[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5249":3,"related-5249":126},{"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":93,"slug":96,"view_count":97,"doi":98,"paper":99,"created_at":125},5249,"Strawberry (Fragaria × ananassa) cultivation in India: A critical review of status, constraints and future directions","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.15408","Strawberry (Fragaria × ananassa Duch.) is an economically important berry crop worldwide, due to its distinctive flavour, nutritional value and health benefits. In recent years, it has been cultivated not only in temperate regions, but also in subtropical and tropical regions. It has become one of the high-value horticultural crops in India, due to the growing consumer demand and good returns to farmers. This review critically examines the trends in yield, constraints and technological factors influencing strawberry cultivation across major strawberry producing states such as Maharashtra, Himachal Pradesh, Uttarakhand, Jammu and Kashmir and other emerging regions in India. Evidence from existing studies indicates that the adoption of improved cultivation practices such as balanced fertilisation, integrated pest management, mulching, protected cultivation and soilless systems has substantially improved fruit yield and quality compared to conventional open-field methods. Despite these advancements, productivity remains limited by climatic sensitivity, pest and disease factors, soil and nutrient management constraints and postharvest losses due to the highly perishable nature of the fruit. Moreover, disparities in technology adoption and the lack of cold-chain infrastructure continue to constrain sectoral growth. Emerging technologies such as precision agriculture and decision-support systems offer considerable potential to improve resource-use efficiency and sustainable production. The review discusses the status of strawberry production in India and its integration with production trends, challenges and future research in the field. It also points out gaps in the current knowledge and limited production systems and also outlines the future technological interventions needed to enhance the productivity, profitability and sustainability of strawberry production under Indian conditions.","草莓（Fragaria × ananassa Duch.）因其独特风味、营养价值和健康益处，成为全球范围内具有重要经济价值的浆果作物。近年来，草莓不仅在中温带地区种植，也在亚热带和热带地区得到栽培。由于消费者需求不断增长且种植者收益良好，草莓已成为印度高值园艺作物之一。本文综述批判性地考察了印度主要草莓产区（如马哈拉施特拉邦、喜马偕尔邦、北阿坎德邦、查谟和克什米尔以及其他新兴地区）的产量趋势、限制因素及影响草莓栽培的技术因素。现有研究证据表明，与传统的露地栽培方法相比，采用平衡施肥、病虫害综合防治、覆盖栽培、设施栽培和无土栽培等改良栽培措施已显著提高了果实产量和品质。尽管取得了这些进展，生产力仍受限于气候敏感性、病虫害因素、土壤和养分管理约束以及果实高度易腐性造成的采后损失。此外，技术采用程度的差异和冷链基础设施的缺乏继续制约着产业增长。精准农业和决策支持系统等新兴技术为提高资源利用效率和可持续生产提供了巨大潜力。本文讨论了印度草莓生产现状及其与生产趋势、挑战和未来研究的融合，指出了当前知识空白和生产体系的局限性，并概述了在印度条件下提高草莓生产力、盈利能力和可持续性所需的未来技术干预措施。",null,"Plant Science Today","2026-10-17T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,18,17,13,8,1,"系统综述印度草莓产业现状与制约，对国内草莓种植与设施农业有参考价值，但非本土资讯，时效与影响层级有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"设施栽培","病虫害防治","印度农业","草莓种植","无土栽培",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53,61,68,75,81,88],{"code":33,"score":46,"supported":47,"title":48,"abstract":50,"tags":52},14,true,[49],"strawberry",[51,49],"草莓",[],{"code":54,"score":55,"supported":47,"title":56,"abstract":57,"tags":60},"F3",6,[],[58,59],"施肥","养分管理",[],{"code":62,"score":55,"supported":47,"title":63,"abstract":64,"tags":67},"H2",[],[65,66],"土壤","soil",[],{"code":69,"score":55,"supported":47,"title":70,"abstract":71,"tags":74},"I1",[],[72,73],"精准农业","precision