[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2864":3,"related-2864":59},{"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":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":35,"paper":36,"created_at":58},2864,"Multi-Species Egg Morphometry and Weight Prediction Using Computer Vision","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10538483\u002Fv1","Multi-Species Egg Morphometry and Weight Prediction Using Computer Vision。Research Square",null,"Research Square","2026-09-17T00:00:00Z","论文",10,false,64,{"impact":16,"substance":17,"depth":18,"authority":12,"freshness":19,"relevant":20,"comment":21},12,18,15,9,1,"计算机视觉用于多物种禽蛋形态测量与重量预测，方法有创新但属预印本、应用面偏窄，可作为智慧养殖技术动态收录。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","计算机视觉","家禽养殖","蛋品检测",[31,32],"蛋重预测 计算机视觉","禽蛋 形态测量","蛋重预测计算机视觉-2864",0,"10.21203\u002Frs.3.rs-10538483\u002Fv1",{"doi":35,"openalex_id":37,"authors":38,"venue":9,"cited_by_count":34,"oa_url":55,"card":8,"direction":56,"ingested_from":57},"W7213451539",[39,42,45,47,49,51,53],{"name":40,"orcid":41},"Henna Hamadani","https:\u002F\u002Forcid.org\u002F0000-0002-0316-7418",{"name":43,"orcid":44},"Ambreen Hamadani","https:\u002F\u002Forcid.org\u002F0000-0002-5455-0468",{"name":46,"orcid":8},"Pakcha Hannah Boje",{"name":48,"orcid":8},"Amelia Moyon",{"name":50,"orcid":8},"Maliha Gulzar",{"name":52,"orcid":8},"A A Khan",{"name":54,"orcid":8},"R Kumar","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10538483\u002Flatest.pdf","智慧农业 \u002F 农业物联网","openalex","2026-09-18T23:30:08.789024Z",{"total":60,"page":20,"page_size":60,"items":61},6,[62,101,152,188,230,265],{"id":63,"title":64,"url":65,"summary":66,"summary_zh":67,"content":8,"source_name":68,"source_url":65,"published_at":69,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":70,"score_detail":71,"sources":77,"tags":79,"search_phrases":83,"slug":86,"view_count":34,"doi":87,"paper":88,"created_at":100},2650,"Automated Cocoa Bean Classification and Defect Detection Based on ASEAN Standard Using Image Processing and Feature-Based Analysis","https:\u002F\u002Fdoi.org\u002F10.64823\u002Fijeee.2601005","This study presents an automated cocoa bean classification and defect detection system developed in accordance with the ASEAN Standard for Cocoa Bean (ASEAN Stan 34:2014). Implemented in MATLAB, the system utilizes digital image processing and computer vision techniques to segment individual cocoa beans, extract geometric, color, and texture features, and classify them into quality categories: Extra Class, Class I, Class II, and Non-Compliant. The image processing workflow integrates color space transformations (RGB to HSV), adaptive thresholding, and morphological filtering to ensure accurate segmentation. Feature extraction targets parameters such as area, length, aspect ratio, texture entropy, and color uniformity to identify specific defects including moldy, slaty, insect-damaged, and germinated beans. Validation against independent expert grading achieved an overall reliability rate of 96.2%, with high precision (0.93) and recall (0.91), and an average processing speed of 1.3 seconds per image. The developed analyzer provides an objective, rapid, and repeatable tool to support standardization and postharvest cocoa quality control across the ASEAN region. Keywords: Defect Detection; MATLAB; cocoa bean grading; ASEAN Standard; digital image processing; computer vision","本研究提出了一套依据《东盟可可豆标准》（ASEAN Stan 34:2014）开发的自动化可可豆分类与缺陷检测系统。