligence applications, conventional mechanized equipment, standalone remote-sensing studies, and broader smart-farming management systems without a direct connection to orchard-robot operation are outside the principal scope of this review.\n\nTo distinguish platform types from functional technologies, the reviewed studies are classified along two dimensions:\n\n(1)\na platform dimension comprising ground robots, aerial robots, heterogeneous ground–aerial systems, and enabling robotic subsystems; and\n\n(2)\na functional dimension comprising environmental perception, localization and navigation, precision operation execution, collaborative decision-making, and system-level evaluation.\n\nTo improve the transparency and reproducibility of this review, a structured literature search and study-selection process was conducted. The search was completed on 15 June 2026 using SPIS, covering publications from 2002 to 15 June 2026. Search terms covered orchard environments, robotic platforms, and key functions, including perception, navigation, mapping, harvesting, spraying, pollination, weeding, manipulation, multi-robot systems, robot–UAV cooperation, and vision-language models. Search syntax was adapted for each database, and reference lists of relevant reviews and primary studies were also examined.\n\nA PRISMA-type procedure was used for study selection. The initial search identified 215 records. After duplicate removal, titles, abstracts, and full texts were screened. Studies were included if they investigated orchard robots or enabling technologies with a clear role in a robotic perception–decision–execution pipeline and reported sufficiently detailed or quantitative results. Studies focused solely on conventional machinery, standalone remote sensing, general smart-farming management, or algorithms without a clear robotic application were excluded. Editor","Applied Sciences 2026-08-12","2026-08-11T16:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,14,7,1,"综述果园机器人感知、自主作业与协同系统，内容系统，对智慧果园发展有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"果园机器人","自主导航","多机器人协作","Applied Sciences","江苏大学",{"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":46},[44,51,58],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},8,true,[48],"orchard",[],[],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":57},"F4",3,[],[56],"除草",[],{"code":59,"score":21,"supported":14,"title":60,"abstract":61,"tags":62},"F5",[],[],[27],[64,65],"多机器人协作 果园机器人 江苏大学 自主导航","多机器人协作 果园机器人","多机器人协作果园机器人江苏大学自主导航-514",4,"2026-08-13T23:53:47.532594Z",{"total":70,"page":21,"page_size":70,"items":71},6,[72,154,198,269,332,405],{"id":73,"title":74,"url":75,"summary":76,"summary_zh":77,"content":8,"source_name":78,"source_url":75,"published_at":79,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":80,"score_detail":81,"sources":86,"tags":88,"primary_topic":94,"secondary_topics":95,"classification":102,"search_phrases":123,"slug":126,"view_count":127,"doi":128,"paper":129,"created_at":153},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":45,"substance":18,"depth":82,"authority":83,"freshness":84,"relevant":21,"comment":85},16,13,9,"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。",[87],{"name":78,"url":75},[89,90,91,92,93],"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},[96],{"code":97,"slug":98,"name":99,"path":100,"parent_code":101},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":39,"locked":14,"version":40,"method":41,"evidence":103},{"candidates":104,"agricultural_evidence":46},[105,112,117],{"code":32,"score":106,"supported":46,"title":107,"abstract":109,"tags":111},17,[108],"apple",[110,48,108],"苹果",[],{"code":97,"score":19,"supported":46,"title":113,"abstract":114,"tags":116},[98],[115,98],"土壤",[],{"code":118,"score":53,"supported":46,"title":119,"abstract":120,"tags":122},"H6",[],[121],"生物多样性",[],[124,125],"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",0,"10.3389\u002Ffsufs.2026.1943487",{"doi":128,"openalex_id":130,"authors":131,"venue":78,"cited_by_count":127,"oa_url":75,"card":146,"direction":150,"ingested_from":152},"W7223104118",[132,135,138,140,143],{"name":133,"orcid":134},"Ulvi