agriculture",[],{"code":76,"score":77,"supported":47,"title":78,"abstract":79,"tags":80},"B7",5,[],[31],[31,27],{"code":82,"score":83,"supported":47,"title":84,"abstract":85,"tags":87},"E3",3,[],[86],"冷链",[],{"code":89,"score":22,"supported":14,"title":90,"abstract":91,"tags":92},"J3",[],[],[29],[94,95],"印度 草莓 栽培","Fragaria ananassa 印度","印度草莓栽培-5249",0,"10.14719\u002Fpst.15408",{"doi":98,"openalex_id":100,"authors":101,"venue":10,"cited_by_count":97,"oa_url":6,"card":117,"direction":123,"ingested_from":124},"W7222796615",[102,105,108,111,114],{"name":103,"orcid":104},"A Dharshni","https:\u002F\u002Forcid.org\u002F0009-0002-8809-3557",{"name":106,"orcid":107},"C. P. Ravindran","https:\u002F\u002Forcid.org\u002F0000-0002-7514-4848",{"name":109,"orcid":110},"S. Muthulakshmi","https:\u002F\u002Forcid.org\u002F0009-0007-2071-9919",{"name":112,"orcid":113},"S. Shenbagavalli","https:\u002F\u002Forcid.org\u002F0000-0001-9344-277X",{"name":115,"orcid":116},"T Anitha","https:\u002F\u002Forcid.org\u002F0000-0003-0723-5948",{"tldr":118,"method":119,"finding":120,"direction":121,"opportunity":122},"综述印度草莓种植的产量趋势、制约因素与技术现状，并提出未来方向。","基于印度主产州文献证据，评述栽培技术、病虫害与采后损失等制约。","改良栽培与无土系统可提产提质，但气候敏感、冷链缺失与技术采纳不均仍限产。","智慧农业 \u002F 农业物联网","可研究面向小农户的低成本精准栽培与决策支持系统，并补齐采后冷链信息化短板。","农业人工智能与决策模型","openalex","2026-10-18T23:31:06.966374Z",{"total":55,"page":22,"page_size":55,"items":127},[128,204,277,345,400,452],{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":9,"source_name":134,"source_url":131,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":141,"tags":143,"primary_topic":148,"secondary_topics":149,"classification":150,"search_phrases":173,"slug":176,"view_count":177,"doi":178,"paper":179,"created_at":203},2278,"StrawberryGRO-E: A techno-economic analytic modeling framework for strawberry production in plant factories","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112405","Plant factories with artificial lighting (PFALs) offer a transformative approach to food security, yet their economic viability is constrained by high production expenditures and persistent yield heterogeneity. Despite uniform macro-environmental controls, endogenous genetic rhythms and micro-environmental fluctuations induce asynchronous inflorescence overlap which acts as a critical biological driver of output variation. This study aimed to quantify dynamic reproductive load and determine how inflorescence management affected physiological performance, resource efficiency, and economic outcomes in PFAL strawberry production. The Simultaneous Active Inflorescences (SAI) index was developed as a quantitative metric of dynamic reproductive load, and physiological assessments were conducted to characterize its effects on source-sink competition. These relationships were then incorporated into the StrawberryGRO-E techno-economic analytic modeling framework to evaluate three management strategies defined by maximum SAI thresholds: Conservative ( SA I max = 1), Moderate ( SA I max = 2), and Intensive ( SA I max = 3). Results demonstrated that despite individual physiological penalties associated with higher reproductive loads, the Intensive strategy recorded the lowest specific energy consumption of 83.7 kWh\u002Fkg, enhancing system-level energy efficiency by diluting fixed costs. However, economic performance was highly market-dependent. Moderate management strategy maximized net profit (1115.55 CNY\u002Fm 2 \u002Fyear) in quality-sensitive premium markets, whereas Intensive management strategy was most effective in volume-driven mass markets. Sensitivity analyses further identified profitability crossover points under fluctuating electricity prices and market volatility, with the Moderate strategy showing stronger resilience in quality-sensitive markets. This research confirms that inflorescence management, tailored to market positioning and resource expenditures, is essential for sustained annual profitability in PFALs.","人工光植物工厂（PFALs）为粮食安全提供了一种变革性途径，但其经济可行性受到高生产成本和持续产量异质性的制约。