[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2301":3},{"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":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":60},2301,"A crop cultivation monitoring platform for evaluating the early growth of cucumbers","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1869455","The precise temporal characterization of early growth dynamics in crops under abiotic stress is critical for stress resistance management in smart agriculture. However, traditional manual measurement methods struggle to achieve high-throughput and accurate quantification of multiple phenotypic parameters over continuous time series. To address this, we developed a full-time-series crop growth monitoring system that continuously collects fine-grained image data of cucumber seed germination and seedling growth under soil culture conditions, constructing an annotated dataset for identifying germination status and quantifying cotyledon area. The YOLOv8n-F_SGX and YOLOv8-seg models were developed and deployed, achieving detection and segmentation accuracies of 0.978 and 0.987, respectively, which enabled automatic monitoring of germination rate and precise extraction of cotyledon area. To further investigate the mitigating effects of nanomaterial priming on salt stress, we conducted germination and seedling growth experiments on cucumber seeds using a series of ZnO-NPs (particle size 30 nm) suspension concentrations at different salt stress levels. The results demonstrated that under salt stress conditions of 0–150 mmol·L −1 , priming with 100 mg·L −1 ZnO-NPs resulted in optimal germination dynamics (highest germination rate and fastest germination speed), along with the largest cotyledon area and highest growth rate in seedlings. In contrast, when the ZnO-NPs concentration increased to 400 mg·L −1 or higher, it significantly inhibited seed germination and seedling growth.","在智慧农业的抗逆管理中，精确表征作物在非生物胁迫下早期生长动态的时间特征至关重要。然而，传统的人工测量方法难以实现对连续时间序列上多个表型参数的高通量、准确量化。为解决这一问题，我们开发了一套全时间序列作物生长监测系统，持续采集土培条件下黄瓜种子萌发和幼苗生长的细粒度图像数据，构建了用于识别萌发状态和量化子叶面积的标注数据集。我们开发并部署了YOLOv8n-F_SGX和YOLOv8-seg模型，分别达到0.978和0.987的检测与分割精度，从而实现了萌发率的自动监测和子叶面积的精确提取。为进一步探究纳米材料引发对盐胁迫的缓解效应，我们在不同盐胁迫水平下，使用一系列浓度ZnO-NPs（粒径30 nm）悬浮液对黄瓜种子进行了萌发和幼苗生长实验。结果表明，在0–150 mmol·L⁻¹盐胁迫条件下，100 mg·L⁻¹ ZnO-NPs引发处理可获得最佳的萌发动态（最高萌发率和最快萌发速度），同时幼苗子叶面积最大、生长速率最高。相反，当ZnO-NPs浓度增至400 mg·L⁻¹或更高时，则显著抑制了种子萌发和幼苗生长。",null,"Frontiers in Plant Science","2026-09-11T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"构建黄瓜早期生长全时序监测系统，YOLOv8检测分割精度达0.978\u002F0.987，并给出ZnO-NPs缓解盐胁迫的最佳浓度，方法新颖、数据扎实，对智慧育种与抗逆栽培有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","盐胁迫","作物表型","黄瓜育种",0,"10.3389\u002Ffpls.2026.1869455",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":6,"card":53,"direction":57,"ingested_from":59},"W7212301419",[36,38,40,42,44,46,49,51],{"name":37,"orcid":9},"Deyi Lei",{"name":39,"orcid":9},"Zaibiao Zhu",{"name":41,"orcid":9},"Shen Penghong",{"name":43,"orcid":9},"Zhibo Zhong",{"name":45,"orcid":9},"Mohamed Ahmed Moustafa",{"name":47,"orcid":48},"Jieyu Xian","https:\u002F\u002Forcid.org\u002F0000-0002-2526-6644",{"name":50,"orcid":9},"Hongbin Wu",{"name":52,"orcid":9},"Xiuqing Fu",{"tldr":54,"method":55,"finding":56,"direction":57,"opportunity":58},"构建黄瓜早期生长监测平台，用YOLO模型自动量化发芽与子叶面积，并评估ZnO-NPs缓解盐胁迫效果。","连续时序图像采集与标注，YOLOv8n-F_SGX检测和YOLOv8-seg分割","100 mg·L−1 ZnO-NPs在0–150 mmol·L−1盐胁迫下促进发芽和幼苗生长，400","智慧农业 \u002F 农业物联网","可扩展至多作物多胁迫场景，融合时序表型与深度学习实现早期胁迫预警和纳米材料精准调控。","openalex","2026-09-13T23:30:09.699346Z"]