[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3977":3,"related-3977":97},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":63,"slug":66,"view_count":54,"doi":67,"paper":68,"created_at":96},3977,"Development and evaluation of a chain-groove lateral seedling feeding device based on modular stepping misalignment for multi-row transplanting of rapeseed bare-root seedlings","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112468","Development and evaluation of a chain-groove lateral seedling feeding device based on modular stepping misalignment for multi-row transplanting of rapeseed bare-root seedlings。Computers and Electronics in Agriculture","基于模块化步进错位的链槽式侧向投苗装置开发与评估：面向油菜裸根苗多行移栽。Computers and Electronics in Agriculture",null,"Computers and Electronics in Agriculture","2026-10-01T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,14,9,1,"核心期刊论文，提出链槽式模块化错位送苗装置，对多行油菜裸苗移栽机械化有实质方法创新，但属细分领域技术进展，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业机器人","精准农业","油菜","移栽机械",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,58],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":51},15,true,[49],"rapeseed",[30,49],[30],{"code":53,"score":54,"supported":14,"title":55,"abstract":56,"tags":57},"I1",2,[],[],[27,29],{"code":59,"score":22,"supported":14,"title":60,"abstract":61,"tags":62},"F6",[],[],[28],[64,65],"油菜裸苗移栽 链槽式送苗装置","Computers and Electronics in Agriculture 移栽","油菜裸苗移栽链槽式送苗装置-3977","10.1016\u002Fj.compag.2026.112468",{"doi":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":88,"oa_url":9,"card":89,"direction":93,"ingested_from":95},"W7215105622",[71,74,76,78,81,83,85],{"name":72,"orcid":73},"Hua Yuan","https:\u002F\u002Forcid.org\u002F0000-0001-7594-8718",{"name":75,"orcid":9},"Qingxi Liao",{"name":77,"orcid":9},"Zhengyu Liu",{"name":79,"orcid":80},"Zhe Liu","https:\u002F\u002Forcid.org\u002F0000-0003-2653-0408",{"name":82,"orcid":9},"Lin Li",{"name":84,"orcid":9},"Yitao Liao",{"name":86,"orcid":87},"Wenli Xiao","https:\u002F\u002Forcid.org\u002F0000-0002-8443-8987",0,{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"研发并评估链槽式侧向供苗装置，实现油菜裸根苗多行移栽的模块化步进错位供苗。","设计链槽侧向供苗机构，采用模块化步进错位方案，并进行样机试验评估。","该装置能实现多行裸根苗有序错位供苗，提升移栽效率与供苗质量。","智慧农业 \u002F 农业物联网","可结合视觉检测与伺服控制，研究供苗状态实时监测与自适应错位调节，提升多行移栽智能化水平。","openalex","2026-10-02T23:30:02.682513Z",{"total":98,"page":22,"page_size":98,"items":99},6,[100,151,247,317,393,451],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":9,"content":105,"source_name":106,"source_url":9,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":113,"tags":115,"primary_topic":121,"secondary_topics":122,"classification":123,"search_phrases":146,"slug":149,"view_count":98,"doi":9,"paper":9,"created_at":150},541,"Towards Smart Agricultural Water–Nitrogen Management: A Multi-Criteria Decision Framework for Rapeseed Production in Southwest China Using EWM-TOPSIS Model","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542","针对中国西南油菜水氮利用效率低、智能水肥装备落地难的问题,基于两季田间试验(2 个灌溉水平 ×3 个施氮水平 + 雨养对照)耦合 EWM-TOPSIS 多准则评价模型。结果显示 W2F2 处理(100% ETc 灌溉 + 300 kg N\u002Fha)在两季分别提升叶片光合速率 51.2% 和 50.8%,在 EWM-TOPSIS 综合评价中两季均居首(S1 季贴近度 0.742,S2 季 0.650),在产量、品质、资源利用与经济效益间达到最优平衡。研究为西南丘陵油菜智能水肥装备应用提供了基于作物生理响应的决策优化框架。","Logical Operator Operator\n\nSearch Text\n\nSearch Type\n\n_add\\_circle\\_outline_\n\n_remove\\_circle\\_outline_\n\n[![Image 1: agronomy-logo](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fjournals\u002Fagronomy-logo.png?010a42cd82f83819)](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Fagronomy)\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 Article\n\nby \nRun Xue\n\n 1,2[](mailto:xue106700@163.com), \nYue Jiang\n\n 2,*[](mailto:jy261715267@126.com), \nHong Li\n\n 1, \nImran Ali Lakhiar\n\n 1,3[![Image 2: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786617101)](https:\u002F\u002Forcid.org\u002F0000-0001-8590-4436) and \nJunjun Ran\n\n 4\n\n1\n\nResearch Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China\n\n2\n\nSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China\n\n3\n\nFaculty of Agricultural Engineering and Technology, Sindh Agriculture University, Tandojam 70060, Pakistan\n\n4\n\nCollege of Water Resource and Hydropower, Sichuan University, Chengdu 650065, China\n\n*\n\nAuthor to whom correspondence should be addressed.\n\nSubmission received: 15 July 2026 \u002F Revised: 7 August 2026 \u002F Accepted: 10 August 2026 \u002F Published: 12 August 2026\n\n## Abstract\n\nTo address the limited synergy between integrated water–fertilizer technologies and intelligent application equipment, as well as the low water–nitrogen use efficiency in rapeseed production systems in southwestern China—which together constrain the large-scale adoption of smart fertigation equipment—this study conducted a two-season field experiment using an integrated water–fertilizer application system. The experiment included two irrigation regimes (W1: 60% ET c; W2: 100% ET c) and three nitrogen rates (F1: 220, F2: 300, F3: 360 kg N ha−1), plus a rainfed control (CK), to quantify rapeseed responses to water–nitrogen interactions under an equipment-based fertigation framework. Results showed that water–nitrogen interactions significantly regulated rapeseed physiological processes, growth, yield formation, and resource-use efficiency. Compared with CK, appropriate water and nitrogen supply markedly enhanced PSII efficiency and overall energy conversion. In particular, W2F2 increased leaf photosynthetic rate by 51.2% and 50.8% across the two seasons. Water–nitrogen coupling also improved yield components such as branch number and thousand-seed weight, thereby increasing final yield. Although nitrogen partial factor productivity declined with increasing N rates, smart fertigation improved economic returns to varying degrees. EWM-TOPSIS results indicated that W2F2 consistently achieved the highest comprehensive performance across both seasons. By balancing yield, seed quality, resource efficiency, and economic benefit, this treatment represents the optimal strategy under intelligent fertigation systems. Overall, this study provides a decision-oriented optimization framework based on crop physiological responses under smart water–fertilizer equipment, offering practical guidance for intelligent fertigation deployment in rapeseed systems in southwestern China.