尽管宏观环境控制保持一致，内源性遗传节律和微环境波动仍会导致花序异步重叠，而后者是产量变异的关键生物驱动因素。本研究旨在量化动态生殖负荷，并确定花序管理如何影响PFAL草莓生产中的生理表现、资源利用效率和经济产出。研究开发了同步活跃花序（SAI）指数，作为动态生殖负荷的定量指标，并通过生理评估表征其对源库竞争的影响。随后，这些关系被纳入StrawberryGRO-E技术经济分析建模框架，用以评估以最大SAI阈值为依据的三种管理策略：保守型（SAI max = 1）、适度型（SAI max = 2）和密集型（SAI max = 3）。结果表明，尽管较高的生殖负荷会带来个体生理代价，密集型策略仍实现了最低的单位能耗83.7 kWh\u002Fkg，通过摊薄固定成本提升了系统级能源效率。然而，经济表现高度依赖市场。适度型管理策略在质量敏感型高端市场中实现了净利润最大化（1115.55 CNY\u002Fm²\u002F年），而密集型管理策略在销量驱动的大众市场中最为有效。敏感性分析进一步识别了在电价波动和市场波动条件下的盈利交叉点，其中适度型策略在质量敏感型市场中表现出更强的韧性。本研究证实，根据市场定位和资源投入来定制花序管理，对于PFAL实现持续年度盈利至关重要。","Computers and Electronics in Agriculture","2026-09-12T00:00:00Z",81,{"impact":18,"substance":138,"depth":18,"authority":46,"freshness":139,"relevant":22,"comment":140},22,9,"核心期刊论文，提出植物工厂草莓花序管理与技术经济分析框架，方法新颖、数据扎实，对设施农业盈利模式有参考价值。",[142],{"name":134,"url":131},[144,145,146,30,147],"智慧农业","设施农业","植物工厂","技术经济分析",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":151},{"candidates":152,"agricultural_evidence":47},[153,157,164,169],{"code":33,"score":46,"supported":47,"title":154,"abstract":155,"tags":156},[49],[51,49],[],{"code":158,"score":55,"supported":47,"title":159,"abstract":160,"tags":163},"A8",[],[161,162],"粮食安全","food security",[],{"code":76,"score":165,"supported":47,"title":166,"abstract":167,"tags":168},4,[],[146],[146],{"code":69,"score":22,"supported":14,"title":170,"abstract":171,"tags":172},[],[],[144],[174,175],"技术经济分析 智慧农业 植物工厂 草莓种植","技术经济分析 智慧农业","技术经济分析智慧农业植物工厂草莓种植-2278",2,"10.1016\u002Fj.compag.2026.112405",{"doi":178,"openalex_id":180,"authors":181,"venue":134,"cited_by_count":97,"oa_url":131,"card":198,"direction":123,"ingested_from":124},"W7212359284",[182,184,186,188,191,193,195],{"name":183,"orcid":9},"Rongmei Fu",{"name":185,"orcid":9},"Wei Liu",{"name":187,"orcid":9},"Fulin Xia",{"name":189,"orcid":190},"Renhai Zhong","https:\u002F\u002Forcid.org\u002F0000-0001-8922-0138",{"name":192,"orcid":9},"K.C. Ting",{"name":194,"orcid":9},"Guichao Hua",{"name":196,"orcid":197},"Tao Lin","https:\u002F\u002Forcid.org\u002F0000-0001-9721-5363",{"tldr":199,"method":200,"finding":201,"direction":123,"opportunity":202},"构建草莓植物工厂技术经济模型，量化花序管理对生理、能效与利润的影响。","提出同时活跃花序指数SAI，结合生理评估与StrawberryGRO-E技术经济","集约策略能耗最低83.7 kWh\u002Fkg，但中等策略在优质市场净利最高。","可扩展SAI指数至其他果菜，耦合实时传感与市场动态优化决策。","2026-09-13T23:30:01.554192Z",{"id":205,"title":206,"url":207,"summary":208,"summary_zh":209,"content":9,"source_name":210,"source_url":207,"published_at":211,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":212,"score_detail":213,"sources":217,"tags":219,"primary_topic":222,"secondary_topics":223,"classification":224,"search_phrases":243,"slug":246,"view_count":97,"doi":247,"paper":248,"created_at":276},1987,"Quantile normalisation enables interpretable spatial prioritisation of persistence-related mechanistic suitability under uncertainty for tomato red spider mite (Tetranychus evansi) in Africa","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffagro.2026.1840571","Mechanistic trait models are increasingly considered to estimate persistence-related suitability for invasive species, although translating reproduction metrics into operational indicators for spatial decision-making remains challenging. Branching-process transformations are widely applied to convert mechanistic reproduction numbers into probabilities of