[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-513":3,"related-513":77},{"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":43,"search_phrases":71,"slug":74,"view_count":75,"doi":8,"paper":8,"created_at":76},513,"Height-Based Stratification in Greenhouse Harvesting Robotics: A Review from Ground-Level to High-Wire Crops","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713","Zuhui Zhou、Yile Chen、Wenshuo Gao、Xinpeng Wang、Xuan Liang、Xifeng Liang(中国计量大学机电工程学院、中国农业科学院研究生院)发表的温室采收机器人综述,以作物高度为主要分层标准,涵盖地面层(≤0.5m,如草莓、生菜)、中层(0.5—1.5m,如番茄、辣椒、黄瓜)和高层(>1.5m,如架式黄瓜)。针对各高度层,分析关键设计特征、技术进展、原型性能及常见障碍——果实遮挡、机械作物损伤、操作不可靠、商业成本高。未来方向应聚焦智能感知、柔性末端执行器和高度特定解决方案(地面作物群体机器人、中层模块化混合设计、高层基础设施协同设计)。",null,"Logical Operator Operator\n\nSearch Text\n\nSearch Type\n\n_add\\_circle\\_outline_\n\n_remove\\_circle\\_outline_\n\n[![Image 1: agriculture-logo](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fjournals\u002Fagriculture-logo.png?d9aa6395761dfdff)](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Fagriculture)\n\n## Article Menu\n\nFont Type:\n\n_Arial_ _Georgia_ _Verdana_\n\nFont Size:\n\nAa Aa Aa\n\nLine Spacing:\n\n__ __ __\n\nColumn Width:\n\n__ __ __\n\nBackground:\n\nOpen Access Review\n\nby \nZuhui Zhou\n\n 1,†[![Image 2: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0009-0005-9059-9305), \nYile Chen\n\n 1,†[![Image 3: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0009-0005-3836-8526), \nWenshuo Gao\n\n 1,*[](mailto:suregeorge@163.com)[![Image 4: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0000-0001-9197-959X), \nXinpeng Wang\n\n 2[![Image 5: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0009-0009-0234-7319), \nXuan Liang\n\n 1 and \nXifeng Liang\n\n 1\n\n1\n\nSchool of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China\n\n2\n\nGraduate School of Chinese Academy of Agricultural Sciences, Beijing 100081, China\n\n*\n\nAuthor to whom correspondence should be addressed.\n\n†\n\nThese authors contributed equally to this work.\n\nSubmission received: 11 June 2026 \u002F Revised: 23 July 2026 \u002F Accepted: 30 July 2026 \u002F Published: 11 August 2026\n\n## Abstract\n\nLabor shortages and the push for higher greenhouse efficiency have accelerated interest in automated harvesting. However, the development of a universal harvesting robot has been constrained by large variations in crop architecture, especially plant height. In this review, a height-based stratification of greenhouse harvesting robots and transferable high-wire crop harvesters is presented, covering ground-level crops (\u003C0.6 m, e.g., strawberry), medium-height crops (0.6–1.5 m, e.g., tomato), and high-wire crops (>1.5 m, e.g., trellised cucumber). For each height layer, key design features, technical progress, prototype performance, and common obstacles—including fruit occlusion, mechanical crop damage, unreliable operation, and high commercial costs—are analyzed. Future efforts should target intelligent perception, soft end-effectors, and height-specific solutions (swarm robotics for ground crops, modular hybrid designs for medium crops, infrastructure co-design for high-wire crops). By using plant height as the primary stratification criterion, a design-oriented framework is provided, distinct from conventional crop-type or mechanism-based categorizations.\n\n## 1. Introduction\n\nThe advancement of protected agriculture, particularly greenhouse cultivation, has become increasingly pivotal for ensuring year-round food security and enhancing production efficiency amidst global population growth [[1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B1-agriculture-16-01713)]. Greenhouses provide a controlled environment that mitigates the adverse effects of climate change and seasonal fluctuations, thus offering a stable platform for high-density, high-quality crop production. As a representative example of this global agricultural trend, China is modernizing its agricultural sector through the mechanization and intelligent upgrading of production equipment, a development aligned with the global transition toward “Agriculture 4.0” [[2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B2-agriculture-16-01713)].\n\nWithin this framework, automated harvesting has emerged as a core technology for mitigating the challenges of escalating labor costs and seasonal workforce shortages. These challenges are particularly acute in labor-intensive greenhouse operations [[3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B3-agriculture-16-01713)]. Harsh working conditions—high humidity, repetitive motion, and elevated temperatures—combined with relatively low wages have made manual harvesting increasingly unpopular among the younger generation. Consequently, advanced robotic interventions are required to sustain agricultural viability [[4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B4-agriculture-16-01713),[5](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B5-agriculture-16-01713)]. The harvesting manipulator and its critical end-effector for picking and handling are considered the functional heart of any automated harvesting system [[6](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B6-agriculture-16-01713)]. The harvesting manipulator is defined as the mechanical arm responsible for motion execution. The end-effector refers to the terminal actuator directly contacting fruits. It serves as the primary interface between mechatronic systems and delicate biological fruit.