[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2862":3,"related-2862":91},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":21,"tags":23,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":64,"slug":67,"view_count":68,"doi":69,"paper":70,"created_at":90},2862,"A closed-loop spatial–temporal–path–execution coordination framework for dual-arm tomato harvesting in constrained shared workspaces","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104590","A closed-loop spatial–temporal–path–execution coordination framework for dual-arm tomato harvesting in constrained shared workspaces。Biosystems Engineering",null,"Biosystems Engineering","2026-09-18T00:00:00Z","论文",10,false,80,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":12,"relevant":19,"comment":20},18,20,14,1,"核心期刊发表的农业机器人双臂协同采摘新框架，方法新颖、时效性强，对智慧农业装备研发有参考价值。",[22],{"name":9,"url":6},[24,25,26,27,28],"智慧农业","农业机器人","路径规划","双臂采摘","番茄收获",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":37,"locked":13,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,49,54,59],{"code":30,"score":43,"supported":44,"title":45,"abstract":47,"tags":48},11,true,[46],"tomato",[46],[],{"code":50,"score":19,"supported":13,"title":51,"abstract":52,"tags":53},"F5",[],[],[26],{"code":55,"score":19,"supported":13,"title":56,"abstract":57,"tags":58},"F6",[],[],[25],{"code":60,"score":19,"supported":13,"title":61,"abstract":62,"tags":63},"I1",[],[],[24],[65,66],"双臂番茄采摘 机器人","Biosystems Engineering 双臂采摘","双臂番茄采摘机器人-2862",2,"10.1016\u002Fj.biosystemseng.2026.104590",{"doi":69,"openalex_id":71,"authors":72,"venue":9,"cited_by_count":88,"oa_url":8,"card":8,"direction":8,"ingested_from":89},"W7213493097",[73,75,77,80,83,85],{"name":74,"orcid":8},"Xingyu Lu",{"name":76,"orcid":8},"Yongnian Zhang",{"name":78,"orcid":79},"Jieyu Xian","https:\u002F\u002Forcid.org\u002F0000-0002-2526-6644",{"name":81,"orcid":82},"Qingyu Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5293-2024",{"name":84,"orcid":8},"Xingsheng Wang",{"name":86,"orcid":87},"Xiaochan Wang","https:\u002F\u002Forcid.org\u002F0000-0003-0461-8417",0,"openalex","2026-09-18T23:30:05.475729Z",{"total":92,"page":19,"page_size":92,"items":93},6,[94,170,226,278,327,389],{"id":95,"title":96,"url":97,"summary":98,"summary_zh":8,"content":8,"source_name":99,"source_url":8,"published_at":100,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":101,"score_detail":102,"sources":107,"tags":109,"primary_topic":113,"secondary_topics":114,"classification":115,"search_phrases":155,"slug":158,"view_count":159,"doi":8,"paper":160,"created_at":169},3960,"Groundbreaking AI Robot Picks Tomatoes Autonomously——YOLOv8n-BiFPN-WIoU模型驱动采摘新范式","https:\u002F\u002Fagritechinsights.com\u002Findex.php\u002F2026\u002F10\u002F02\u002Fgroundbreaking-ai-robot-picks-tomatoes-autonomously","山东理工大学孟凡钊团队开发出基于YOLOv8n-BiFPN-WIoU轻量级深度学习模型的温室番茄自主采摘机器人，可在边缘设备NVIDIA Jetson Nano上运行，优化后达21.1 FPS实时运行；定位误差3.5±0.32mm；mAP@0.5达92.4%。系统采用六轴机械臂+双爪末端执行器，针对温室遮挡与光照变化优化，适用于商业化串番茄采收；系统基于开源ROS模块化设计，未来可适配黄瓜、辣椒、草莓等。","Agritech Insights·2026年10月02日（Frontiers in Plant Science）","2026-10-02T00:00:00Z",86,{"impact":103,"substance":104,"depth":16,"authority":105,"freshness":12,"relevant":19,"comment":106},22,23,13,"轻量模型+边缘部署的番茄自主采摘机器人，方法新颖、指标扎实，具备细分行业参考价值。",[108],{"name":99,"url":97},[24,110,25,111,112],"农业人工智能","边缘计算","番茄采摘",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":116},{"candidates":117,"agricultural_evidence":44},[118,127,133,139,146,150],{"code":30,"score":119,"supported":44,"title":120,"abstract":121,"tags":126},9,[],[122,123,124,125],"番茄","辣椒","黄瓜","草莓",[],{"code":55,"score":128,"supported":44,"title":129,"abstract":130,"tags":132},4,[],[131],"采摘机器人",[25],{"code":134,"score":128,"supported":44,"title":135,"abstract":136,"tags":138},"I2",[],[137],"深度学习",[110],{"code":140,"score":141,"supported":44,"title":142,"abstract":143,"tags":145},"B7",3,[],[144],"温室",[],{"code":60,"score":19,"supported":13,"title":147,"abstract":148,"tags":149},[],[],[24],{"code":151,"score":19,"supported":13,"title":152,"abstract":153,"tags":154},"I5",[],[],[111],[156,157],"山东理工大学 