Moor","https:\u002F\u002Forcid.org\u002F0000-0002-8808-8494",{"name":136,"orcid":137},"Annely Kuu","https:\u002F\u002Forcid.org\u002F0000-0002-0335-6243",{"name":139,"orcid":8},"K. Kahu",{"name":141,"orcid":142},"P. Põldma","https:\u002F\u002Forcid.org\u002F0000-0002-7906-823X",{"name":144,"orcid":145},"Reelika Rätsep","https:\u002F\u002Forcid.org\u002F0000-0002-8673-9978",{"tldr":147,"method":148,"finding":149,"direction":150,"opportunity":151},"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。","openalex","2026-10-22T11:20:15.557635Z",{"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":166,"tags":168,"primary_topic":174,"secondary_topics":175,"classification":176,"search_phrases":193,"slug":196,"view_count":127,"doi":8,"paper":8,"created_at":197},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":82,"substance":106,"depth":83,"authority":83,"freshness":70,"relevant":21,"comment":165},"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。",[167],{"name":160,"url":157},[169,170,171,172,173],"智慧农业","智能农机","葡萄","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":177},{"candidates":178,"agricultural_evidence":46},[179,184,189],{"code":32,"score":180,"supported":46,"title":181,"abstract":182,"tags":183},12,[171],[171],[171],{"code":185,"score":67,"supported":46,"title":186,"abstract":187,"tags":188},"I1",[],[169],[169],{"code":59,"score":21,"supported":14,"title":190,"abstract":191,"tags":192},[],[],[170],[194,195],"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":199,"title":200,"url":201,"summary":202,"summary_zh":203,"content":8,"source_name":78,"source_url":201,"published_at":204,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":205,"score_detail":206,"sources":209,"tags":211,"primary_topic":217,"secondary_topics":218,"classification":219,"search_phrases":238,"slug":241,"view_count":127,"doi":242,"paper":243,"created_at":268},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":180,"substance":207,"depth":17,"authority":83,"freshness":84,"relevant":21,"comment":208},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。",[210],{"name":78,"url":201},[212,213,214,215,216],"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":220},{"candidates":221,"agricultural_evidence":46},[222,226,233],{"code":32,"score":19,"supported":46,"title":223,"abstract":224,"tags":225},[108],[110,108],[],{"code":227,"score":70,"supported":46,"title":228,"abstract":229,"tags":232},"H3",[],[230,231],"灌溉","irrigation",[],{"code":234,"score":21,"supported":14,"title":235,"abstract":236,"tags":237},"H8",[],[],[212],[239,240],"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":242,"openalex_id":244,"authors":245,"venue":78,"cited_by_count":127,"oa_url":201,"card":262,"direction":266,"ingested_from":152},"W7223025732",[246,248,251,254,257,260],{"name":247,"orcid":8},"Seerat Sajjad",{"name":249,"orcid":250},"Khursheed Hussain Dar","https:\u002F\u002Forcid.org\u002F0000-0002-3925-0758",{"name":252,"orcid":253},"Anam Ul Haq Ganie","https:\u002F\u002Forcid.org\u002F0000-0002-4404-5011",{"name":255,"orcid":256},"Ghadda Mohamed Awad Yousif","https:\u002F\u002Forcid.org\u002F0000-0003-4183-2957",{"name":258,"orcid":259},"Mohd Salim","https:\u002F\u002Forcid.org\u002F0000-0003-0162-8433",{"name":261,"orcid":8},"Mahdy