\n\nIn recent years, much research has focused on soft robotic grippers that mimic human finger compliance. However, the remarkable architectural diversity of greenhouse crops remains a formidable obstacle to universal manipulators [[7](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B7-agriculture-16-01713),[8](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B8-agriculture-16-01713)]. Unlike industrial environments, agricultural settings present unstructured challenges: fruit size, shape, and ripeness often vary even within the same cultivar. Consequently, the performance of harvesting robots—measured by success rate, cycle time, and damage rate—frequently falls short of commercial requirements. A harvesting robot refers to the complete system, including mobile platform, manipulator, vision system, and control system. Success rates vary significantly and drop sharply when robots encounter complex morphologies or unpredictable conditions such as fluctuating light and overlapping foliage [[9](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B9-agriculture-16-01713),[10](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B10-agriculture-16-01713)].\n\nAmong various morphological traits, crop growth height is a primary factor dictating manipulator design. The vertical distribution of fruit directly determines the required workspace, kinematic configuration (e.g., degrees of freedom), and structural stiffness needed for precision at different reaches [[11](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B11-agriculture-16-01713),[12](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B12-agriculture-16-01713)]. Furthermore, growth height influences perception complexity. For example, ground-level crops often need top-down sensing to navigate soil occlusion, whereas high-wire crops require side-view vision systems with obstacle-avoidance algorithms [[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B13-agriculture-16-01713)].\n\nDespite its critical importance, crop height has not been systematically adopted as a classification criterion in existing reviews of harvesting manipulators. Current surveys typically fall into two categories: crop-based classifications, which organize technologies by individual species and are intuitive for agronomic research but often redundantly describe structurally similar manipulators across different crops; and mechanism-based classifications, which group technologies by hardware form and clarify mechanical distinctions but remain decoupled from practical agronomic constraints and field application requirements [[14](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B14-agriculture-16-01713),[15](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B15-agriculture-16-01713)]. Neither approach prioritizes height as a primary design driver, leaving a gap between hardware taxonomies and the crop-driven spatial constraints that fundamentally shape manipulator architecture.\n\nThis gap is significant because a robot’s operational efficiency depends on how well its manipulator adapts to specific crop growth patterns and height distributions [[16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B16-agriculture-16-01713)]. A mismatch between workspace and fruit spatial distribution leads to “dead zones” and reduced coverage.\n\nTo address this gap, a literature search was conducted in Web of Science, Scopus, and IEEE Xplore using keywords “greenhouse harvesting robot”, “harvesting manipulator”, “fruit picking”, “crop height”, and “agricultural robot”. The search covered papers published between 2010 and 2026, up to May 2026. Only peer-reviewed journal articles and conference proceedings describing physical prototypes or validated simulation studies of harvesting manipulators for greenhouse crops were included. Duplicate records were identified and removed using reference management software. “Reports not retrieved” refers to articles for which the full text could not be obtained through the Web of Science, Scopus, or IEEE Xplore databases, whereas “Full text unavailable” refers to conference abstracts without full manuscript publications. From the retrieved literature, a keyword co-occurrence network was derived using VOS viewer, as shown in [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#fig_body_display_agriculture-16-01713-f001).\n\n**Figure 1.** VOS viewer keyword co-occurrence network.\n\nFollowing the PRISMA 2020 guidelines, a total of 88 papers were selected from the initial 342 for detailed analysis based on relevance to height-dependent design. The inclusion and exclusion criteria are listed in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#table_body_display_agriculture-16-01713-t001), and the PRISMA flow diagram is presented in [Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#fig_body_display_agriculture-16-01713-f002). Relevance was defined as papers that explicitly reported manipulator design parameters related to crop height or presented experimental results for a specific height layer, i.e., ground-level, medium-height, or high-wire. In addition, 16 references were cited as background material but were not included in the systematic review. The literature screening was conducted independently by two reviewers. The inter-rater agreement was substantial, with a Cohen’s κ of approximately 0.85. Any disagreements regarding paper inclusion were resolved through discussion with a third senior researcher to reach a consensus.