\n\n## 1. Introduction\n\nRapeseed (Brassica napus L.) is an economically important global crop valued for edible oil production and multiple-purpose utilization [[1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B1-agronomy-16-01542),[2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B2-agronomy-16-01542),[3](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B3-agronomy-16-01542)]. In 2023, the global rapeseed planting area reached 43.45 million hectares. As the world’s second-largest producer, China contributes 17.96% of global rapeseed output [[4](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B4-agronomy-16-01542)]. Winter rapeseed is a major cash crop in southwest China [[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B5-agronomy-16-01542),[6](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B6-agronomy-16-01542)]. Nevertheless, its long growth cycle and high water requirement conflict with frequent seasonal drought in this region. The seasonal precipitation of approximately 200 mm during the rapeseed growing period cannot meet the crop water demand of 300–500 mm [[5](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B5-agronomy-16-01542)]. Drought stress thus severely restricts rapeseed yield, and supplementary irrigation is indispensable for local rapeseed production. Previous studies have demonstrated that over-winter supplementary irrigation improves the soil hydrothermal conditions and winter survival of rapeseed and mitigates spring drought damage [[7](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B7-agronomy-16-01542),[8](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B8-agronomy-16-01542),[9](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B9-agronomy-16-01542)]. However, rapeseed is sensitive to both water shortage and surplus. Water deficit inhibits root activity and nitrogen acquisition, whereas excessive irrigation degrades soil aeration, lowers nitrogen-use efficiency and triggers nitrogen leaching-related environmental risks. Accordingly, rational supplementary irrigation is critical to balance rapeseed water demand and seasonal drought in Southwest China.\n\nNitrogen serves as another essential nutrient for winter rapeseed. Optimal nitrogen supply facilitates dry-matter accumulation, photosynthetic performance, yield and quality formation [[10](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B10-agronomy-16-01542)]. Nevertheless, over-nitrogen application is widespread in local farming practice. Such fertilization patterns reduce nitrogen-use efficiency, raise nitrate leaching risks to groundwater, and increase lodging susceptibility, which eventually causes yield and quality losses [[11](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B11-agronomy-16-01542),[12](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B12-agronomy-16-01542),[13](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B13-agronomy-16-01542)]. In traditional winter rapeseed production in southwestern China, single-dimensional nitrogen or irrigation management cannot simultaneously achieve high yield, superior quality, high resource efficiency and environmental sustainability [[14](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B14-agronomy-16-01542)]. Driven by advances in smart agricultural hardware and precision water–fertilizer technologies, intelligent irrigation and variable-rate fertigation platforms offer promising solutions to improve water–nitrogen use efficiency for rainfed croplands [[15](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B15-agronomy-16-01542),[16](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B16-agronomy-16-01542)]. Such systems support timely, quantitative and site-specific water and nutrient supply, greatly departing from traditional extensive field management [[17](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B17-agronomy-16-01542),[18](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B18-agronomy-16-01542)]. Nevertheless, large-scale field deployment of these smart devices is still constrained by heterogeneous field conditions and the shortage of field-validated water–nitrogen regulation strategies. The mismatch between theoretical optimization and practical field performance further limits their popularization [[19](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B19-agronomy-16-01542),[20](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B20-agronomy-16-01542)]. Accordingly, it is necessary to consider the practical field adaptability of integrated smart fertigation equipment, and clarify rapeseed physiological, yield-quality and resource-use responses under equipment-driven water–nitrogen combinations, so as to develop practical precision management strategies.