S. Othman",{"tldr":263,"method":264,"finding":265,"direction":266,"opportunity":267},"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":270,"title":271,"url":272,"summary":273,"summary_zh":274,"content":8,"source_name":78,"source_url":272,"published_at":204,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":275,"sources":278,"tags":280,"primary_topic":285,"secondary_topics":286,"classification":287,"search_phrases":312,"slug":315,"view_count":127,"doi":316,"paper":317,"created_at":331},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。",{"impact":82,"substance":276,"depth":17,"authority":83,"freshness":45,"relevant":21,"comment":277},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。",[279],{"name":78,"url":272},[281,282,283,284],"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":288},{"candidates":289,"agricultural_evidence":46},[290,295,301,307],{"code":32,"score":70,"supported":46,"title":291,"abstract":292,"tags":294},[],[171,293],"grape",[],{"code":296,"score":297,"supported":46,"title":298,"abstract":299,"tags":300},"G2",5,[],[283],[282,283],{"code":302,"score":53,"supported":46,"title":303,"abstract":304,"tags":306},"G6",[],[305],"农户收入",[],{"code":308,"score":21,"supported":14,"title":309,"abstract":310,"tags":311},"G4",[],[],[281],[313,314],"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":316,"openalex_id":318,"authors":319,"venue":78,"cited_by_count":127,"oa_url":272,"card":326,"direction":266,"ingested_from":152},"W7222997429",[320,322,324],{"name":321,"orcid":8},"Man Huang",{"name":323,"orcid":8},"蒋礼文",{"name":325,"orcid":8},"Jincai Zhuang",{"tldr":327,"method":328,"finding":329,"direction":266,"opportunity":330},"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",{"id":333,"title":334,"url":335,"summary":336,"summary_zh":337,"content":8,"source_name":338,"source_url":335,"published_at":339,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":340,"score_detail":341,"sources":343,"tags":345,"primary_topic":350,"secondary_topics":351,"classification":352,"search_phrases":377,"slug":380,"view_count":127,"doi":381,"paper":382,"created_at":404},5401,"A HIERARCHICAL DISTRIBUTED EDGE AI FRAMEWORK FOR REAL-TIME APPLE LEAF DISEASE CLASSIFICATION","https:\u002F\u002Fdoi.org\u002F10.22190\u002Ffuacr260506002g","The application of artificial intelligence (AI) in agriculture has transformed crop management, particularly in the detection of plant diseases. Apple trees, a key agricultural commodity, are vulnerable to various diseases, which can severely impact crop yields. Traditional disease detection methods, often reliant on manual inspections, are time-consuming and prone to error. In response, this study proposes a novel two-stage edge AI framework for apple leaf disease classification that integrates RGB imagery with distributed edge computing. The first stage performs binary classification to distinguish between healthy and diseased leaves, achieving an F1 score of 95.50%. This optimization is achieved by utilizing lightweight models on edge devices, thereby enhancing computational efficiency. The second stage employs a consensus-based multiclass classification approach, achieving a 96.44% F1 score in identifying specific diseases. This dual-stage system, supported by an edge AI architecture, ensures real-time disease detection while minimizing resource consumption and enhancing scalability in large-scale agricultural environments. The full system achieved a 95.50% accuracy and F1 score on the special dataset. The results demonstrate the system’s efficacy in controlled laboratory conditions, offering a promising solution for precise, real-time agricultural monitoring. This study underscores the potential of distributed edge AI systems for scalable and efficient disease detection in precision agriculture.","人工智能（AI）在农业中的应用已深刻改变了作物管理方式，尤其是在植物病害检测方面。苹果树作为重要的农业经济作物，易受多种病害侵袭，严重影响作物产量。传统的病害检测方法通常依赖人工检查，耗时且易出错。为此，本研究提出了一种新颖的两阶段边缘AI框架，用于苹果叶片病害分类，该框架将RGB图像与分布式边缘计算相结合。第一阶段执行二分类以区分健康叶片与患病叶片，F1分数达到95.50%。