该系统在MATLAB中实现，利用数字图像处理与计算机视觉技术对单颗可可豆进行分割，提取几何、颜色和纹理特征，并将其分为优质级（Extra Class）、一级（Class I）、二级（Class II）和不合格（Non-Compliant）四个质量类别。图像处理流程整合了颜色空间变换（RGB转HSV）、自适应阈值分割和形态学滤波，以确保分割的准确性。特征提取针对面积、长度、长宽比、纹理熵和颜色均匀性等参数，以识别霉变、僵化、虫害和发芽等特定缺陷。经与独立专家评级进行验证，系统总体可靠率达到96.2%，精确率为0.93，召回率为0.91，平均处理速度为每张图像1.3秒。所开发的分析仪为支持东盟地区可可采后质量控制的标准化提供了一种客观、快速且可重复的工具。关键词：缺陷检测；MATLAB；可可豆分级；东盟标准；数字图像处理；计算机视觉","International Journal of Electrical and Electronics Engineering","2026-09-14T00:00:00Z",77,{"impact":17,"substance":72,"depth":73,"authority":74,"freshness":75,"relevant":20,"comment":76},21,17,13,8,"基于东盟标准的可可豆自动分级与缺陷检测研究，方法成熟、验证可靠（96.2%），对热带经济作物产后质控有实用价值，但属细分领域技术进展，影响力有限。",[78],{"name":68,"url":65},[25,26,27,80,81,82],"行业标准","农产品分级","可可产业",[84,85],"农业人工智能 农产品分级 计算机视觉 可可产业","农业人工智能 农产品分级","农业人工智能农产品分级计算机视觉可可产业-2650","10.64823\u002Fijeee.2601005",{"doi":87,"openalex_id":89,"authors":90,"venue":68,"cited_by_count":34,"oa_url":93,"card":94,"direction":56,"ingested_from":57},"W7212670192",[91],{"name":92,"orcid":8},"Jennifer Natnat","https:\u002F\u002Fioro.org\u002Farticle\u002F529160740839\u002Fpdf",{"tldr":95,"method":96,"finding":97,"direction":98,"opportunity":99},"基于图像处理与特征分析，按东盟标准自动分类可可豆并检测缺陷。","MATLAB图像处理，RGB转HSV、自适应阈值、形态学滤波，提取几何颜色纹理特","系统分类准确率96.2%，精度0.93，召回0.91，单图处理1.3秒。","农业人工智能与决策模型","可扩展至多作物、多标准实时分级，结合深度学习提升复杂缺陷识别鲁棒性。","2026-09-16T23:30:13.899726Z",{"id":102,"title":103,"url":104,"summary":105,"summary_zh":106,"content":8,"source_name":107,"source_url":104,"published_at":69,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":108,"score_detail":109,"sources":112,"tags":114,"search_phrases":117,"slug":120,"view_count":34,"doi":121,"paper":122,"created_at":151},2511,"Evaluation of hybrid models based on image segmentation and inference for pig weight estimation","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrai.2026.1876396","Introduction Accurate weight estimation in pig production is essential for optimizing management, feeding, and commercialization decisions; however, traditional weighing methods are invasive, time-consuming, and prone to operational errors. This study proposes a non-invasive computer vision–based approach to estimate pig weight under real farm conditions in San Martín, Peru. Methods A dataset of 3,800 lateral images paired with their corresponding ground-truth weights was collected. A computational pipeline was implemented, including geometric standardization, instance segmentation using YOLOv8n-seg, and feature extraction through EfficientNet-B0. The resulting embeddings were used as input for supervised regression models (SVR, XGBoost, and CatBoost), evaluated using repeated stratified cross-validation and an independent test set, with MAE, RMSE, and R² as performance metrics. Statistical comparisons were conducted using the Friedman test followed by Wilcoxon post hoc analysis with Holm correction. Results The results demonstrated strong predictive performance, with the SVR model achieving the best results (RMSE = 2.68 kg, MAE = 1.81 kg, R 2 = 0.85), showing statistically significant differences compared to the other models. Discussion These findings indicate that combining computer vision techniques with models capable of capturing non-linear relationships effectively models the relationship between animal morphology and body weight, providing a low-cost, non-invasive solution applicable to real-world production systems and supporting the advancement of precision livestock farming.","引言 在生猪生产中，准确的体重估测对于优化管理、饲喂和商业化决策至关重要；然而，传统称重方法具有侵入性、耗时且易产生操作误差。