\n\nPreite et al. [[21](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B21-agronomy-16-01542)] integrated soil and environmental sensor data with short-term weather conditions to develop an intelligent irrigation management system, enabling automated and intelligent irrigation regulation. Chen et al. [[22](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B22-agronomy-16-01542)] combined the DSSAT crop model with distributed reinforcement learning techniques to establish a multi-source information-driven precision intelligent irrigation regulation system, which significantly improved cotton irrigation water-use efficiency and crop yield. However, existing intelligent irrigation and fertigation systems mainly rely on external information, such as soil moisture and meteorological conditions, for water–nitrogen regulation, while lacking water–nitrogen management strategies driven by crop physiological feedback. Under complex field conditions, environmental parameter-based water–nitrogen optimization strategies cannot fully capture real-time crop requirements, thereby limiting the precision application and broader adoption of intelligent irrigation and fertigation technologies. Nowadays, advanced crop intelligent monitoring technologies enable real-time acquisition of crop physiological status, providing reliable information for determining water and fertilizer input requirements in fertigation systems [[23](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B23-agronomy-16-01542),[24](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B24-agronomy-16-01542)]. Among these physiological indicators, chlorophyll fluorescence parameters reflect photosystem activity and stress levels, and can sensitively characterize the damage of abiotic stress to photosynthetic apparatus [[25](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B25-agronomy-16-01542),[26](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B26-agronomy-16-01542)]. Gas exchange parameters, such as photosynthetic rate, directly represent plant carbon assimilation capacity and growth status, serving as important physiological feedback signals for precision water–nitrogen decision-making [[27](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B27-agronomy-16-01542),[28](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B28-agronomy-16-01542)]. Nevertheless, multiple dimensions, including physiological responses, yield, quality, and resource-use efficiency, need to be comprehensively considered to identify optimal water–nitrogen management strategies rather than relying on single-indicator evaluation.\n\nThe entropy weight-TOPSIS (EWM-TOPSIS) is an objective multi-criteria decision-making tool that mitigates information redundancy and subjective weighting bias [[29](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B29-agronomy-16-01542),[30](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B30-agronomy-16-01542)]. In this framework, indicator weights are objectively quantified via entropy weighting according to experimental data variation, followed by TOPSIS calculation of distances to positive- and negative-ideal solutions to obtain relative closeness coefficients for treatment ranking. Widely adopted in crop water–fertilizer assessment, this method can reliably distinguish the comprehensive performance of different agronomic treatments [[31](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B31-agronomy-16-01542),[32](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#B32-agronomy-16-01542)]. Therefore, the objectives of this study are (1) to analyze how irrigation–nitrogen regimes regulated by integrated smart fertigation equipment affect rapeseed growth, chlorophyll fluorescence and seed quality, and to reveal the regulatory mechanism of equipment-mediated water–nitrogen interaction on crop growth and quality formation; (2) to evaluate water–nitrogen use efficiency as well as economic input–output performance across different water–fertilizer coupling treatments, and quantify resource-use and economic responses under equipment-based management; and (3) to establish an EWM-TOPSIS-based multi-criteria optimization framework for winter rapeseed under smart fertigation, and determine the optimal management strategy for green-efficient production in southwestern China. This work provides theoretical support and decision-making references for parameter tuning and large-scale application of intelligent irrigation and variable fertilization equipment in rapeseed planting.\n\n## 2. Materials and Methods\n\n### 2.1. Experimental Site\n\nThe field experiment was conducted over two consecutive winter rapeseed growing seasons from October 2024 to May 2026 at Wufangpo Village, Daying County, Sichuan Province, China (105°08′ E, 30°34′ N; 377 m above sea level). The experimental site is located in the central Sichuan Basin and is characterized by a subtropical humid monsoon climate, with a mean annual air temperature of 16.5 °C, annual precipitation of approximately 930 mm, annual sunshine duration of 1380 h, and a frost-free period of approximately 279 days. The experimental soil was classified as a silt loam according to the USDA soil texture classification, with a sand:silt:clay ratio of 34:58:8. Prior to the experiment, composite soil samples were collected from the 0–20 cm soil layer to determine the initial physicochemical properties. The soil had a bulk density of 1.37 g cm−3, a field water-holding capacity of 0.42 cm 3 cm−3, and a permanent wilting point of 0.19 cm 3 cm−3. The soil pH was 6.8, with a soil organic matter content of 21.1 g kg−1, total nitrogen of 0.91 g kg−1, available phosphorus of 8.71 mg kg−1, and available potassium of 147.8 mg kg−1.