番茄采摘机器人","YOLOv8n 温室番茄 采摘","山东理工大学番茄采摘机器人-3960",8,{"doi":8,"openalex_id":8,"authors":161,"venue":8,"cited_by_count":88,"oa_url":8,"card":162,"direction":166,"ingested_from":168},[],{"tldr":163,"method":164,"finding":165,"direction":166,"opportunity":167},"开发基于YOLOv8n-BiFPN-WIoU的轻量模型，实现温室番茄自主采摘机器人实时运行。","YOLOv8n-BiFPN-WIoU轻量模型，Jetson Nano边缘部署，六","mAP@0.5达92.4%，21.1 FPS实时运行，定位误差3.5±0.32mm，可商业化串番茄采","智慧农业 \u002F 农业物联网","可延伸至多作物泛化、复杂遮挡与光照鲁棒性提升，以及边缘端模型轻量化与多机协同采摘。","agent","2026-10-02T11:53:16.408068Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":175,"content":8,"source_name":176,"source_url":173,"published_at":177,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":178,"score_detail":179,"sources":182,"tags":184,"primary_topic":189,"secondary_topics":190,"classification":191,"search_phrases":208,"slug":211,"view_count":88,"doi":212,"paper":213,"created_at":225},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":16,"substance":103,"depth":16,"authority":180,"freshness":119,"relevant":19,"comment":181},12,"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。",[183],{"name":176,"url":173},[24,185,186,187,188],"遥感监测","植物表型","作物胁迫诊断","能量平衡",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":192},{"candidates":193,"agricultural_evidence":44},[194,199,203],{"code":30,"score":141,"supported":44,"title":195,"abstract":196,"tags":198},[],[197],"萝卜",[],{"code":60,"score":19,"supported":13,"title":200,"abstract":201,"tags":202},[],[],[24],{"code":204,"score":19,"supported":13,"title":205,"abstract":206,"tags":207},"I3",[],[],[185],[209,210],"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":212,"openalex_id":214,"authors":215,"venue":176,"cited_by_count":88,"oa_url":173,"card":219,"direction":223,"ingested_from":89},"W7223098474",[216],{"name":217,"orcid":218},"Mokhdum Azam Mashrafi","https:\u002F\u002Forcid.org\u002F0009-0002-1801-1130",{"tldr":220,"method":221,"finding":222,"direction":223,"opportunity":224},"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":227,"title":228,"url":229,"summary":230,"summary_zh":8,"content":8,"source_name":231,"source_url":8,"published_at":177,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":232,"score_detail":233,"sources":238,"tags":240,"primary_topic":245,"secondary_topics":249,"classification":251,"search_phrases":266,"slug":269,"view_count":88,"doi":8,"paper":270,"created_at":277},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":234,"substance":235,"depth":236,"authority":105,"freshness":12,"relevant":19,"comment":237},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。",[239],{"name":231,"url":229},[24,241,242,243,244],"设施农业","环境调控","温室番茄","智能决策",{"code":140,"slug":246,"name":247,"path":248,"parent_code":34},"protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture",[250],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":252},{"candidates":253,"agricultural_evidence":44},[254,258,262],{"code":140,"score":105,"supported":44,"title":255,"abstract":256,"tags":257},[144],[144],[243,242],{"code":30,"score":43,"supported":44,"title":259,"abstract":260,"tags":261},[122],[122],[],{"code":60,"score":19,"supported":13,"title":263,"abstract":264,"tags":265},[],[],[24],[267,268],"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":8,"openalex_id":8,"authors":271,"venue":8,"cited_by_count":88,"oa_url":8,"card":272,"direction":166,"ingested_from":168},[],{"tldr":273,"method":274,"finding":275,"direction":166,"opportunity":276},"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。","2026-10-22T00:07:10.129353Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":8,"content":8,"source_name":283,"source_url":8,"published_at":284,