establishment; however, such transformations may compress spatial gradients when persistence is broadly feasible. Using tomato red spider mite ( Tetranychus evansi ) as a case study across Africa, this study developed an uncertainty-aware, physiologically-based model and generated spatially explicit estimates of expected reproduction (R 0 ). We compared three representations: raw mechanistic suitability, branching-process persistence probability, and quantile-normalised suitability. Although all representations preserved rank ordering, branching probability showed strong spatial saturation (>93% of pixels have P = 1 − 1 R 0 close to 1), reducing effective resolution for prioritisation. In contrast, quantile normalisation retained uniform spatial contrast and stable hotspot delineation under fixed-effort targeting. Our findings demonstrate that indicator choice fundamentally affects interpretability, and that rank-based transformations may provide more robust ecological indicators for spatial and spatiotemporal decision support under uncertainty.","机制性性状模型越来越多地被用于评估入侵物种的持续适应性，尽管将繁殖指标转化为空间决策的可操作指标仍具挑战性。分枝过程变换被广泛用于将机制性繁殖数转化为建立概率；然而，当持续存在广泛可行时，此类变换可能压缩空间梯度。本研究以番茄红蜘蛛（*Tetranychus evansi*）为非洲案例，开发了一种考虑不确定性的生理基础模型，并生成了预期繁殖量（R₀）的空间显式估计。我们比较了三种表征方式：原始机制性适宜性、分枝过程持续概率和分位数归一化适宜性。尽管所有表征均保持了秩次排序，但分枝概率表现出强烈的空间饱和（>93%的像元其P = 1 − 1\u002FR₀接近1），降低了优先排序的有效分辨率。相比之下，分位数归一化在固定投入目标下保持了均匀的空间对比度和稳定的热点区域划定。我们的研究结果表明，指标选择从根本上影响可解释性，基于秩次的变换可能为不确定条件下的空间和时空决策支持提供更稳健的生态指标。","Frontiers in Agronomy","2026-09-08T00:00:00Z",67,{"impact":214,"substance":18,"depth":18,"authority":214,"freshness":215,"relevant":22,"comment":216},12,7,"论文提出分位数归一化方法改进入侵物种适生区空间优先级排序，对农业病虫害精准防控有参考价值。",[218],{"name":210,"url":207},[144,220,221,28],"农业人工智能","非洲",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":225},{"candidates":226,"agricultural_evidence":47},[227,233,239],{"code":33,"score":46,"supported":47,"title":228,"abstract":230,"tags":232},[229],"tomato",[231,229],"番茄",[],{"code":234,"score":165,"supported":47,"title":235,"abstract":236,"tags":238},"I2",[],[237],"decision support",[220],{"code":69,"score":22,"supported":14,"title":240,"abstract":241,"tags":242},[],[],[144],[244,245],"农业人工智能 病虫害防治 智慧农业 非洲","农业人工智能 病虫害防治","农业人工智能病虫害防治智慧农业非洲-1987","10.3389\u002Ffagro.2026.1840571",{"doi":247,"openalex_id":249,"authors":250,"venue":210,"cited_by_count":97,"oa_url":270,"card":271,"direction":121,"ingested_from":124},"W7211971144",[251,254,257,260,263,265,268],{"name":252,"orcid":253},"Komi Agboka","https:\u002F\u002Forcid.org\u002F0000-0002-8287-6203",{"name":255,"orcid":256},"Luca Rossini","https:\u002F\u002Forcid.org\u002F0000-0003-2558-7111",{"name":258,"orcid":259},"Quinto Juma Meltus","https:\u002F\u002Forcid.org\u002F0009-0008-8712-0465",{"name":261,"orcid":262},"Bonoukpoè Mawuko Sokame","https:\u002F\u002Forcid.org\u002F0000-0001-5874-3243",{"name":264,"orcid":9},"Abdulmutallab G. A. Azrag",{"name":266,"orcid":267},"Thomas Dubois","https:\u002F\u002Forcid.org\u002F0000-0002-4865-843X",{"name":269,"orcid":9},"Elfatih M. Abdel-Rahman","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fagronomy\u002Farticles\u002F10.3389\u002Ffagro.2026.1840571\u002Fpdf",{"tldr":272,"method":273,"finding":274,"direction":123,"opportunity":275},"提出分位数归一化方法，改进非洲番茄红蜘蛛螨持久性适宜性的空间优先排序。","基于生理的机理模型，结合分支过程与分位数归一化，生成R0空间估计。","分位数归一化保持空间对比度，避免分支概率的空间饱和，提高决策可解释性。","可探索将分位数归一化应用于其他入侵物种或农业害虫的时空动态预测与优先防控区域划定。","2026-09-09T23:30:08.949411Z",{"id":278,"title":279,"url":280,"summary":281,"summary_zh":282,"content":9,"source_name":283,"source_url":280,"published_at":284,"category":12,"cover_url":9,"hotness":13,"is_selected":47,"score":285,"score_detail":286,"sources":289,"tags":291,"primary_topic":292,"secondary_topics":293,"classification":299,"search_phrases":319,"slug":321,"view_count":165,"doi":322,"paper":323,"created_at":344},1762,"Deep