\n\n[Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#fig_body_display_agronomy-16-01542-f001) presents the long-term (1974–2023) monthly climatic characteristics of the experimental site based on data obtained from the National Meteorological Data Service Center, including monthly maximum (T max), mean (T avg), and minimum (T min) air temperatures, average relative humidity (RH avg), sunshine duration (S d), and precipitation (rain). The lowest temperatures occurred in January, while most precipitation was concentrated in April and May. In contrast, cumulative precipitation during the winter rapeseed growing period from November to March was only 101.6 mm, which is insufficient to satisfy the crop water requirement without supplemental irrigation. The daily variations in precipitation, maximum, mean, and minimum air temperatures, and relative humidity during the two experimental seasons are presented in [Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F16\u002F1542#fig_body_display_agronomy-16-01542-f002). Total precipitation during the winter rapeseed growing period was 187.6 mm and 245.4 mm in the first and second seasons, respectively. The corresponding mean daily air temperatures were 11.1 °C and 12.4 °C, while the average relative humidities were 89.9% and 88.5%, respectively. The cumulative solar radiation reached respective values of 1295.7 and 1285.1 MJ m−2 during the two growing seasons.\n\n**Figure 1.** Meteorological data from the experimental site over the past 50 years.\n\n**Figure 2.** Meteorological data from the experimental site in the S1 (**a**) and S2 (**b**) seasons.\n\n### 2.2. Experimental Design\n\nThe experimental crop management and basal fertilization practices were consistent with local farmers’ conventional methods. The rapeseed cultivar used in this study was the hybrid variety “Xianyou 188”. Seedlings were transplanted manually on 6 November 2024, and 4 November 2025, with corresponding harvest dates on 6 May 2025, and 2 May 2026. The experiment included two irrigation levels (W1: low irrigation, 60% ET c; W2: high irrigation, 100% ET c) and three nitrogen application rates (F1: 220 kg N ha−1; F2: 300 kg N ha−1; F3: 360 kg N ha−1), resulting in six water–nitrogen coupling treatments. In addition, a rainfed control without nitrogen fertilization (CK) was included, giving a total of seven treatments. Each treatment was replicated three times, resulting in 21 plots arranged in a randomized complete block design. The planting density of rapeseed is 83,333 plants per hectare, with uniform planting density across all treatments. Before transplanting, a uniform basal application of rapeseed-specific compound fertilizer (N:P 2 O 5:K 2 O = 25:10:10, total nutrients ≥ 40%) was applied at 720 kg ha−1, corresponding to 180 kg N ha−1. This was used to ensure sufficient nutrient availability during early seedling establishment and to promote vigorous root and shoot development. During the overwintering and bolting stages, irrigation and topdressing nitrogen were applied at a ratio of 4:6. The total topdressing nitrogen amounts for N1, N2, and N3 were 60, 120, and 180 kg N ha−1, respectively. All irrigation events were conducted using a sprinkler system under non-rainy conditions with wind speed below 3 m s−1 to minimize meteorological interference. Other field management practices, including pest control and weed management, were carried out uniformly following local conventional agronomic practices. The intelligent fertigation system used in this study was independently developed and manufactured by Jiangsu University. The system mainly consisted of a fertigation controller, fertilizer mixing tank, pipeline system, and intelligent control unit. The sensing module was integrated with multiple sensors to enable real-time acquisition of environmental information, including soil moisture content and meteorological conditions. These data were used to calculate crop evapotranspiration (ET c) and determine irrigation water requirements.\n\n### 2.3. Physiological Indicators\n\nThe chlorophyll fluorometer (ChlF) was monitored using a portable photosynthesis system (MultispeQ V2.0, PHOTOSYNQ INC, East Lansing, MI, USA). Saturation pulse method (PAM) technology was used to measure ChlF parameters, while simultaneously recording leaf temperature, photosynthetically active radiation (PAR), leaf thickness, SPAD, and environmental temperature and humidity in the field. The ChlF contains the maximum quantum yield of PSII (Fv\u002FFm), the actual quantum yield of PSII (Φ PSII), the relative photosynthetic electron transfer rate (ETR), the photochemical quenching (qP), and the non-photochemical quenching (NPQ). The Fv\u002FFm and Φ PSII represents the maximum and actual proportion of energy absorbed by the crop that can be used for photosynthetic processes, respectively. In the condition without any stress, the plant’s Fv\u002FFm is generally 0.84, which means that 84% of the energy is converted to the photosynthetic process. The qL represents the proportion of open-state photosystem II reaction centers and it reflects the level of photosynthetic activity. The NPQ represents the proportion of energy absorbed by photosystem II that is used to dissipate as heat, and the ability of plants to dissipate excess light energy as heat. When measuring leaf physiological parameters at different growth stages of rapeseed, representative functional leaves were selected. During the seedling stage, the second and third fully expanded true leaves at the top were selected; during the bolting stage, the third and fourth functional leaves below the main stem apex were selected; during the flowering stage, the first and second fully expanded leaves