\n\n**Figure 2.** PRISMA 2020 flow diagram. Note: Five records were automatically excluded by the database search engines during the initial retrieval process, as they did not match the predefined search strings and were deemed irrelevant to the review topic.\n\n**Table 1.** Inclusion and exclusion criteria (PRISMA 2020).\n\nBased on this curated corpus, plant height is adopted as the primary classification criterion. A height-based stratification of greenhouse harvesting manipulators is proposed, spanning from ground-level crops such as strawberries to high-wire crops such as trellised cucumber. This review focuses on three height layers, namely ground-level, medium-height, and high-wire. Below-soil crops are excluded from this classification because they are rarely harvested by dedicated greenhouse robots due to different working principles and perception challenges; a brief discussion is provided in [Section 2.4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#sec2dot4-agriculture-16-01713). To substantiate the proposed classification, a comparative analysis of the primary design specifications imposed by each height layer is presented in [Figure 3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#fig_body_display_agriculture-16-01713-f003). The analysis reveals that growth height does not merely influence a single parameter but systematically constrains the entire manipulator architecture, from kinematic configuration to perception strategy. For example, perception algorithms developed for occlusion handling in medium-height crops may be adapted to high-wire crops after suitable retraining. By using height as an integrative variable that encapsulates multiple design-critical factors, a more holistic framework is provided for the design and evaluation of greenhouse harvesting manipulators. This framework highlights both cross-layer opportunities and barriers to technology transfer.\n\n**Figure 3.** The height distribution of the greenhouse crops’ fruit.\n\n## 2. State of the Art in Height-Based Stratification of Harvesting Manipulators\n\nGreenhouse harvesting manipulators are reviewed here according to crop growth height. Three height layers are defined, based on the classification established in the abstract and [Table 2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#table_body_display_agriculture-16-01713-t002): ground-level crops (\u003C0.6 m, e.g., strawberry), medium-height crops (0.6–1.5 m, e.g., tomato and cucumber), and high-wire crops (>1.5 m, e.g., trellised grapes). The classification logic is directly linked to the technical requirements imposed by each layer. The parameter ranges in [Table 2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#table_body_display_agriculture-16-01713-t002) are derived from a qualitative synthesis of the reviewed literature to illustrate general design trends, not as precise specifications. Payload typically includes the end-effector and fruit, positioning accuracy refers to static repeatability as reported in the original studies, generally excluding mobile-platform-induced errors unless explicitly stated.\n\n**Table 2.** Comparative analysis of design requirements across height categories.\n\nThese specific height thresholds (0.6 m and 1.5 m) are proposed based on a systematic synthesis of the crop height distributions reported in the 88 reviewed studies, combined with standard greenhouse cultivation practices and typical kinematic workspace limitations of agricultural manipulators. The 0.6 m boundary corresponds to the maximum canopy height of ground-level crops such as strawberries in ridge or table-top cultivation systems, above which fruits are no longer ground-accessible and require different manipulator configurations. The 1.5 m boundary separates medium-height crops from high-wire crops, corresponding to the practical reach limit of conventional multi-purpose manipulators; crops exceeding this height typically require gantry-mounted or long-reach arms with extended workspaces. These thresholds are therefore presented as a research-derived classification framework rather than a citation of any single external standard.\n\nAs crop height increases, the manipulator must contend with a progressive shift in working zone orientation (from downward to overhead), an expanding reach requirement that amplifies positioning errors through joint compliance [[11](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B11-agriculture-16-01713),[12](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B12-agriculture-16-01713)], and a transition in perception complexity (from ground-level to canopy-penetrating 3D localization) [[13](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B13-agriculture-16-01713)]. These systematic trends validate growth height as a functionally meaningful axis for organizing manipulator technologies.\n\nThe specific design imperatives for each height layer are as follows:\n\nGround-level crops (e.g., strawberries): Manipulators with a small working radius, high-precision positioning, and damage-resistant end-effectors are needed. These requirements are driven by compact growth (dense planting, narrow aisles) and fragile fruits [[17](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B17-agriculture-16-01713),[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B18-agriculture-16-01713),[19](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B19-agriculture-16-01713),[20](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B20-agriculture-16-01713)].