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":285,"score_detail":286,"sources":288,"tags":290,"primary_topic":293,"secondary_topics":294,"classification":295,"search_phrases":314,"slug":317,"view_count":88,"doi":8,"paper":318,"created_at":326},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":234,"substance":235,"depth":16,"authority":119,"freshness":128,"relevant":19,"comment":287},"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。",[289],{"name":283,"url":281},[110,25,111,291,292],"模仿学习","草莓采摘",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":296},{"candidates":297,"agricultural_evidence":44},[298,302,306,310],{"code":30,"score":159,"supported":44,"title":299,"abstract":300,"tags":301},[125],[],[],{"code":55,"score":128,"supported":44,"title":303,"abstract":304,"tags":305},[],[25],[25],{"code":134,"score":19,"supported":13,"title":307,"abstract":308,"tags":309},[],[],[110],{"code":151,"score":19,"supported":13,"title":311,"abstract":312,"tags":313},[],[],[111],[315,316],"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":8,"openalex_id":8,"authors":319,"venue":8,"cited_by_count":88,"oa_url":8,"card":320,"direction":324,"ingested_from":168},[],{"tldr":321,"method":322,"finding":323,"direction":324,"opportunity":325},"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。","农业人工智能与决策模型","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",{"id":328,"title":329,"url":330,"summary":331,"summary_zh":332,"content":8,"source_name":333,"source_url":330,"published_at":334,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":335,"score_detail":336,"sources":338,"tags":340,"primary_topic":343,"secondary_topics":344,"classification":350,"search_phrases":369,"slug":372,"view_count":88,"doi":373,"paper":374,"created_at":388},5387,"LIGHTWEIGHT FEATURE FUSED ATTENTION DRIVEN MULTI LEVEL HYBRID MOBILENET V2 FRAMEWORK FOR EARLY TOMATO LEAF DISEASE DETECTION","https:\u002F\u002Fdoi.org\u002F10.62441\u002Factainnovations.vi.1918","It is crucial to find and treat tomato crop diseases early and accurately to improve crop efficiency and make sure there is no scarcity of crop. However, computerized analysis is provocative due to lighting differences, compound backgrounds, and graphic likenesses among infection regions. Research work proposed a lightweight Attention Driven Multi Level Hybrid MobileNet-V2 algorithm that fuses the handcrafted texture structures with Deep Learning (DL) features, then image preprocessing is incorporated using Gaussian, Bilateral, and Guided filters to minimize noise by preserving the disease lesions. LAB color model along with Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to improve the contrast. Otsu thresholding is involved to effectively separate the leaf segment from diseased parts. Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) descriptors are used to obtain fine-grained features, while high level features are gained by pretrained MobileNet-V2 model. Features fusion is fed to the fully connected layers of deep learning model for training and validation purpose. Results have revealed that the suggested model attains better classification performance as compared to existing models, making it an effective explanation for real time tomato leaf disease detection for smart agriculture applications.","早期准确发现并治疗番茄作物病害，对于提高作物效率、确保作物不短缺至关重要。然而，由于光照差异、复杂背景以及感染区域之间的图形相似性，计算机化分析具有挑战性。研究提出了一种轻量级注意力驱动多层混合MobileNet-V2算法，该算法将手工纹理结构与深度学习（DL）特征相融合，然后结合高斯、双边和引导滤波器进行图像预处理，以在保留病害病变的同时最小化噪声。采用LAB颜色模型和对比度受限自适应直方图均衡化（CLAHE）来提高对比度。引入Otsu阈值分割以有效分离叶片片段与病害部分。使用局部二值模式（LBP）和灰度共生矩阵（GLCM）描述符获取细粒度特征，同时通过预训练的MobileNet-V2模型获取高层特征。特征融合后输入深度学习模型的全连接层进行训练和验证。