below the main inflorescence were selected; and during the silique development stage, the upper functional leaves adjacent to the siliques were measured. All measurements were conducted on sunny days between 9:00 and 11:00 a.m. and at night under no sunlight. For each growth stage, three plants were randomly selected from different treatments and arranged in a diagonal pattern to measure leaf ChlF parameters, avoiding leaves affected by disease, damage, or shading to ensure that the data accurately reflected the true photosynthetic physiological dynamics of the rapeseed. The quantum yield of regulated energy dissipation in photosystem II (Φ NPQ) represents the fraction of excitation energy absorbed by PSII that is dissipated as heat through regulated photoprotective mechanisms. The quantum yield of non-regulated energy dissipation in photosystem II (Φ NO) represents the fraction of excitation energy absorbed by PSII that is passively dissipated as heat and re-emitted","Agronomy","2026-08-11T22:00:00Z",80,{"impact":110,"substance":111,"depth":18,"authority":20,"freshness":98,"relevant":22,"comment":112},18,22,"研究提出基于EWM-TOPSIS的水氮智能管理决策框架，为西南油菜智能水肥一体化提供优化方案，方法新颖且数据详实，但影响范围限于区域细分领域。",[114],{"name":106,"url":103},[27,116,29,30,117,118,119,120],"农业物联网","丘陵山区","智能水肥","EWM-TOPSIS","中国西南",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":124},{"candidates":125,"agricultural_evidence":47},[126,130,137,141],{"code":33,"score":17,"supported":47,"title":127,"abstract":128,"tags":129},[49],[30],[30],{"code":131,"score":132,"supported":47,"title":133,"abstract":134,"tags":136},"H3",3,[],[135],"灌溉",[],{"code":53,"score":54,"supported":14,"title":138,"abstract":139,"tags":140},[],[],[27,29],{"code":142,"score":22,"supported":14,"title":143,"abstract":144,"tags":145},"I5",[],[],[116],[147,148],"农业物联网 丘陵山区 中国西南 智慧农业","农业物联网 丘陵山区","农业物联网丘陵山区中国西南智慧农业-541","2026-08-15T00:02:55.759543Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":9,"source_name":157,"source_url":154,"published_at":158,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":159,"score_detail":160,"sources":164,"tags":166,"primary_topic":170,"secondary_topics":171,"classification":178,"search_phrases":203,"slug":206,"view_count":22,"doi":207,"paper":208,"created_at":246},4445,"Soil intrinsic penetration (SIP) index: decoupling environmental noise from mechanical resistance to improve soybean yield prediction","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75118-7","Soil compaction is a critical global agricultural concern, acting as a severe physical barrier that restricts root system expansion and reduces crop productivity. However, conventional Soil Penetration Resistance (PR) is a transient metric dictated by instantaneous hydraulic states rather than a stable structural property, leading to misleading interpretations in precision agriculture. This study aimed to develop and validate the Moisture-Normalized Soil Intrinsic Penetration (SIP) Index to isolate persistent anthropogenic structural degradation from environmental noise. Data were compiled from five field campaigns (2020–2025) conducted on a Typic Hapludox in Chapadão do Sul, Brazil. Three distinct sensing technologies: electronic (reference); hydraulic; and impact penetrometers, were evaluated across four depth intervals (0.0–0.4 m). The SIP Index was derived using a power-law normalization approach (SIP = PR x SW α x Clay β ), with empirical coefficients optimized via a Differential Evolution algorithm to maximize Spearman correlations with soybean yield. Results consistently identified the 0.10–0.20 m layer as the critical functional compaction barrier (traffic pan) across all sensing systems. While raw PR from continuous-insertion sensors (electronic and hydraulic) showed negligible or counter-intuitive yield correlations (ρ ≈ -0.01 to 0.08), the SIP Index drastically enhanced predictive capacity, reaching ρ = -0.39 for the electronic sensor, representing a substantial shift from the negligible associations observed with raw data, and ρ = -0.41 for the hydraulic system. Impact penetrometers exhibited higher inherent stability against moisture variations, rendering the SIP correction less significant for that device. Ultimately, the SIP framework effectively filters out pedogenic and hydraulic noise, providing a robust anthropogenic signature for delineating management zones and identifying true mechanical constraints to crop performance.","土壤压实是全球农业面临的严峻问题，作为一种严重的物理障碍，限制根系扩展并降低作物生产力。然而，传统的土壤穿透阻力（PR）是一种瞬时指标，受即时水分状态支配，而非稳定的结构属性，这导致在精准农业中产生误导性解读。本研究旨在开发并验证水分归一化土壤固有穿透（SIP）指数，以将持久性人为结构退化与环境噪声分离。数据来源于2020至2025年间在巴西沙帕当杜苏尔一处典型强淋溶老成土上开展的五次田间试验。评估了三种不同的传感技术：电子（参考）、液压和冲击式穿透仪，涵盖四个深度区间（0.0–0.4 m）。SIP指数采用幂律归一化方法（SIP = PR × SW^α × Clay^β）推导，经验系数通过差分进化算法优化，以最大化与大豆产量的Spearman相关性。结果一致表明，在所有传感系统中，0.10–0.20 m土层均为关键功能性压实障碍（机械压实层）。连续插入式传感器（电子和液压）的原始PR与产量相关性可忽略或呈反直觉关系（ρ ≈ -0.01至0.08），而SIP指数大幅提升了预测能力，电子传感器达到ρ = -0.39，相较于原始数据的可忽略关联实现了显著转变，液压系统达到ρ = -0.41。冲击式穿透仪对水分变化表现出更高的固有稳定性，因此SIP校正对该设备意义较小。