\n\nMedium-height crops (e.g., tomatoes, cucumbers): Manipulators that balance flexibility, load capacity, and crop-specific handling (e.g., gentle grasping for tomatoes) are required. This is attributed to varied fruit positions, diverse morphologies, and moderate occlusion [[21](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B21-agriculture-16-01713),[22](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B22-agriculture-16-01713),[23](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B23-agriculture-16-01713)].\n\nHigh-wire crops (e.g., trellised grapes): Manipulators with extended working ranges, stable high-altitude performance, and branch or trellis navigation capabilities are required. These features address long-reach needs, complex canopy occlusion, and high fruit positions [[12](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B12-agriculture-16-01713),[24](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B24-agriculture-16-01713),[25](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B25-agriculture-16-01713),[26](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B26-agriculture-16-01713)].\n\nEach of the following three sections, [Section 2.1](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#sec2dot1-agriculture-16-01713), [Section 2.2](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#sec2dot2-agriculture-16-01713) and [Section 2.3](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#sec2dot3-agriculture-16-01713) follows a consistent format. First, the defining characteristics of the height layer are described. Next, the influence of growing conditions and plant morphology on manipulator design is examined. Then, current research is summarized, and the performance and limitations of key robotic prototypes are evaluated. By organizing the content by crop height, specific design needs for each layer are clarified, and both the technical differences between layers and the common challenges faced across them are highlighted. Below-soil crops ([Section 2.4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#sec2dot4-agriculture-16-01713)) are only included for comparative reference and boundary definition, and do not belong to the three core height layers of this review.\n\n### 2.1. Ground-Level Crops (\u003C0.6 m)\n\nStrawberries are a representative example of ground-level crops, which hold significant economic value in greenhouse cultivation due to their high market demand. However, the automated harvesting of such low-stature crops has long been considered technically challenging because it requires a delicate balance among operational efficiency, fruit integrity, and adaptability to complex growing environments [[17](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B17-agriculture-16-01713),[18](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B18-agriculture-16-01713)]. These difficulties stem from several common traits: compact planting layouts, narrow operational aisles, short and fragile stems, and soft fruits with thin peels that are prone to bruising. As a result, harvesting manipulators for ground-level crops must feature a small working radius, high-precision positioning, and damage-resistant end-effectors, while their overall size and mobility must be optimized to suit confined greenhouse spaces.\n\n**Occlusion handling and active removal.** A major obstacle in strawberry harvesting is the dense foliage that often occludes the target fruit. To address this, several researchers have integrated active occlusion-removal mechanisms into their manipulators. In the following comparisons, “success rate” is broadly defined as the ratio of successfully harvested fruits to total attempts, while “harvesting success rate” refers to the harvesting success rate of the specific operation (e.g., the proportion of successfully picked fruits relative to total targets). As shown in [Figure 4](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#fig_body_display_agriculture-16-01713-f004), He et al. [[17](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B17-agriculture-16-01713)] equipped a fan-integrated end-effector that blows away obstructive leaves after the vision system classifies strawberries into occluded and non-occluded categories, raising the harvesting success rate from 58.1% to 73.9%. Similarly, in the dual-arm system developed by Liu et al. [[27](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F16\u002F1713#B27-agriculture-16-01713)], a brushless ducted fan is mounted on one arm to clear leaves while the other arm performs picking. More comprehensively, Parsa et al. ","Agriculture 2026-08-11","2026-08-10T16:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,13,7,1,"综述性论文，按高度分层梳理温室采摘机器人，提供设计框架，对智慧农业有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"采摘机器人","温室","高度分层","Agriculture","中国计量大学",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,56,66],{"code":32,"score":50,"supported":51,"title":52,"abstract":54,"tags":55},11,true,[53],"greenhouse",[27],[],{"code":39,"score":57,"supported":51,"title":58,"abstract":59,"tags":65},9,[],[60,61,62,63,64],"番茄","辣椒","黄瓜","生菜","草莓",[],{"code":67,"score":21,"supported":14,"title":68,"abstract":69,"tags":70},"F6",[],[],[26],[72,73],"中国计量大学 