learning and computer vision framework for tomato insect classification with XAI insights","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-66844-z","Pest infestations pose a life-threatening challenge to global tomato production, often resulting in devastating yield losses and economic instability. Traditional manual identification practices are subjective, labor-intensive, and prone to error. To address this, within the paradigm of Agriculture 5.0, we introduce a novel hybrid deep learning model, ResNet50-Fusion (ViT), designed for the precise detection of six economically relevant tomato pests. The proposed architecture integrates a ResNet50 backbone with a Vision Transformer (ViT) branch via a Multi-Head Cross-Attention mechanism that facilitates asymmetric semantic alignment, enabling the simultaneous extraction of fine-grained morphological features and global contextual patterns. To ensure scientific rigor and eliminate the risk of data leakage, a ‘Split-then-Augment’ protocol was implemented, keeping the test set entirely independent and original. The framework achieved a state-of-the-art test accuracy of 97.00% ± 0.12%, significantly outperforming baseline models such as ResNet50 (96.44%) and DenseNet169 (96.00%), while demonstrating high computational efficiency with an average inference latency of 26.6 ms per image for real-time edge deployment. Beyond predictive accuracy, the model’s reliability was validated using a multi-method quantitative Explainable AI (XAI) framework integrating semantic indicators: Semantic Localization Score (SLS) for feature alignment, Robustness Score (RS) for logical stability, and Interpretability Reliability Coefficient (IRC) for decision consistency. Our results demonstrate a 96.5% Pointing Game Accuracy and a low Deletion AUC (0.142), indicating that the model’s attention patterns strongly align with biologically relevant morphological features rather than background artifacts. Systematic occlusion sensitivity analysis further confirmed model robustness with a stability score of 0.988. Finally, external validation on world-scale datasets, including IP102 and Pest24, yielded accuracies exceeding 90% via direct inference, demonstrating the generalizability of the framework to diverse automated field monitoring conditions. This research provides a precise, robust, and scalable solution for real-time pest identification in global precision agriculture.","虫害侵袭对全球番茄生产构成致命威胁，常导致毁灭性的产量损失和经济不稳定。传统的人工识别方法主观性强、劳动密集且易出错。为此，在农业5.0（Agriculture 5.0）范式下，我们提出了一种新颖的混合深度学习模型ResNet50-Fusion（ViT），专用于精准检测六种具有经济重要性的番茄害虫。该架构通过多头交叉注意力（Multi-Head Cross-Attention）机制整合ResNet50主干与视觉Transformer（Vision Transformer, ViT）分支，实现非对称语义对齐，从而能够同时提取细粒度形态特征与全局上下文模式。为确保科学严谨性并消除数据泄漏风险，我们实施了“先分割后增强”（Split-then-Augment）协议，使测试集保持完全独立且原始状态。该框架实现了97.00%±0.12%的最先进测试准确率，显著优于ResNet50（96.44%）和DenseNet169（96.00%）等基线模型，同时展现出高计算效率，平均推理延迟为每张图像26.6毫秒，适用于实时边缘部署。除预测精度外，模型的可靠性还通过整合语义指标的多方法定量可解释人工智能（Explainable AI, XAI）框架得到验证，包括用于特征对齐的语义定位分数（Semantic Localization Score, SLS）、用于逻辑稳定性的鲁棒性分数（Robustness Score, RS）以及用于决策一致性的可解释性可靠性系数（Interpretability Reliability Coefficient, IRC）。结果表明，我们的模型实现了96.5%的指向游戏准确率（Pointing Game Accuracy）和较低的删除AUC（0.142），说明模型的注意力模式与生物学相关的形态特征高度一致，而非背景伪影。系统性遮挡敏感性分析进一步证实了模型的鲁棒性，稳定性得分为0.988。最后，在包括IP102和Pest24在内的世界规模数据集上进行的外部验证，通过直接推理获得了超过90%的准确率，证明了该框架在多样化自动化田间监测条件下的泛化能力。