最终，SIP框架有效滤除了成土和水分噪声，为划定管理分区和识别作物生长的真实机械限制提供了稳健的人为信号。","Scientific Reports","2026-10-06T00:00:00Z",76,{"impact":46,"substance":111,"depth":110,"authority":161,"freshness":162,"relevant":22,"comment":163},13,8,"提出水分归一化土壤内在穿透指数，用三种传感技术跨五年田间数据验证，显著提升大豆产量预测能力，方法新颖且对精准农业管理分区有实用价值。",[165],{"name":157,"url":154},[27,29,167,168,169],"土壤传感","土壤压实","大豆产量",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[172],{"code":173,"slug":174,"name":175,"path":176,"parent_code":177},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":179},{"candidates":180,"agricultural_evidence":47},[181,187,192,197],{"code":33,"score":20,"supported":47,"title":182,"abstract":184,"tags":186},[183],"soybean",[185,183],"大豆",[],{"code":173,"score":20,"supported":47,"title":188,"abstract":189,"tags":191},[174],[190,174],"土壤",[],{"code":53,"score":162,"supported":47,"title":193,"abstract":194,"tags":196},[],[29,195],"precision agriculture",[27,29],{"code":198,"score":132,"supported":47,"title":199,"abstract":200,"tags":202},"J3",[],[201],"全球农业",[],[204,205],"土壤穿透阻力 大豆产量","SIP 指数 土壤压实","土壤穿透阻力大豆产量-4445","10.1038\u002Fs41598-026-75118-7",{"doi":207,"openalex_id":209,"authors":210,"venue":157,"cited_by_count":88,"oa_url":239,"card":240,"direction":245,"ingested_from":95},"W7220671604",[211,214,216,219,222,225,228,231,233,236],{"name":212,"orcid":213},"Fábio Henrique Rojo Baio","https:\u002F\u002Forcid.org\u002F0000-0002-9522-0342",{"name":215,"orcid":9},"Job Teixeira de Oliveira",{"name":217,"orcid":218},"Cassiano Garcia Roque","https:\u002F\u002Forcid.org\u002F0000-0001-6872-0424",{"name":220,"orcid":221},"Ricardo Gava","https:\u002F\u002Forcid.org\u002F0000-0001-6268-5728",{"name":223,"orcid":224},"Larissa Pereira Ribeiro Teodoro","https:\u002F\u002Forcid.org\u002F0000-0002-8121-0119",{"name":226,"orcid":227},"Cid Naudi Silva Campos","https:\u002F\u002Forcid.org\u002F0000-0001-6810-885X",{"name":229,"orcid":230},"Rafael Felippe Ratke","https:\u002F\u002Forcid.org\u002F0000-0001-6930-3913",{"name":232,"orcid":9},"Octávio Babosa Plaster",{"name":234,"orcid":235},"Dthenifer Cordeiro Santana","https:\u002F\u002Forcid.org\u002F0000-0001-7611-6040",{"name":237,"orcid":238},"Paulo Eduardo Teodoro","https:\u002F\u002Forcid.org\u002F0000-0002-8236-542X","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75118-7_reference.pdf",{"tldr":241,"method":242,"finding":243,"direction":93,"opportunity":244},"提出土壤固有穿透指数SIP，剔除水分等环境噪声，提升大豆产量预测精度。","基于2020-2025年巴西五季田间数据，用幂律归一化与差分进化算法优化系数。","0.10-0.20米为关键压实层，SIP使电子与液压传感器产量相关性提升至-0.39和-0.41。","可探索SIP指数在不同土壤类型与作物上的普适性，并集成到实时传感与变量作业决策中。","数字乡村与农业信息化","2026-10-08T23:30:38.497161Z",{"id":248,"title":249,"url":250,"summary":251,"summary_zh":252,"content":9,"source_name":253,"source_url":250,"published_at":158,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":254,"score_detail":255,"sources":258,"tags":260,"primary_topic":264,"secondary_topics":265,"classification":266,"search_phrases":288,"slug":291,"view_count":88,"doi":292,"paper":293,"created_at":316},4325,"Precision agriculture reveals crop-yield footprints associated with distance to natural-seminatural habitats","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104997","Context Modern agricultural expansion has simplified landscapes and reduced natural and semi-natural habitats (NSNH), potentially disrupting ecological processes supporting crop production. Many of these processes are distance-dependent, potentially leaving spatial footprints in crop yield that vary with landscape context. Yet, their influence on crop yields at sub-field scales remains poorly understood. Precision agriculture offers opportunities to address this gap. Objective To evaluate how sunflower distance-to-NSNH - yield relationships vary across landscape contexts within contrasting landscape settings using precision agriculture data. Methods High-resolution yield-monitor data (∼700,000 observations) from 54 yield maps (Pampas, Argentina) were combined with landscape metrics derived from land-cover products, and distance-yield relationships were analysed using mixed-effects models. These relationships were evaluated across gradients of NSNH cover (landscape context) and between zones representing contrasting landscape settings. Results and conclusions Distance-yield relationships were non-linear, varied with landscape context, and differed between landscape settings. In simplified landscapes, yields declined beyond a few hundred meters from NSNH, with steeper declines under higher NSNH cover. In the more complex landscape setting, where NSNH were more abundant and distances to habitat were shorter, yield responses were weak across the observed NSNH gradient. These patterns may reflect distance-mediated ecological processes associated with NSNH, although their observational nature precludes attribution to specific mechanisms. Expanding the dataset across broader landscape gradients would strengthen the mechanistic interpretation of these patterns. Significance Precision agriculture can help identify crop-yield footprints consistent with distance-mediated ecological processes at sub-field scales, improving understanding of landscape-crop interactions and supporting ecologically informed landscape management.","背景 现代农业扩张使景观趋于简化，并减少了自然与半自然生境（NSNH），可能破坏了支撑作物生产的生态过程。其中许多过程具有距离依赖性，可能在作物产量中留下随景观背景而变化的时空印记。然而，这些过程在田块尺度以下对作物产量的影响仍知之甚少。精准农业为解决这一空白提供了机遇。目的 利用精准农业数据，评估在不同景观情境下，向日葵距自然与半自然生境的距离—产量关系如何随景观背景而变化。方法 将来自54幅产量图的高分辨率产量监测数据（约70万条观测值，阿根廷潘帕斯地区）与基于土地覆盖产品衍生的景观指标相结合，并采用混合效应模型分析距离—产量关系。这些关系在自然与半自然生境覆盖度梯度（景观背景）以及代表不同景观情境的区域之间进行了评估。结果与结论 距离—产量关系呈非线性，随景观背景而变化，并在不同景观情境之间存在差异。在简化景观中，产量在距自然与半自然生境数百米之外下降，且在自然与半自然生境覆盖度较高时下降更陡。在更为复杂的景观情境中，自然与半自然生境更为丰富且距生境距离更短，产量响应在所观测的自然与半自然生境梯度上较弱。这些格局可能反映了与自然与半自然生境相关的距离介导生态过程，尽管其观测性质使其无法归因于具体机制。将数据集扩展至更广泛的景观梯度将增强对这些格局的机制性解释。