采摘机器人 高度分层 温室","中国计量大学 采摘机器人","中国计量大学采摘机器人高度分层温室-513",6,"2026-08-13T23:53:47.499847Z",{"total":75,"page":21,"page_size":75,"items":78},[79,134,235,282,324,363],{"id":80,"title":81,"url":82,"summary":83,"summary_zh":8,"content":8,"source_name":84,"source_url":8,"published_at":85,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":86,"score_detail":87,"sources":92,"tags":94,"primary_topic":100,"secondary_topics":101,"classification":103,"search_phrases":119,"slug":122,"view_count":123,"doi":8,"paper":124,"created_at":133},5536,"日光温室番茄全生育期环境特征解析与智能调控决策支持方法","https:\u002F\u002Fpaper.sciencenet.cn\u002Fhtmlpaper\u002F2026\u002F10\u002F20261021145441802156759.shtm","研究解析日光温室番茄全生育期环境特征，发现该类温室气候缓冲能力较强、有效积温与番茄生理需求呈双峰匹配，但相对增益指标揭示单位时间热量供给失衡、深冬热量不足以及光能结构性不足、湿度调控昼夜失衡等问题。作者建议构建温度—光照—湿度耦合的智能闭环控制系统。","Agriculture 2026-10-21","2026-10-21T00:00:00Z",77,{"impact":88,"substance":89,"depth":90,"authority":19,"freshness":13,"relevant":21,"comment":91},16,21,17,"论文解析日光温室番茄全生育期环境特征并提出温光湿耦合智能调控方案，结论有新意，对设施农业信息化有参考价值。",[93],{"name":84,"url":82},[95,96,97,98,99],"智慧农业","设施农业","环境调控","温室番茄","智能决策",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[102],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":104},{"candidates":105,"agricultural_evidence":51},[106,110,114],{"code":32,"score":19,"supported":51,"title":107,"abstract":108,"tags":109},[27],[27],[98,97],{"code":39,"score":50,"supported":51,"title":111,"abstract":112,"tags":113},[60],[60],[],{"code":115,"score":21,"supported":14,"title":116,"abstract":117,"tags":118},"I1",[],[],[95],[120,121],"日光温室 番茄 环境调控","温室番茄 智能闭环控制","日光温室番茄环境调控-5536",0,{"doi":8,"openalex_id":8,"authors":125,"venue":8,"cited_by_count":123,"oa_url":8,"card":126,"direction":130,"ingested_from":132},[],{"tldr":127,"method":128,"finding":129,"direction":130,"opportunity":131},"解析日光温室番茄全生育期环境特征并提出智能调控决策支持方法。","基于全生育期环境监测数据，分析积温匹配与相对增益指标。","温室缓冲力强但深冬热量不足、光能结构性不足、湿度昼夜失衡。","智慧农业 \u002F 农业物联网","可研发温度—光照—湿度耦合的智能闭环控制算法，并验证其对番茄产量与能耗的协同优化效果。","agent","2026-10-22T00:07:10.129353Z",{"id":135,"title":136,"url":137,"summary":138,"summary_zh":139,"content":8,"source_name":140,"source_url":137,"published_at":141,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":142,"score_detail":143,"sources":147,"tags":149,"primary_topic":154,"secondary_topics":155,"classification":163,"search_phrases":211,"slug":214,"view_count":123,"doi":8,"paper":215,"created_at":234},5313,"Real-Time IoT-Integrated Ensemble LSTM–Bi-LSTM Framework for Greenhouse Microclimate Forecasting","https:\u002F\u002Fwasrpublication.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F512","Accurate prediction of greenhouse microclimates is essential for optimizing crop growth and resource utilization in precision agriculture. The research presents an IoT-integrated ensemble deep learning framework for real-time greenhouse microclimate forecasting using Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Environmental variables, including temperature, light intensity, soil moisture, and humidity, were acquired through IoT sensor nodes deployed in a controlled greenhouse environment. A benchmark greenhouse dataset was first used for model development and hyperparameter optimization, while an original IoT-based dataset collected during cucumber, tomato, and pepper cultivation at the Federal University Wukari, Nigeria, was used for validation and real-world deployment. The data were preprocessed through noise filtering, missing value imputation, Z-score-based outlier removal, and Min–Max normalization. A weighted ensemble strategy was then applied to combine LSTM and Bi-LSTM predictions for improved robustness. Experimental results show that the Bi-LSTM model outperformed the LSTM model, achieving R² = 0.9981, MAE = 0.0088, RMSE = 0.0110, and MSE = 0.0001, compared to LSTM results of R² = 0.9956, MAE = 0.0133, RMSE = 0.0167, and MSE = 0.0003. Statistical validation using paired t-test, Wilcoxon signed-rank test, confidence intervals, and multi-run analysis confirmed that the performance improvement is statistically significant and robust. The proposed framework enables accurate real-time microclimate forecasting and intelligent environmental monitoring, providing a scalable solution for precision greenhouse management, improved crop productivity, optimized energy utilization, and efficient irrigation scheduling.","准确预测温室微气候对于精准农业中优化作物生长和资源利用至关重要。本研究提出了一种集成物联网的集成深度学习框架，利用长短期记忆网络（LSTM）和双向长短期记忆网络（Bi-LSTM）进行实时温室微气候预测。温度、光照强度、土壤湿度和湿度等环境变量通过部署在受控温室环境中的物联网传感器节点采集。研究首先使用基准温室数据集进行模型开发和超参数优化，随后使用在尼日利亚武卡里联邦大学黄瓜、番茄和辣椒种植期间采集的原始物联网数据集进行验证和实际部署。数据经过噪声滤波、缺失值插补、基于Z分数的异常值去除和最小—最大归一化预处理。然后采用加权集成策略融合LSTM和Bi-LSTM的预测结果以提高鲁棒性。实验结果表明，Bi-LSTM模型优于LSTM模型，其R² = 0.9981、MAE = 0.0088、RMSE = 0.0110、MSE = 0.0001，而LSTM的结果为R² = 0.9956、MAE = 0.0133、RMSE = 0.0167、MSE = 0.0003。采用配对t检验、Wilcoxon符号秩检验、置信区间和多轮运行分析进行的统计验证证实，性能提升具有统计显著性和鲁棒性。