本研究为全球精准农业中的实时害虫识别提供了一种精准、稳健且可扩展的解决方案。","Scientific Reports","2026-09-04T00:00:00Z",76,{"impact":18,"substance":138,"depth":287,"authority":46,"freshness":83,"relevant":22,"comment":288},19,"提出高精度番茄害虫识别模型，兼具可解释性与泛化能力，对精准农业有实质推进。",[290],{"name":283,"url":280},[144,220,231,28],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[294],{"code":234,"slug":295,"name":296,"path":297,"parent_code":298},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":300},{"candidates":301,"agricultural_evidence":47},[302,310,315],{"code":234,"score":303,"supported":47,"title":304,"abstract":307,"tags":309},23,[305,306],"deep learning","computer vision",[308,305],"深度学习",[220],{"code":33,"score":311,"supported":47,"title":312,"abstract":313,"tags":314},15,[229],[231,229],[231],{"code":69,"score":215,"supported":47,"title":316,"abstract":317,"tags":318},[],[72,73],[144],[320,245],"农业人工智能 病虫害防治 智慧农业 番茄","农业人工智能病虫害防治智慧农业番茄-1762","10.1038\u002Fs41598-026-66844-z",{"doi":322,"openalex_id":324,"authors":325,"venue":283,"cited_by_count":97,"oa_url":338,"card":339,"direction":121,"ingested_from":124},"W7208742661",[326,328,330,333,335],{"name":327,"orcid":9},"Mohammad Monirul Islam",{"name":329,"orcid":9},"Md. Anisur Rahman",{"name":331,"orcid":332},"Md. Masum Billah","https:\u002F\u002Forcid.org\u002F0000-0003-1881-4275",{"name":334,"orcid":9},"Morium Akter",{"name":336,"orcid":337},"Mohammad B. Uddin","https:\u002F\u002Forcid.org\u002F0000-0001-9665-7442","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-66844-z_reference.pdf",{"tldr":340,"method":341,"finding":342,"direction":123,"opportunity":343},"提出ResNet50-Fusion(ViT)混合深度学习模型，用于番茄害虫精准识别，准确率达97%。","ResNet50与ViT融合，多头交叉注意力，Split-then-Augmen","模型准确率97%，注意力与生物特征对齐，泛化性强，推理快，适合边缘部署。","可探索模型在更多作物害虫识别中的迁移，或结合边缘设备实现实时监测，并优化轻量化结构。","2026-09-06T23:30:09.870628Z",{"id":346,"title":347,"url":348,"summary":349,"summary_zh":350,"content":9,"source_name":351,"source_url":348,"published_at":352,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":353,"score_detail":354,"sources":356,"tags":358,"primary_topic":363,"secondary_topics":364,"classification":365,"search_phrases":382,"slug":385,"view_count":97,"doi":386,"paper":387,"created_at":399},5565,"A Unified Survival-Based Energy Framework Linking Plant Color, Stress, and Productivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23540961","Plant coloration, expressed through leaf greenness, floral pigmentation, and fruit color intensity, reflects the integrated outcome of pigment biosynthesis, optical absorption, metabolic regulation, and energy–mass exchange with the environment. While the biochemical pathways governing the synthesis and degradation of major pigment classes—including chlorophylls, carotenoids, and anthocyanins—are well established, existing models largely interpret color expression as a localized biochemical phenomenon. Such approaches do not explicitly account for whole-plant energy balance, transpiration-driven losses, or thermodynamic constraints, limiting their ability to explain widely observed patterns of color saturation, stress-induced fading, and species-specific stability across environmental gradients. Here we develop a unified framework for plant color generation and stability that treats pigmentation as a survival-limited energetic process embedded within the broader dynamics of absorption, transport, metabolism, and entropy production. Central to the framework is a dimensionless energy survival factor, Ψ=AE\u002FTE+ε where AE represents absorbed and retained energetic and elemental resources relevant to pigment formation, TE denotes transpiration-linked, metabolic, and environmental dissipation losses, and ε captures irreducible