本研究提出了一种基于非侵入式计算机视觉的方法，用于在秘鲁圣马丁的实际农场条件下估测生猪体重。方法 收集了3，800张侧向图像及其对应的真实体重数据集。实施了计算流程，包括几何标准化、使用YOLOv8n-seg进行实例分割，以及通过EfficientNet-B0进行特征提取。所得嵌入向量被用作监督回归模型（SVR、XGBoost和CatBoost）的输入，采用重复分层交叉验证和独立测试集进行评估，以MAE、RMSE和R²作为性能指标。统计比较采用Friedman检验，随后进行Wilcoxon事后分析并应用Holm校正。结果 结果表明预测性能良好，SVR模型取得了最佳结果（RMSE = 2.68 kg，MAE = 1.81 kg，R² = 0.85），与其他模型相比显示出统计学显著差异。讨论 这些发现表明，将计算机视觉技术与能够捕捉非线性关系的模型相结合，可以有效建模动物形态与体重之间的关系，提供一种适用于实际生产系统的低成本、非侵入式解决方案，并支持精准畜牧养殖的发展。","Frontiers in Artificial Intelligence",73,{"impact":18,"substance":110,"depth":73,"authority":74,"freshness":75,"relevant":20,"comment":111},20,"基于YOLOv8分割与EfficientNet特征提取的生猪无接触称重研究，3800张图像、RMSE 2.68kg，方法组合与统计验证扎实，对精准畜牧有实用价值，但属细分技术进展，影响面有限。",[113],{"name":107,"url":104},[25,26,27,115,116],"生猪养殖","精准畜牧",[118,119],"农业人工智能 计算机视觉 智慧农业 生猪养殖","农业人工智能 计算机视觉","农业人工智能计算机视觉智慧农业生猪养殖-2511","10.3389\u002Ffrai.2026.1876396",{"doi":121,"openalex_id":123,"authors":124,"venue":107,"cited_by_count":34,"oa_url":145,"card":146,"direction":56,"ingested_from":57},"W7212747795",[125,128,131,133,136,138,140,143],{"name":126,"orcid":127},"Miguel Angel Valles-Coral","https:\u002F\u002Forcid.org\u002F0000-0002-8806-2892",{"name":129,"orcid":130},"Kelvin Lleins Rojas-Córdova","https:\u002F\u002Forcid.org\u002F0009-0001-4097-3961",{"name":132,"orcid":8},"Lloy Pinedo",{"name":134,"orcid":135},"Richard Injante","https:\u002F\u002Forcid.org\u002F0000-0002-2449-8937",{"name":137,"orcid":8},"Pierre Vidaurre-Rojas",{"name":139,"orcid":8},"Jorge Saavedra-Ramírez",{"name":141,"orcid":142},"Fernando Ruiz-Saavedra","https:\u002F\u002Forcid.org\u002F0000-0003-4664-4867",{"name":144,"orcid":8},"Williams Ramirez","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fartificial-intelligence\u002Farticles\u002F10.3389\u002Ffrai.2026.1876396\u002Fpdf",{"tldr":147,"method":148,"finding":149,"direction":98,"opportunity":150},"用YOLOv8n-seg分割猪体并提取EfficientNet特征，结合回归模型实现非侵入式猪体重估","3800张侧视图像，YOLOv8n-seg实例分割+EfficientNet-B","SVR表现最佳（RMSE=2.68kg，MAE=1.81kg，R²=0.85），显著优于其他模型。","可探索多视角、多品种及轻量化边缘部署，提升复杂农场环境下的泛化能力。","2026-09-15T23:30:08.535413Z",{"id":153,"title":154,"url":155,"summary":156,"summary_zh":157,"content":8,"source_name":158,"source_url":155,"published_at":159,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":160,"score_detail":161,"sources":165,"tags":167,"search_phrases":169,"slug":171,"view_count":34,"doi":172,"paper":173,"created_at":187},2339,"Quality Assessment of Scotch Bonnet Pepper (Capsicum Chinense Jacq) Using Image Processing Techniques","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.0903.06-j","Scotch Bonnet peppers (Capsicum chinense) are globally cultivated cash crops prized for their distinct culinary uses and high nutritional profile. However, meeting rising consumer demands for premium quality is hindered by traditional manual inspection, which remains subjective, slow, and labour-intensive. To address this, this study presents a computer vision-based framework designed to automate the pepper grading process. A convolutional neural network (CNN), a support vector machine (SVM), and logistic regression were trained and tested using a diverse dataset of 455 images capturing peppers at multiple stages of maturity. The algorithms categorized the crops into binary quality classes (\"good\" vs. \"bad\") by analysing extracted physical attributes, including dimensions, geometry, colouration, and