所提出的框架能够实现准确的实时微气候预测和智能环境监测，为精准温室管理、提高作物生产力、优化能源利用和高效灌溉调度提供了可扩展的解决方案。","World Journal of Multidisciplinary Studies","2026-10-16T00:00:00Z",67,{"impact":144,"substance":89,"depth":90,"authority":57,"freshness":145,"relevant":21,"comment":146},12,8,"方法扎实、验证充分，但属单点应用研究，公共影响有限，适合作为技术前沿条目而非头条精选。",[148],{"name":140,"url":137},[95,150,151,152,153],"农业人工智能","农业物联网","温室环境","小气候预测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[156,162],{"code":157,"slug":158,"name":159,"path":160,"parent_code":161},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":164},{"candidates":165,"agricultural_evidence":51},[166,173,178,184,190,197,204],{"code":157,"score":167,"supported":51,"title":168,"abstract":170,"tags":172},15,[169],"IoT",[171,169],"物联网",[151],{"code":32,"score":174,"supported":51,"title":175,"abstract":176,"tags":177},14,[53],[27,53],[],{"code":39,"score":57,"supported":51,"title":179,"abstract":180,"tags":183},[],[60,61,62,181,182],"tomato","cucumber",[],{"code":115,"score":20,"supported":51,"title":185,"abstract":186,"tags":189},[],[187,188],"精准农业","precision agriculture",[95],{"code":191,"score":20,"supported":51,"title":192,"abstract":193,"tags":196},"I2",[],[194,195],"深度学习","deep learning",[150],{"code":198,"score":75,"supported":51,"title":199,"abstract":200,"tags":203},"H2",[],[201,202],"土壤","soil",[],{"code":205,"score":75,"supported":51,"title":206,"abstract":207,"tags":210},"H3",[],[208,209],"灌溉","irrigation",[],[212,213],"温室 小气候 LSTM 预测","尼日利亚 温室 IoT 黄瓜 番茄","温室小气候LSTM预测-5313",{"doi":8,"openalex_id":216,"authors":217,"venue":140,"cited_by_count":123,"oa_url":8,"card":228,"direction":130,"ingested_from":233},"W7222909837",[218,220,222,224,226],{"name":219,"orcid":8},"Siman Emmanuel*",{"name":221,"orcid":8},"Edward Onyebueke Agu",{"name":223,"orcid":8},"Musa Gani",{"name":225,"orcid":8},"Oladunjoye John Abiodun",{"name":227,"orcid":8},"Anagu Emmanuel John",{"tldr":229,"method":230,"finding":231,"direction":130,"opportunity":232},"提出物联网集成LSTM与Bi-LSTM加权集成框架，实时预测温室微气候。","IoT传感器采集温光湿数据，经预处理后用LSTM-Bi-LSTM加权集成建模。","Bi-LSTM优于LSTM，R²达0.9981，统计检验证实性能提升显著稳健。","可探索边缘端轻量化部署与多作物跨温室迁移学习，提升实时预测泛化性。","openalex","2026-10-19T23:30:13.295236Z",{"id":236,"title":237,"url":238,"summary":239,"summary_zh":8,"content":240,"source_name":241,"source_url":8,"published_at":242,"category":243,"cover_url":8,"hotness":13,"is_selected":14,"score":244,"score_detail":245,"sources":248,"tags":250,"primary_topic":254,"secondary_topics":255,"classification":257,"search_phrases":277,"slug":280,"view_count":269,"doi":8,"paper":8,"created_at":281},4459,"中国农科院环发所构建知识与数据双驱动模型精准预测温室番茄生长态势","https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572492.shtm","中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队以番茄为对象，构建知识与数据双驱动的尺度自适应时序卷积混合模型，根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测；研究成果发表在Computers and Electronics in Agriculture；仅依靠常规温室环境数据即可无损估算生长态势，对推动温室智能环控、产量预测具重要应用价值。","[![Image 1: 科学网新闻频道](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnews.jpg)](https:\u002F\u002Fnews.sciencenet.cn\u002F)\n\n[生命科学](https:\u002F\u002Fwww.sciencenet.cn\u002Flife\u002F) | [医学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmedicine\u002F) | [化学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fchemistry\u002F) | [工程材料](https:\u002F\u002Fwww.sciencenet.cn\u002Fmaterial\u002F) | [信息科学](https:\u002F\u002Fwww.sciencenet.cn\u002Finformation\u002F) | [地球科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fearth\u002F) | [数理科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmathematics\u002F) | [管理综合](https:\u002F\u002Fwww.sciencenet.cn\u002Fpolicy\u002F)[站内规定](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-45-1064777.html) | [手机版](https:\u002F\u002Fwap.sciencenet.cn\u002F)\n\n[首页](https:\u002F\u002Fwww.sciencenet.cn\u002F) | [新闻](https:\u002F\u002Fnews.sciencenet.cn\u002F) | [博客](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php) | [院士](https:\u002F\u002Fnews.sciencenet.cn\u002Fys\u002F) | [人才](https:\u002F\u002Ftalent.sciencenet.cn\u002F) | [会议](https:\u002F\u002Fmeeting.sciencenet.cn\u002F) | [基金·项目](https:\u002F\u002Ffund.sciencenet.cn\u002F) | [论文](https:\u002F\u002Fpaper.sciencenet.cn\u002F) | [绘图](https:\u002F\u002Fvisual-web.sciencenet.cn\u002F) | [视频·直播](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=video) | [小柯机器人](https:\u002F\u002Fpaper.sciencenet.cn\u002FAInews) | [医学科普](https:\u002F\u002Fyxkp.sciencenet.cn\u002F)\n\n作者：李晨,王佳 来源：中国科学报 发布时间：2026\u002F10\u002F4 22:09:23 选择字号：小 中 大 双驱动模型可精准预测温室番茄生长态势 近日，中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队构建了知识与数据双驱动模型，实现温室番茄生长的高精度无损预测，相关研究成果发表在《农业计算机与电子学》（_**Computers and Electronics in Agriculture**_）上。 作物生长精准估算是温室智能控制的核心前提。然而，传统机理模型参数获取难、泛化有限，而纯数据驱动模型常因缺乏生理约束致预测失真。因此，如何融合环境时序数据与作物生长规律，实现高精度且生理合理的生长预测仍是关键挑战。 科研团队以番茄为对象，提出一种知识与数据双驱动的尺度自适应时序卷积混合模型。该模型根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测。该模型依靠常规温室环境数据即可无损估算生长态势，对推动温室智能环控、产量预测具有重要应用价值。 相关论文信息：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112281 版权声明：凡本网注明“来源：中国科学报、科学网、科学新闻杂志”的所有作品，网站转载，请在正文上方注明来源和作者，且不得对内容作实质性改动；微信公众号、头条号等新媒体平台，转载请联系授权。邮箱：shouquan@stimes.cn。\n\n打印 发E-mail给：\n\n以下评论只代表网友个人观点，不代表科学网观点。\n\n 无标题页 \n\n[![Image 2](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnewcomm.gif)](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtml\u002Fcomment.aspx?id=572492)\n\n相关新闻 相关论文\n*   1\n*   [《科学》（20261001出版）一周论文导读](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572487.shtm)\n\n*   2\n*   [2026年度湖南省科学技术奖提名工作启动](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572486.shtm)\n\n*   3\n*   [河南中央引导地方科技发展资金项目结项验收结果公示](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572485.shtm)\n\n*   4\n*   [江西组织开展2027年度自然科学基金项目申报工作](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F572484.shtm)\n\n*   5\n*   