entropy-producing degradation inherent to non-equilibrium biological systems. By construction, 0\u003CΨ\u003C1, ensuring full compliance with the second law of thermodynamics. When combined with pigment-specific optical absorption coefficients and physiological limitation functions, the framework yields a physically interpretable description of both pigment concentration and color stability. Empirical ranges for absorbed photosynthetically active radiation (≈45–70%), excitation survival (≈85–95%), and respiratory and transpiration losses (≈30–60% of assimilated resources) naturally constrain Ψ, predicting observed limits on sustained pigment accumulation without invoking biochemical inefficiency or ad hoc correction factors. The analysis demonstrates that increased light availability or enhanced biosynthetic capacity alone does not intensify coloration unless absorbed resources survive transport and loss processes. Stress-induced chlorosis, transient anthocyanin accumulation, and saturation of greenness under high irradiance emerge as direct consequences of reduced energy survival rather than impaired pigment synthesis. By explicitly separating survival from synthesis, the framework reconciles molecular-scale pigment biochemistry with whole-plant and ecosystem-scale color dynamics. This survival-based formulation provides a unified physical basis for interpreting plant color variation across species and environments and offers a scalable foundation for crop stress diagnostics, remote-sensing interpretation, and comparative plant phenotyping. More broadly, it extends survival-limited energy principles to visible plant traits, advancing plant science beyond isolated biochemical descriptions toward a systems-level understanding of how energy governs phenotypic expression.","植物色泽通过叶片绿色度、花色苷着色和果实颜色强度体现，反映了色素生物合成、光学吸收、代谢调控以及与环境的能量-物质交换的综合结果。尽管主要色素类别——包括叶绿素、类胡萝卜素和花青素——的合成与降解生化途径已较为明确，但现有模型大多将颜色表达解释为局部生化现象。此类方法未明确考虑整株植物能量平衡、蒸腾驱动的损耗或热力学约束，限制了其解释广泛观察到的颜色饱和度、胁迫诱导褪色以及跨环境梯度的物种特异性稳定性等模式的能力。本文构建了一个统一的植物颜色生成与稳定性框架，将色素沉着视为一种受生存限制的能量过程，并将其嵌入吸收、运输、代谢和熵产生的更广泛动力学之中。该框架的核心是一个无量纲能量生存因子Ψ=AE\u002FTE+ε，其中AE代表与色素形成相关的被吸收并保留的能量和元素资源，TE表示与蒸腾相关的、代谢的和环境的耗散损失，ε则捕捉非平衡生物系统固有的不可约熵产生降解。根据构建方式，0\u003CΨ\u003C1，确保完全符合热力学第二定律。当与色素特异性光学吸收系数和生理限制函数相结合时，该框架可对色素浓度和颜色稳定性给出具有物理可解释性的描述。吸收性光合有效辐射（约45–70%）、激发能存活率（约85–95%）以及呼吸和蒸腾损失（约占同化资源的30–60%）的经验范围自然约束了Ψ，从而预测出所观察到的持续色素积累限度，而无需引入生化低效性或特设校正因子。分析表明，仅增加光照可用性或增强生物合成能力并不会加深颜色，除非被吸收的资源能够在运输和损失过程中存活下来。胁迫诱导的黄化、瞬时花青素积累以及高辐照下绿色度饱和，均表现为能量生存率降低而非色素合成受损的直接后果。通过明确分离生存","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",79,{"impact":18,"substance":138,"depth":18,"authority":214,"freshness":139,"relevant":22,"comment":355},"提出以能量生存因子统一解释植物颜色与胁迫的物理框架，方法新颖且对遥感解译和作物诊断有潜在价值，但属预印本理论模型，尚缺田间验证。",[357],{"name":351,"url":348},[144,359,360,361,362],"遥感监测","植物表型","作物胁迫诊断","能量平衡",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":366},{"candidates":367,"agricultural_evidence":47},[368,373,377],{"code":33,"score":83,"supported":47,"title":369,"abstract":370,"tags":372},[],[371],"萝卜",[],{"code":69,"score":22,"supported":14,"title":374,"abstract":375,"tags":376},[],[],[144],{"code":378,"score":22,"supported":14,"title":379,"abstract":380,"tags":381},"I3",[],[],[359],[383,384],"植物颜色 能量生存因子","作物胁迫 遥感诊断","植物颜色能量生存因子-5565","10.5281\u002Fzenodo.23540961",{"doi":386,"openalex_id":388,"authors":389,"venue":351,"cited_by_count":97,"oa_url":348,"card":393,"direction":397,"ingested_from":124},"W7223098474",[390],{"name":391,"orcid":392},"Mokhdum Azam