意义 精准农业有助于识别与田块尺度以下距离介导生态过程相一致的作物产量印记，增进对景观—作物相互作用的理解，并支持基于生态学信息的景观管理。","Agricultural Systems",79,{"impact":256,"substance":111,"depth":110,"authority":20,"freshness":21,"relevant":22,"comment":257},16,"基于70万条产量监测数据揭示田块尺度产量与自然栖息地距离的非线性关系，方法新颖、数据规模大，对生态化农田管理有参考价值。",[259],{"name":253,"url":250},[27,29,261,262,263],"遥感","作物产量","农业生态",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":267},{"candidates":268,"agricultural_evidence":47},[269,273,278,283],{"code":53,"score":256,"supported":47,"title":270,"abstract":271,"tags":272},[195],[29,195],[27,29],{"code":33,"score":132,"supported":47,"title":274,"abstract":275,"tags":277},[],[276],"向日葵",[],{"code":279,"score":54,"supported":14,"title":280,"abstract":281,"tags":282},"I3",[],[],[262,261],{"code":284,"score":22,"supported":14,"title":285,"abstract":286,"tags":287},"H6",[],[],[263],[289,290],"潘帕斯 向日葵 产量 自然栖息地","精准农业 产量监测 景观","潘帕斯向日葵产量自然栖息地-4325","10.1016\u002Fj.agsy.2026.104997",{"doi":292,"openalex_id":294,"authors":295,"venue":253,"cited_by_count":88,"oa_url":250,"card":310,"direction":314,"ingested_from":95},"W7220681058",[296,299,302,305,307],{"name":297,"orcid":298},"Facundo José Oddi","https:\u002F\u002Forcid.org\u002F0000-0003-4478-978X",{"name":300,"orcid":301},"Matías G. Goldenberg","https:\u002F\u002Forcid.org\u002F0000-0002-9498-2951",{"name":303,"orcid":304},"Paula Zermoglio","https:\u002F\u002Forcid.org\u002F0000-0002-6056-5084",{"name":306,"orcid":9},"Tomás González",{"name":308,"orcid":309},"Lucas A. Garibaldi","https:\u002F\u002Forcid.org\u002F0000-0003-0725-4049",{"tldr":311,"method":312,"finding":313,"direction":314,"opportunity":315},"利用精准农业产量数据揭示向日葵产量随距自然半自然生境距离变化的非线性关系。","54幅产量图约70万条监测数据，结合土地覆盖景观指标与混合效应模型。","简化景观中产量随距生境距离增加而下降，复杂景观中响应微弱。","农业遥感与作物表型","可扩展至多作物多区域，结合机制模型量化生境距离对产量的因果贡献。","2026-10-07T23:30:03.865642Z",{"id":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":9,"source_name":10,"source_url":320,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":323,"sources":325,"tags":327,"primary_topic":331,"secondary_topics":332,"classification":339,"search_phrases":358,"slug":361,"view_count":54,"doi":362,"paper":363,"created_at":392},3986,"YOLOv8-MST: a lightweight and efficient real-time detection and counting deep learning algorithm for double-grain peanut seeding","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112481","YOLOv8-MST: a lightweight and efficient real-time detection and counting deep learning algorithm for double-grain peanut seeding。Computers and Electronics in Agriculture","YOLOv8-MST：一种用于双粒花生播种的轻量高效实时检测与计数深度学习算法。《计算机与电子农业》",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":324},"核心期刊发表的轻量化实时检测计数算法，对花生精量播种智能化有实用价值，但属细分技术进展，影响面有限。",[326],{"name":10,"url":320},[27,328,329,29,330],"农业人工智能","目标检测","花生播种",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[333],{"code":334,"slug":335,"name":336,"path":337,"parent_code":338},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":340},{"candidates":341,"agricultural_evidence":47},[342,348,354],{"code":334,"score":256,"supported":47,"title":343,"abstract":345,"tags":347},[344],"deep learning",[346,344],"深度学习",[328,329],{"code":33,"score":20,"supported":47,"title":349,"abstract":351,"tags":353},[350],"peanut",[352,350],"花生",[],{"code":53,"score":54,"supported":14,"title":355,"abstract":356,"tags":357},[],[],[27,29],[359,360],"YOLOv8-MST 花生播种","双粒花生 实时检测计数","YOLOv8-MST花生播种-3986","10.1016\u002Fj.compag.2026.112481",{"doi":362,"openalex_id":364,"authors":365,"venue":10,"cited_by_count":88,"oa_url":9,"card":386,"direction":390,"ingested_from":95},"W7215133023",[366,369,371,373,375,377,379,381,384],{"name":367,"orcid":368},"Baogang Li","https:\u002F\u002Forcid.org\u002F0000-0002-3430-7508",{"name":370,"orcid":9},"Enxi Liu",{"name":372,"orcid":9},"Yijia Chen",{"name":374,"orcid":9},"Zhipeng Feng",{"name":376,"orcid":9},"Yuhuan Li",{"name":378,"orcid":9},"Zunmin Liu",{"name":380,"orcid":9},"Jingchun Tang",{"name":382,"orcid":383},"Jiang Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6746-9987",{"name":385,"orcid":9},"Fazhan Yang",{"tldr":387,"method":388,"finding":389,"direction":390,"opportunity":391},"提出YOLOv8-MST轻量算法，实现双粒花生播种实时检测与计数。","基于YOLOv8改进的轻量深度学习模型，用于花生播种图像检测计数。","YOLOv8-MST在保持高精度的同时实现轻量化实时检测与计数。","农业人工智能与决策模型","可探索轻量模型在多种作物播种质量实时监测中的泛化与边缘部署。","2026-10-02T23:30:03.835851Z",{"id":394,"title":395,"url":396,"summary":397,"summary_zh":398,"content":9,"source_name":10,"source_url":396,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":399,"sources":401,"tags":403,"primary_topic":407,"secondary_topics":408,"classification":409,"search_phrases":425,"slug":428,"view_count":88,"doi":429,"paper":430,"created_at":450},3983,"Numerical and experimental investigation on the cutting mechanism of rapeseed stalk using abrasive gas jet based on SPH-FEM coupling","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112469","Numerical and experimental investigation on the cutting mechanism of rapeseed stalk using abrasive gas jet based on SPH-FEM coupling。Computers and Electronics in Agriculture","基于SPH-FEM耦合的磨料气体射流油菜茎秆切割机理数值与实验研究。《计算机与电子农业》",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":400},"核心期刊论文，SPH-FEM耦合方法研究油菜秸秆磨料气体射流切割机理，方法有新意但属细分领域基础研究，产业影响有限。",[402],{"name":10,"url":396},[27,30,404,405,406],"数值模拟","农业机械","秸秆处理",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":410},{"candidates":411,"agricultural_evidence":47},[412,416,421],{"code":33,"score":46,"supported":47,"title":413,"abstract":414,"tags":415},[49],[30,49],[30],{"code":417,"score":22,"supported":14,"title":418,"abstract":419,"tags":420},"F5",[],[],[405],{"code":53,"score":22,"supported":14,"title":422,"abstract":423,"tags":424},[],[],[27],[426,427],"油菜秸秆 磨料气体射流 切割","SPH-FEM 耦合 秸秆切割","油菜秸秆磨料气体射流切割-3983","10.1016\u002Fj.compag.2026.112469",{"doi":429,"openalex_id":431,"authors":432,"venue":10,"cited_by_count":88,"oa_url":9,"card":445,"direction":390,"ingested_from":95},"W7215128671",[433,436,438,440,442],{"name":434,"orcid":435},"Mun-Sung Han","https:\u002F\u002Forcid.org\u002F0009-0000-4043-6982",{"name":437,"orcid":9},"Xiaokang Qin",{"name":439,"orcid":9},"Kai Yu",{"name":441,"orcid":9},"Yun Chen",{"name":443,"orcid":444},"Kaixiong