结果表明，与现有模型相比，所提出的模型取得了更好的分类性能，使其成为智慧农业应用中实时番茄叶片病害检测的有效解决方案。","Acta Innovations","2026-10-18T00:00:00Z",64,{"impact":180,"substance":16,"depth":234,"authority":12,"freshness":159,"relevant":19,"comment":337},"提出融合手工纹理特征与MobileNet-V2的轻量番茄叶病检测方法，有一定方法新颖性，但属单篇论文、应用范围有限，未达每日精选门槛。",[339],{"name":333,"url":330},[24,110,137,341,342],"番茄病害","图像识别",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[345],{"code":134,"slug":346,"name":347,"path":348,"parent_code":349},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":37,"locked":13,"version":38,"method":39,"evidence":351},{"candidates":352,"agricultural_evidence":44},[353,358,363],{"code":30,"score":354,"supported":44,"title":355,"abstract":356,"tags":357},15,[46],[122,46],[341],{"code":134,"score":119,"supported":44,"title":359,"abstract":360,"tags":362},[],[137,361],"deep learning",[110,342,137],{"code":60,"score":364,"supported":44,"title":365,"abstract":366,"tags":368},7,[],[24,367],"smart agriculture",[24],[370,371],"MobileNet-V2 番茄叶片病害","番茄叶病 深度学习 检测","MobileNet-V2番茄叶片病害-5387","10.62441\u002Factainnovations.vi.1918",{"doi":373,"openalex_id":375,"authors":376,"venue":333,"cited_by_count":88,"oa_url":382,"card":383,"direction":166,"ingested_from":89},"W7222977034",[377,379],{"name":378,"orcid":8},"Mrs. Vijetalaxmi A Hegde",{"name":380,"orcid":381},"Abhay Ramakant Kasetwar","https:\u002F\u002Forcid.org\u002F0000-0002-5800-8764","https:\u002F\u002Factainnovations.com\u002Findex.php\u002Fpub\u002Farticle\u002Fdownload\u002F1918\u002F981",{"tldr":384,"method":385,"finding":386,"direction":324,"opportunity":387},"提出轻量注意力多级混合MobileNet-V2框架，融合手工纹理与深度学习特征实现番茄叶片病害早期检","高斯\u002F双边\u002F引导滤波去噪，LAB+CLAHE增强，Otsu分割，LBP与GLCM","所提模型分类性能优于现有模型，可有效用于智慧农业实时番茄叶片病害检测。","可探索多作物跨域泛化、田间复杂光照下轻量模型部署与可解释性，及病害早期分级检测。","2026-10-20T23:30:10.424511Z",{"id":390,"title":391,"url":392,"summary":393,"summary_zh":394,"content":8,"source_name":395,"source_url":392,"published_at":284,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":396,"score_detail":397,"sources":399,"tags":401,"primary_topic":405,"secondary_topics":406,"classification":412,"search_phrases":455,"slug":458,"view_count":88,"doi":8,"paper":459,"created_at":477},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",67,{"impact":180,"substance":235,"depth":236,"authority":119,"freshness":159,"relevant":19,"comment":398},"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。",[400],{"name":395,"url":392},[24,110,402,403,404],"农业物联网","温室环境","小气候预测",{"code":140,"slug":246,"name":247,"path":248,"parent_code":34},[407,411],{"code":151,"slug":408,"name":409,"path":410,"parent_code":349},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":413},{"candidates":414,"agricultural_evidence":44},[415,421,426,431,437,441,448],{"code":151,"score":354,"supported":44,"title":416,"abstract":418,"tags":420},[417],"IoT",[419,417],"物联网",[402],{"code":140,"score":18,"supported":44,"title":422,"abstract":424,"tags":425},[423],"greenhouse",[144,423],[],{"code":30,"score":119,"supported":44,"title":427,"abstract":428,"tags":430},[],[122,123,124,46,429],"cucumber",[],{"code":60,"score":364,"supported":44,"title":432,"abstract":433,"tags":436},[],[434,435],"精准农业","precision agriculture",[24],{"code":134,"score":364,"supported":44,"title":438,"abstract":439,"tags":440},[],[137,361],[110],{"code":442,"score":92,"supported":44,"title":443,"abstract":444,"tags":447},"H2",[],[445,446],"土壤","soil",[],{"code":449,"score":92,"supported":44,"title":450,"abstract":451,"tags":454},"H3",[],[452,453],"灌溉","irrigation",[],[456,457],"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":460,"authors":461,"venue":395,"cited_by_count":88,"oa_url":8,"card":472,"direction":166,"ingested_from":89},"W7222909837",[462,464,466,468,470],{"name":463,"orcid":8},"Siman Emmanuel*",{"name":465,"orcid":8},"Edward Onyebueke Agu",{"name":467,"orcid":8},"Musa Gani",{"name":469,"orcid":8},"Oladunjoye John Abiodun",{"name":471,"orcid":8},"Anagu Emmanuel John",{"tldr":473,"method":474,"finding":475,"direction":166,"opportunity":476},"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。","2026-10-19T23:30:13.295236Z"]