Mashrafi","https:\u002F\u002Forcid.org\u002F0009-0002-1801-1130",{"tldr":394,"method":395,"finding":396,"direction":397,"opportunity":398},"提出基于生存的能量框架，用能量存活因子解释植物颜色生成与稳定性。","构建无量纲能量存活因子Ψ=AE\u002F(TE+ε)，结合色素吸收系数与生理限制函数。","颜色深浅取决于吸收资源经运输损耗后的存活率，而非单纯合成能力。","农业遥感与作物表型","可将Ψ因子与冠层光谱\u002F表型遥感结合，发展胁迫早期诊断与生产力预测模型。","2026-10-22T11:20:31.195819Z",{"id":401,"title":402,"url":403,"summary":404,"summary_zh":9,"content":9,"source_name":405,"source_url":9,"published_at":352,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":406,"score_detail":407,"sources":410,"tags":412,"primary_topic":416,"secondary_topics":420,"classification":422,"search_phrases":439,"slug":442,"view_count":97,"doi":9,"paper":443,"created_at":451},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",77,{"impact":17,"substance":408,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":409},21,"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。",[411],{"name":405,"url":403},[144,145,413,414,415],"环境调控","温室番茄","智能决策",{"code":76,"slug":417,"name":418,"path":419,"parent_code":37},"protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",[421],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":423},{"candidates":424,"agricultural_evidence":47},[425,430,435],{"code":76,"score":20,"supported":47,"title":426,"abstract":428,"tags":429},[427],"温室",[427],[414,413],{"code":33,"score":431,"supported":47,"title":432,"abstract":433,"tags":434},11,[231],[231],[],{"code":69,"score":22,"supported":14,"title":436,"abstract":437,"tags":438},[],[],[144],[440,441],"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",{"doi":9,"openalex_id":9,"authors":444,"venue":9,"cited_by_count":97,"oa_url":9,"card":445,"direction":121,"ingested_from":450},[],{"tldr":446,"method":447,"finding":448,"direction":121,"opportunity":449},"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。","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":453,"title":454,"url":455,"summary":456,"summary_zh":9,"content":9,"source_name":457,"source_url":9,"published_at":458,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":459,"score_detail":460,"sources":462,"tags":464,"primary_topic":469,"secondary_topics":470,"classification":471,"search_phrases":492,"slug":495,"view_count":97,"doi":9,"paper":496,"created_at":503},5533,"基于低成本遥操作机械臂模仿学习的草莓果柄定点采摘","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.22765","该研究构建视觉运动模仿学习流程，统一感知、运动规划与接触操作，从人工遥操作演示训练基于ResNet-50骨干的动作分块Transformer策略，并在Jetson AGX Orin边缘平台以15赫兹自主部署。策略在130条演示、十万梯度步内损失由2.84降至0.091，数据集与模型权重已开源。预印本，已被IROS农业机器人研讨会接收。","arXiv 预印本 2026-10-16","2026-10-16T00:00:00Z",68,{"impact":17,"substance":408,"depth":18,"authority":139,"freshness":165,"relevant":22,"comment":461},"方法新颖、数据与开源材料扎实，但属预印本且已过一周，适合作为细分技术进展收录而非头条精选。",[463],{"name":457,"url":455},[220,465,466,467,468],"农业机器人","边缘计算","模仿学习","草莓采摘",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":472},{"candidates":473,"agricultural_evidence":47},[474,478,483,487],{"code":33,"score":21,"supported":47,"title":475,"abstract":476,"tags":477},[51],[],[],{"code":479,"score":165,"supported":47,"title":480,"abstract":481,"tags":482},"F6",[],[465],[465],{"code":234,"score":22,"supported":14,"title":484,"abstract":485,"tags":486},[],[],[220],{"code":488,"score":22,"supported":14,"title":489,"abstract":490,"tags":491},"I5",[],[],[466],[493,494],"草莓果柄 定点采摘 机械臂","Jetson AGX Orin 采摘 模仿学习","草莓果柄定点采摘机械臂-5533",{"doi":9,"openalex_id":9,"authors":497,"venue":9,"cited_by_count":97,"oa_url":9,"card":498,"direction":123,"ingested_from":450},[],{"tldr":499,"method":500,"finding":501,"direction":123,"opportunity":502},"用低成本遥操作机械臂采集演示，模仿学习实现草莓果柄定点采摘。","ResNet-50骨干的动作分块Transformer策略，Jetson边缘部署","130条演示训练后损失降至0.091，策略可15赫兹自主采摘。","可探索小样本跨品种泛化与失败恢复，降低演示采集成本。","2026-10-22T00:07:09.891422Z"]