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[更多>>](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=recommend)\n\n[关于我们](http:\u002F\u002Fwww.sciencenet.cn\u002Faboutus.aspx) | [网站声明](http:\u002F\u002Fwww.sciencenet.cn\u002Fshengming.aspx) | [服务条款](http:\u002F\u002Fwww.sciencenet.cn\u002Ftiaokuang.aspx) | [联系方式](http:\u002F\u002Fwww.sciencenet.cn\u002Fcontact.aspx) | [举报](https:\u002F\u002Fwww.sciencenet.cn\u002Fjubao.aspx) | [中国科学报社](https:\u002F\u002Fstimes.sciencenet.cn\u002F)  \n[京ICP备07017567号-12](http:\u002F\u002Fwww.beian.miit.gov.cn\u002F)互联网新闻信息服务许可证10120230008 京公网安备 11010802032783  \nCopyright @ 2007-2026 中国科学报社 All Rights Reserved  \n地址：北京市海淀区中关村南一条乙三号 电话：010-62580783","科学网 中国农业科学院 2026-10-04","2026-10-04T00:00:00Z","报道",80,{"impact":246,"substance":89,"depth":90,"authority":174,"freshness":75,"relevant":21,"comment":247},22,"国家级科研机构在智慧农业领域的模型方法突破，方法新颖、结论可靠，对温室智能环控与产量预测有应用价值，值得进入每日精选。",[249],{"name":241,"url":238},[95,150,251,252,253],"作物生长模型","设施番茄","温室环控",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[256],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":258},{"candidates":259,"agricultural_evidence":51},[260,264,268,273],{"code":39,"score":144,"supported":51,"title":261,"abstract":262,"tags":263},[60],[60],[252],{"code":32,"score":50,"supported":51,"title":265,"abstract":266,"tags":267},[27],[27],[],{"code":191,"score":269,"supported":14,"title":270,"abstract":271,"tags":272},2,[],[],[251,150],{"code":115,"score":21,"supported":14,"title":274,"abstract":275,"tags":276},[],[],[95],[278,279],"中国农科院环发所 温室番茄 生长预测","知识与数据双驱动 番茄 干物质","中国农科院环发所温室番茄生长预测-4459","2026-10-09T00:04:56.060677Z",{"id":283,"title":284,"url":285,"summary":286,"summary_zh":8,"content":287,"source_name":288,"source_url":8,"published_at":242,"category":243,"cover_url":8,"hotness":13,"is_selected":14,"score":289,"score_detail":290,"sources":292,"tags":294,"primary_topic":297,"secondary_topics":298,"classification":300,"search_phrases":319,"slug":322,"view_count":21,"doi":8,"paper":8,"created_at":323},4259,"中国农科院构建知识与数据双驱动模型精准预测温室番茄生长","https:\u002F\u002Fwap.sciencenet.cn\u002Fmobile.php?cat=T&id=572492&mobile=1&type=detail","中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队提出知识与数据双驱动的尺度自适应时序卷积混合模型，根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测。成果发表在《Computers and Electronics in Agriculture》。","գйũҵѧԺũҵɳչоʩֲﻷ̴Ŷӹ֪ʶ˫ģͣʵҷĸ߾Ԥ⣬оɹڡũҵѧComputers and Electronics in Agricultureϡ\n\n׼ܿƵĺǰᡣȻͳģͲȡѡޣģͳȱԼԤʧ档ˣںϻʱɣʵָ߾Ԥǹؼս","科学网 2026-10-04",82,{"impact":246,"substance":89,"depth":90,"authority":174,"freshness":145,"relevant":21,"comment":291},"国家级科研机构在智慧种植领域提出知识数据双驱动模型，方法新颖、结论可靠，对温室作物精准管理有参考价值。",[293],{"name":288,"url":285},[95,150,98,295,296],"知识数据双驱动","生长预测",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[299],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":301},{"candidates":302,"agricultural_evidence":51},[303,307,311,315],{"code":39,"score":50,"supported":51,"title":304,"abstract":305,"tags":306},[60],[60],[],{"code":32,"score":57,"supported":51,"title":308,"abstract":309,"tags":310},[27],[],[98],{"code":115,"score":21,"supported":14,"title":312,"abstract":313,"tags":314},[],[],[95],{"code":191,"score":21,"supported":14,"title":316,"abstract":317,"tags":318},[],[],[150],[320,321],"中国农科院 温室番茄 生长预测","知识数据双驱动 模型 番茄","中国农科院温室番茄生长预测-4259","2026-10-06T00:05:01.795653Z",{"id":325,"title":326,"url":327,"summary":328,"summary_zh":8,"content":8,"source_name":329,"source_url":8,"published_at":330,"category":243,"cover_url":8,"hotness":13,"is_selected":14,"score":289,"score_detail":331,"sources":333,"tags":335,"primary_topic":336,"secondary_topics":337,"classification":339,"search_phrases":358,"slug":360,"view_count":361,"doi":8,"paper":8,"created_at":362},3935,"知识与数据双驱动赋能温室番茄生长精准预测——中国农科院环发所研究成果发表","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fkyhd\u002F613bca00d9e84cfeb977251a606af346.htm","近日，中国农业科学院农业环境与可持续发展研究所设施植物环境工程创新团队构建了知识与数据双驱动模型，实现温室番茄生长的高精度无损预测，研究成果发表在《农业计算机与电子学（Computers and Electronics in Agriculture）》上。科研团队提出一种尺度自适应时序卷积混合模型，根据番茄不同生育期动态调整时序特征提取尺度，结合机理模型合成数据预训练与生理阈值约束，实现番茄不同器官干物质及叶面积指数的精准预测。","中国农业科学院·2026年10月","2026-10-01T00:00:00Z",{"impact":17,"substance":246,"depth":17,"authority":167,"freshness":57,"relevant":21,"comment":332},"国家级科研机构在核心期刊发表的知识与数据双驱动温室番茄生长预测模型，方法新颖、结论可靠，对智慧农业与设施种植有实质参考价值。",[334],{"name":329,"url":327},[95,150,96,98,296],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[338],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":340},{"candidates":341,"agricultural_evidence":51},[342,346,350,354],{"code":32,"score":144,"supported":51,"title":343,"abstract":344,"tags":345},[27],[27],[98],{"code":39,"score":50,"supported":51,"title":347,"abstract":348,"tags":349},[60],[60],[],{"code":115,"score":21,"supported":14,"title":351,"abstract":352,"tags":353},[],[],[95],{"code":191,"score":21,"supported":14,"title":355,"abstract":356,"tags":357},[],[],[150],[278,359],"知识与数据双驱动 番茄 模型","中国农科院环发所温室番茄生长预测-3935",3,"2026-10-02T11:53:11.319506Z",{"id":364,"title":365,"url":366,"summary":367,"summary_zh":368,"content":8,"source_name":369,"source_url":366,"published_at":370,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":371,"score_detail":372,"sources":374,"tags":376,"primary_topic":381,"secondary_topics":382,"classification":388,"search_phrases":420,"slug":423,"view_count":21,"doi":424,"paper":425,"created_at":449},3814,"Optimizing biogas slurry and biochar co-application to enhance soil microenvironment, mitigate greenhouse gas emissions, and increase tomato yield in greenhouse systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1866425","To support agricultural carbon peaking and carbon neutrality