surface uniformity. Model performance was quantified using standard metrics, namely precision, recall, F1 score, and overall accuracy. The findings reveal that while all classifiers achieved acceptable benchmarks, the deep learning CNN model demonstrated superior capabilities, reaching an accuracy of 97%—outperforming logistic regression at 80% and the SVM at 78%. Ultimately, these outcomes validate the integration of automated image analysis systems into modern agricultural pipelines, offering a robust and scalable substitute for manual quality control.","苏格兰帽椒（Capsicum chinense）是全球广泛种植的经济作物，因其独特的烹饪用途和高营养价值而备受青睐。然而，传统的人工检测方式主观性强、速度慢且劳动强度大，难以满足消费者对优质产品日益增长的需求。为解决这一问题，本研究提出了一种基于计算机视觉的框架，旨在实现辣椒分级过程的自动化。研究使用包含455张图像的数据集对卷积神经网络（CNN）、支持向量机（SVM）和逻辑回归进行了训练和测试，该数据集涵盖了辣椒多个成熟阶段的图像。这些算法通过分析提取的物理属性（包括尺寸、几何形状、色泽和表面均匀度），将作物分为二元质量类别（“优质”与“劣质”）。模型性能采用标准指标进行量化评估，即精确率、召回率、F1分数和总体准确率。研究结果表明，尽管所有分类器均达到了可接受的基准水平，但深度学习CNN模型表现更为优异，准确率达到97%，优于逻辑回归的80%和支持向量机的78%。最终，这些结果验证了自动化图像分析系统融入现代农业流程的可行性，为人工质量控制提供了一种稳健且可扩展的替代方案。","ABUAD Journal of Engineering Research and Development (AJERD)","2026-09-12T00:00:00Z",71,{"impact":162,"substance":110,"depth":163,"authority":16,"freshness":19,"relevant":20,"comment":164},14,16,"基于CNN的辣椒品质自动分级研究，97%准确率且对比SVM与逻辑回归，方法清晰、结论可靠，对农产品智能分选有参考价值，但属细分作物应用，影响范围有限。",[166],{"name":158,"url":155},[25,26,27,168,81],"辣椒",[170,85],"农业人工智能 农产品分级 计算机视觉 智慧农业","农业人工智能农产品分级计算机视觉智慧农业-2339","10.53982\u002Fajerd.2026.0903.06-j",{"doi":172,"openalex_id":174,"authors":175,"venue":158,"cited_by_count":34,"oa_url":155,"card":182,"direction":98,"ingested_from":57},"W7212404580",[176,178,180],{"name":177,"orcid":8},"Babatunde Olayinka Oyefeso",{"name":179,"orcid":8},"Rotimi Rufus Dinrifo",{"name":181,"orcid":8},"Oladapo Oyedeji",{"tldr":183,"method":184,"finding":185,"direction":98,"opportunity":186},"用图像处理和CNN\u002FSVM\u002F逻辑回归对苏格兰帽椒进行好坏二分类质量分级。","455张多成熟度辣椒图像，提取尺寸、几何、颜色、表面均匀性特征，训练CNN、SV","CNN准确率达97%，显著优于逻辑回归80%和SVM78%，验证自动图像分析可替代人工质检。","可扩展到多等级细粒度分级、多品种泛化及田间实时轻量化部署研究。","2026-09-13T23:30:43.649124Z",{"id":189,"title":190,"url":191,"summary":192,"summary_zh":193,"content":8,"source_name":194,"source_url":191,"published_at":195,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":196,"score_detail":197,"sources":200,"tags":202,"search_phrases":205,"slug":208,"view_count":34,"doi":209,"paper":210,"created_at":229},2314,"Spectral Transforms as a Tool to Optimize Digital Phenotyping in Biological Images","https:\u002F\u002Fdoi.org\u002F10.1111\u002Fjbg.70076","ABSTRACT Modern livestock breeding has mastered genotyping. Genome‐wide association studies, genomic selection, and SNP arrays enable genetic merit prediction at lower cost. However, phenotyping remains the bottleneck, as manual measurement is slow, expensive, subjective, and unable to capture spatial or temporal trait organization. Digital phenotyping via artificial intelligence could resolve this, but deep learning requires thousands of labelled examples, impractical when phenotyping cost itself limits datasets to hundreds of individuals. This creates a paradox: AI could accelerate phenotyping but requires large numbers of samples to train the models. Here, we demonstrate that integrating computer vision with machine learning offers sample‐efficient digital phenotyping using eggshell colour as a model system. Rather than learning features from scratch (deep learning), we engineer physically motivated features via Wavelet transforms