Hu","https:\u002F\u002Forcid.org\u002F0000-0001-9904-6137",{"tldr":446,"method":447,"finding":448,"direction":390,"opportunity":449},"采用SPH-FEM耦合方法数值与实验研究磨料气体射流切割油菜茎秆的机理。","SPH-FEM耦合数值模拟结合切割实验，分析磨料气体射流切割过程。","揭示了磨料气体射流切割油菜茎秆的力学机制与切割参数影响规律。","可将SPH-FEM耦合仿真拓展至其他作物茎秆切割，并融合机器视觉实现切割参数智能优化。","2026-10-02T23:30:03.593073Z",{"id":452,"title":453,"url":454,"summary":455,"summary_zh":456,"content":9,"source_name":457,"source_url":454,"published_at":458,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":459,"score_detail":460,"sources":462,"tags":464,"primary_topic":468,"secondary_topics":469,"classification":470,"search_phrases":493,"slug":496,"view_count":497,"doi":498,"paper":499,"created_at":526},3786,"A cost-effective approach of building plant digital twin combining plant phenotyping and growth models: A case study on rapeseed plants","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128341","High-throughput phenotyping platforms enable dense temporal monitoring of plant growth, yet field-based image collection remains labor-intensive with sparse sampling intervals, leading to missing information on intermediate developmental stages. Functional–structural plant models (FSPMs) offer dynamic growth simulation but often lack realistic 3D architecture derived from actual plants. This study proposes a lightweight framework that bridges sparse phenotyping time points by integrating 3D reconstruction with the GreenLab plant growth model, enabling the construction of plant digital twins for rapeseed ( Brassica napus L.). Multi-angle smartphone images of four rapeseed cultivars at two key growth stages were used to reconstruct 3D plant architecture via 3D Gaussian Splatting (3DGS). Leaves were segmented from the point cloud to calculate area. GreenLab source and sink parameters were estimated from organ-level fresh weights (leaves, internodes, and roots) measured at two growth stages. The goodness–of–fit was high, with weighted sum of squares (WSS) between 5.63 and 9.33 and Pearson correlation coefficients ( r ) ranging from 0.93 to 0.95 across the four cultivars. The calibrated model reproduced leaf areas consistent with 3D reconstructed static structures. Reconstructed organ geometries were used for visualization and functional analysis of light interception. The integrated model thus recovered the complete dynamic development and growth trajectory between sparse sampling points. Temporal hold-out validation showed reasonable agreement between reconstructed leaf area and length–width–derived reference estimates, with a single-leaf mean absolute percentage error (MAPE) of 18.42%. Light interception analysis revealed cultivar-specific differences in canopy light use efficiency attributable to contrasting morphological architectures. Simulated and reconstructed canopy light interception showed strong agreement ( r = 0.98 ). This framework provides a cost-effective approach to reconstructing plant growth dynamics and evaluating light capture characteristics from minimal field data. It offers practical utility for breeders in cultivar evaluation and for optimizing environmental control strategies in precision agriculture.","高通量表型平台能够实现对植物生长的高密度时间监测，但田间图像采集仍然劳动密集且采样间隔稀疏，导致中间发育阶段的信息缺失。功能–结构植物模型（FSPMs）能够进行动态生长模拟，但通常缺乏源自实际植物的真实三维结构。本研究提出了一种轻量级框架，通过整合三维重建与GreenLab植物生长模型来桥接稀疏表型时间点，从而为甘蓝型油菜（Brassica napus L.）构建植物数字孪生。利用四个油菜品种在两个关键生长阶段的多角度智能手机图像，通过三维高斯泼溅（3DGS）重建植物三维结构。从点云中分割叶片以计算叶面积。基于两个生长阶段测定的器官水平鲜重（叶片、节间和根系）估计GreenLab源和库参数。拟合优度较高，四个品种的加权平方和（WSS）在5.63至9.33之间，皮尔逊相关系数（r）在0.93至0.95之间。校准后的模型再现的叶面积与三维重建的静态结构一致。重建的器官几何结构用于可视化和光截获功能分析。该集成模型由此恢复了稀疏采样点之间完整的动态发育和生长轨迹。时间留出验证表明，重建叶面积与基于长宽推导的参考估计之间具有合理的一致性，单叶平均绝对百分比误差（MAPE）为18.42%。光截获分析揭示了由于形态结构差异导致的品种特异性冠层光利用效率差异。模拟与重建的冠层光截获表现出高度一致性（r = 0.98）。该框架提供了一种经济有效的方法，可从最少的田间数据重建植物生长动态并评估光捕获特征。它为育种者进行品种评估以及优化精准农业中的环境调控策略提供了实用价值。","European Journal of Agronomy","2026-09-28T00:00:00Z",78,{"impact":46,"substance":111,"depth":110,"authority":20,"freshness":21,"relevant":22,"comment":461},"该研究提出结合3D高斯泼溅与GreenLab模型的低成本植物数字孪生框架，在油菜上验证可靠，对智慧育种与精准农业有实用价值，值得精选。",[463],{"name":457,"url":454},[27,30,465,466,467],"表型组学","作物生长模型","植物数字孪生",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":471},{"candidates":472,"agricultural_evidence":47},[473,479,483,489],{"code":53,"score":110,"supported":47,"title":474,"abstract":476,"tags":478},[475],"digital twin",[29,477,195],"数字孪生",[27],{"code":33,"score":46,"supported":47,"title":480,"abstract":481,"tags":482},[49],[30,49],[30],{"code":484,"score":132,"supported":47,"title":485,"abstract":486,"tags":488},"B1",[],[487],"甘蓝",[],{"code":334,"score":22,"supported":14,"title":490,"abstract":491,"tags":492},[],[],[466],[494,495],"油菜 植物数字孪生 表型","GreenLab 生长模型 油菜","油菜植物数字孪生表型-3786",4,"10.1016\u002Fj.eja.2026.128341",{"doi":498,"openalex_id":500,"authors":501,"venue":457,"cited_by_count":88,"oa_url":454,"card":521,"direction":314,"ingested_from":95},"W7214617633",[502,505,508,510,512,514,516,518],{"name":503,"orcid":504},"Xiujuan Wang","https:\u002F\u002Forcid.org\u002F0000-0001-8327-1735",{"name":506,"orcid":507},"Bowen Zheng","https:\u002F\u002Forcid.org\u002F0009-0008-1141-7158",{"name":509,"orcid":9},"Zhiqing Liu",{"name":511,"orcid":9},"Cuiping Bu",{"name":513,"orcid":9},"Jing Hua",{"name":515,"orcid":9},"Haoyu Wang",{"name":517,"orcid":9},"Qirun Huo",{"name":519,"orcid":520},"Mengzhen Kang","https:\u002F\u002Forcid.org\u002F0000-0002-5016-1772",{"tldr":522,"method":523,"finding":524,"direction":314,"opportunity":525},"结合手机3D重建与GreenLab生长模型，低成本构建油菜植株数字孪生。","多角度手机图像3D高斯泼溅重建，GreenLab源库参数标定与光截获分析。","模型拟合优度高（r=0.93-0.95），可恢复稀疏采样间生长动态并评估光截获。","可探索将轻量级数字孪生框架扩展至更多作物及田间高通量场景，降低表型监测成本。","2026-09-29T23:30:23.763490Z"]