goals, this study investigated the uncertainties surrounding greenhouse gas emissions and nutrient leaching associated with biogas slurry utilization. It systematically explored the mechanisms and mitigation pathways of co-applying biogas slurry, biochar, and chemical fertilizers for reducing greenhouse gas emissions and nutrient losses under intensive agricultural management characterized by excessive irrigation and fertilizer application. Under equivalent N, P, and K inputs and consistent irrigation levels, different substitution ratios of biogas slurry for chemical fertilizers were evaluated in a point-source underground irrigation system. Treatments included CF, FR, BS25, BS50, BS75, and BS100, with corresponding biochar treatments CF + C, FR + C, BS25 + C, BS50 + C, BS75 + C, and BS100 + C. Tomato yield, soil physical properties, microbial biomass, microbial gene copy numbers, and soil greenhouse gas emissions were measured. Key influencing factors and optimal management strategies were identified using multivariate statistical analysis and a multi-criteria decision approach. The results indicated that biogas slurry combined with biochar significantly improved the soil microenvironment in greenhouse tomato systems by enhancing soil temperature (ST), soil moisture content (SMC), and water-filled pore space (WFPS), especially during the flowering stage in both spring-summer and autumn-winter periods. Biochar also significantly increased microbial biomass carbon (MBC), while microbial gene copy numbers, including AOA, AOB, and mcrA, showed clear seasonal variation. Regarding greenhouse gas emissions, co-application of biogas slurry and biochar significantly affected CO 2 fluxes, particularly from days 8–119, promoting CO 2 emissions. Biochar effectively suppressed N 2 O emissions, promoted CH 4 emissions during days 1–7, and reduced CH 4 fluxes from days 8–119. The BS75 + C treatment significantly increased tomato yield to 151.34 t ha −1 , reduced global warming potential to 9491.43 kg ha −1 , and lowered greenhouse gas emission intensity. Compared with other treatments, BS75 + C consistently showed superior agronomic and environmental performance. Comprehensive analysis showed that ST, SMC, and MBC mainly influenced CO 2 emissions; ST, AOA, and AOB influenced N 2 O emissions; and mcrA, WFPS, and ST influenced CH 4 emissions. Multi-criteria decision analysis identified BS75 + C as the optimal strategy for maximizing yield while minimizing greenhouse gas emissions and improving biogas slurry utilization efficiency in sustainable greenhouse agriculture.","为支撑农业碳达峰与碳中和目标，本研究探讨了沼液利用过程中温室气体排放与养分淋失的不确定性，系统揭示了在过量灌溉与施肥的集约化农业管理条件下，沼液、生物炭与化肥配施减少温室气体排放和养分损失的机理与调控路径。在等氮、磷、钾投入及一致灌溉水平下，采用点源地下灌溉系统，评估了不同沼液替代化肥比例的效果。处理包括CF、FR、BS25、BS50、BS75和BS100，以及相应的生物炭处理CF + C、FR + C、BS25 + C、BS50 + C、BS75 + C和BS100 + C。测定了番茄产量、土壤物理性质、微生物生物量、微生物基因拷贝数及土壤温室气体排放。通过多元统计分析和多准则决策方法，确定了关键影响因素和最优管理策略。结果表明，沼液配施生物炭通过提高土壤温度(ST)、土壤含水量(SMC)和充水孔隙度(WFPS)，显著改善了温室番茄系统的土壤微环境，尤其在春夏季和秋冬季的开花期。生物炭还显著提高了微生物生物量碳(MBC)，而微生物基因拷贝数(包括AOA、AOB和mcrA)则表现出明显的季节性变化。在温室气体排放方面，沼液与生物炭配施显著影响CO₂通量，尤其在8—119天期间促进CO₂排放。生物炭有效抑制了N₂O排放，在1—7天促进CH₄排放，并在8—119天降低CH₄通量。BS75 + C处理显著提高了番茄产量至151.34 t ha⁻¹，将全球增温潜势降至9491.43 kg ha⁻¹，并降低了温室气体排放强度。与其他处理相比，BS75 + C在农艺和环境性能方面始终表现优越。综合分析表明，ST、SMC和MBC主要影响CO₂排放；ST、AOA和AOB影响N₂O排放；mcrA、WFPS和ST影响CH₄排放。多准则决策分析确定BS75 + C为最优策","Frontiers in Sustainable Food Systems","2026-09-29T00:00:00Z",78,{"impact":88,"substance":246,"depth":17,"authority":19,"freshness":57,"relevant":21,"comment":373},"该研究通过沼液与生物炭配施实现温室番茄增产与温室气体减排，方法系统、数据扎实，对农业绿色低碳发展具有参考价值。",[375],{"name":369,"url":366},[377,98,378,379,380],"生物炭","农业碳中和","沼液利用","减排增效",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[383,387],{"code":198,"slug":202,"name":384,"path":385,"parent_code":386},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":389},{"candidates":390,"agricultural_evidence":51},[391,395,399,403,410,414],{"code":32,"score":167,"supported":51,"title":392,"abstract":393,"tags":394},[53],[27,53],[98],{"code":198,"score":167,"supported":51,"title":396,"abstract":397,"tags":398},[202],[201,202],[377],{"code":39,"score":174,"supported":51,"title":400,"abstract":401,"tags":402},[181],[60,181],[],{"code":404,"score":75,"supported":51,"title":405,"abstract":406,"tags":409},"F3",[],[407,408],"施肥","fertilizer",[],{"code":205,"score":75,"supported":51,"title":411,"abstract":412,"tags":413},[],[208,209],[],{"code":415,"score":361,"supported":51,"title":416,"abstract":417,"tags":419},"H4",[],[418],"农业碳",[],[421,422],"沼液 生物炭 温室番茄","BS75 C 温室气体减排","沼液生物炭温室番茄-3814","10.3389\u002Ffsufs.2026.1866425",{"doi":424,"openalex_id":426,"authors":427,"venue":369,"cited_by_count":123,"oa_url":366,"card":443,"direction":447,"ingested_from":233},"W7214739741",[428,431,434,436,439,441],{"name":429,"orcid":430},"Qinglin Sa","https:\u002F\u002Forcid.org\u002F0009-0007-5248-566X",{"name":432,"orcid":433},"Jian Zheng","https:\u002F\u002Forcid.org\u002F0009-0003-5723-1950",{"name":435,"orcid":8},"Ke Zhang",{"name":437,"orcid":438},"Yan Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5950-8904",{"name":440,"orcid":8},"Xuqin FU",{"name":442,"orcid":8},"Shikun Sun",{"tldr":444,"method":445,"finding":446,"direction":447,"opportunity":448},"研究沼液与生物炭配施对温室番茄土壤微环境、温室气体排放及产量的影响。","等氮磷钾滴灌试验，设沼液替代化肥比例与生物炭处理，测产量、土壤及气体。","BS75+C处理番茄产量最高达151.34 t\u002Fha，全球增温潜势最低，减排增产效果最优。","农业绿色发展与碳","可探究不同气候区或土壤类型下沼液生物炭配施的长期固碳减排效应与微生物机制。","2026-09-30T23:30:07.179554Z"]