该优化通过在边缘设备上采用轻量级模型实现，从而提升了计算效率。第二阶段采用基于共识的多类分类方法，在识别具体病害方面F1分数达到96.44%。该双阶段系统在边缘AI架构的支持下，实现了实时病害检测，同时最大限度地降低了资源消耗，并增强了在大规模农业环境中的可扩展性。完整系统在专用数据集上达到了95.50%的准确率和F1分数。结果表明，该系统在受控实验室条件下具有有效性，为精准、实时的农业监测提供了一种有前景的解决方案。本研究凸显了分布式边缘AI系统在精准农业中实现可扩展且高效病害检测的潜力。","Facta Universitatis Series Automatic Control and Robotics","2026-10-18T00:00:00Z",76,{"impact":17,"substance":207,"depth":106,"authority":180,"freshness":45,"relevant":21,"comment":342},"该研究提出两级边缘AI框架，在苹果叶片病害识别上取得95%以上F1值，方法新颖、数据可靠，对精准农业实时监测有参考价值，但尚处实验室验证阶段，产业影响有限。",[344],{"name":338,"url":335},[169,346,347,348,349],"农业人工智能","边缘计算","作物病害识别","苹果病害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":353},{"candidates":354,"agricultural_evidence":46},[355,359,365,371],{"code":32,"score":17,"supported":46,"title":356,"abstract":357,"tags":358},[108],[34,110,108],[349],{"code":185,"score":20,"supported":46,"title":360,"abstract":361,"tags":364},[],[362,363],"精准农业","precision agriculture",[169],{"code":366,"score":20,"supported":46,"title":367,"abstract":368,"tags":370},"I5",[],[347,369],"edge computing",[347],{"code":372,"score":297,"supported":46,"title":373,"abstract":374,"tags":376},"I2",[],[375],"artificial intelligence",[348,346],[378,379],"苹果叶片病害 边缘AI","分布式边缘计算 病害分类","苹果叶片病害边缘AI-5401","10.22190\u002Ffuacr260506002g",{"doi":381,"openalex_id":383,"authors":384,"venue":338,"cited_by_count":127,"oa_url":335,"card":397,"direction":403,"ingested_from":152},"W7222964752",[385,388,391,394],{"name":386,"orcid":387},"Milan Grujev","https:\u002F\u002Forcid.org\u002F0009-0002-3211-6844",{"name":389,"orcid":390},"Dragan H. Stojanovic","https:\u002F\u002Forcid.org\u002F0000-0003-4893-3814",{"name":392,"orcid":393},"Miloš Ilić","https:\u002F\u002Forcid.org\u002F0000-0002-9696-2874",{"name":395,"orcid":396},"Aleksandar Milosavljević","https:\u002F\u002Forcid.org\u002F0000-0003-3057-7727",{"tldr":398,"method":399,"finding":400,"direction":401,"opportunity":402},"提出两级边缘AI框架，用RGB图像实时分类苹果叶片病害。","分布式边缘计算+轻量模型，先二分类再共识多分类。","两级系统准确率与F1均达95.50%，多分类F1为96.44%。","智慧农业 \u002F 农业物联网","仅在受控实验室验证，可延伸至田间复杂光照与多作物泛化及边缘能耗优化。","农业人工智能与决策模型","2026-10-20T23:30:33.830748Z",{"id":406,"title":407,"url":408,"summary":409,"summary_zh":8,"content":8,"source_name":410,"source_url":8,"published_at":411,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":412,"score_detail":413,"sources":415,"tags":417,"primary_topic":422,"secondary_topics":423,"classification":424,"search_phrases":435,"slug":438,"view_count":127,"doi":8,"paper":439,"created_at":447},5297,"高志永团队《基于特征优选与优化算法的干热河谷区柑橘蒸腾模拟》","https:\u002F\u002Fwww.sohu.com\u002Fa\u002F1087305323_122014422","《智慧农业(中英文)》2026年第8卷第4期刊载云南农业大学吴明清、高志永等团队论文,以10年生柑橘树为研究对象收集2020—2022年树干液流与环境因子数据;针对干热河谷加快建设智慧灌区背景下传统机理模型参数需求高、对部分参数高度敏感且误差来源复杂等难题,提出基于特征优选与优化算法的蒸腾模拟方法,可为优化灌区水分调控与配水策略提供决策支撑。","智慧农业(中英文)(2026,8(4))","2026-10-13T00:00:00Z",72,{"impact":82,"substance":207,"depth":106,"authority":83,"freshness":297,"relevant":21,"comment":414},"核心期刊论文，方法有创新且数据规模扎实，对干热河谷智慧灌区水分调控有决策参考价值，但属细分领域研究，时效性一般。",[416],{"name":410,"url":408},[169,418,419,420,421],"云南","柑橘","智慧灌区","蒸腾模拟",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":425},{"candidates":426,"agricultural_evidence":46},[427,431],{"code":32,"score":180,"supported":46,"title":428,"abstract":429,"tags":430},[419],[419],[419],{"code":185,"score":67,"supported":46,"title":432,"abstract":433,"tags":434},[],[169],[169],[436,437],"云南农业大学 柑橘 蒸腾模拟","干热河谷 智慧灌区","云南农业大学柑橘蒸腾模拟-5297",{"doi":8,"openalex_id":8,"authors":440,"venue":8,"cited_by_count":127,"oa_url":8,"card":441,"direction":401,"ingested_from":446},[],{"tldr":442,"method":443,"finding":444,"direction":401,"opportunity":445},"用特征优选与优化算法模拟干热河谷区柑橘蒸腾。","基于2020—2022年树干液流与环境因子数据构建特征优选与优化算法模型。","该方法可降低机理模型参数依赖与敏感性，提升蒸腾模拟精度。","可探索将蒸腾模拟嵌入智慧灌区实时配水决策，研究多树种迁移与边缘部署。","agent","2026-10-19T00:07:48.135945Z"]