that decompose images into multi‐scale spatial components. Wavelet features captured 14.2 percentage points more variance ( R 2 = 0.976 vs. 0.834, p \u003C 0.001) than standard colorimetry, with 50% better sample efficiency (achieving at n = 60 what colorimetry required n = 120). Variance decomposition revealed 77% of discriminative capacity derives from spatial patterns (bands, spots, gradients) invisible to scalar averages. Additionally, we identified “cryptic phenotypes” (3.3%) where spatial patterns contradicted average colour, cases where colorimeters failed but Wavelets succeeded. The underlying principle—that spatial decomposition can recover organizational information lost by scalar averaging—may be applicable to other traits with spatial or temporal structure, such as marbling, dermatitis, or pigmentation rhythms, although whether comparable performance gains would be observed remains to be tested empirically. Hence, for breeding programs implementing genomic selection, computer vision‐based digital phenotyping captures complex trait variation without massive training datasets, addressing the bottleneck that increasingly limits genetic progress as genotyping becomes trivial.","摘要 现代畜牧育种已掌握基因分型技术。全基因组关联研究、基因组选择和SNP芯片使得遗传 merit 预测成本降低。然而，表型测定仍是瓶颈，因为人工测量速度慢、成本高、主观性强，且无法捕捉性状的空间或时间组织。通过人工智能进行数字表型测定有望解决这一问题，但深度学习需要数千个标记样本，当表型测定成本本身将数据集限制在数百个个体时，这并不现实。这形成了一个悖论：人工智能可以加速表型测定，但需要大量样本才能训练模型。在此，我们证明将计算机视觉与机器学习相结合可提供样本高效的数字表型测定，并以蛋壳颜色作为模型系统。我们不是从头学习特征（深度学习），而是通过小波变换设计具有物理动机的特征，将图像分解为多尺度空间分量。小波特征捕获的方差比标准色度法多14.2个百分点（R² = 0.976 vs. 0.834，p \u003C 0.001），样本效率提高50%（在n = 60时达到色度法需要n = 120才能达到的效果）。方差分解显示，77%的判别能力来自标量平均值无法看到的空间模式（条带、斑点、梯度）。此外，我们识别出“隐蔽表型”（3.3%），即空间模式与平均颜色相矛盾的情况，这些情况下色度计失败但小波成功。其基本原理——空间分解可以恢复标量平均所丢失的组织信息——可能适用于其他具有空间或时间结构的性状，如大理石纹、皮炎或色素沉着节律，尽管是否能观察到 comparable 的性能提升仍有待实证检验。因此，对于实施基因组选择的育种项目，基于计算机视觉的数字表型测定无需大规模训练数据集即可捕获复杂性状变异，解决了随着基因分型变得轻而易举而日益限制遗传进展的瓶颈。","Journal of Animal Breeding and Genetics","2026-09-10T00:00:00Z",80,{"impact":17,"substance":198,"depth":17,"authority":162,"freshness":75,"relevant":20,"comment":199},22,"小波变换实现样本高效数字表型，为育种表型瓶颈提供新思路，方法新颖、数据扎实，值得进入每日精选。",[201],{"name":194,"url":191},[25,26,27,203,204],"基因组选择","数字表型",[206,207],"农业人工智能 基因组选择 计算机视觉 数字表型","农业人工智能 基因组选择","农业人工智能基因组选择计算机视觉数字表型-2314","10.1111\u002Fjbg.70076",{"doi":209,"openalex_id":211,"authors":212,"venue":194,"cited_by_count":34,"oa_url":222,"card":223,"direction":228,"ingested_from":57},"W7212167663",[213,216,219],{"name":214,"orcid":215},"Joseane Padilha da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-6681-6799",{"name":217,"orcid":218},"José V.V. Isola","https:\u002F\u002Forcid.org\u002F0000-0002-3168-3188",{"name":220,"orcid":221},"E. A. P. de Figueiredo","https:\u002F\u002Forcid.org\u002F0000-0003-0893-2489","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1111\u002Fjbg.70076",{"tldr":224,"method":225,"finding":226,"direction":98,"opportunity":227},"用离散小波变换提取蛋壳图像多尺度空间特征，实现小样本高效数字表型。","小波变换结合机器学习，以蛋壳颜色为模型系统，对比色度法。","小波特征解释方差比色度法高14.2个百分点，样本效率提升50%，77%判别力来自空间模式。","将小波空间分解表型框架迁移到其他具空间\u002F时间结构的性状，如大理石纹、皮炎或色素节律，验证普适性。","农业遥感与作物表型","2026-09-13T23:30:17.802829Z",{"id":231,"title":232,"url":233,"summary":234,"summary_zh":235,"content":8,"source_name":236,"source_url":233,"published_at":195,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":237,"score_detail":238,"sources":240,"tags":242,"search_phrases":245,"slug":247,"view_count":34,"doi":248,"paper":249,"created_at":264},2120,"Wild rice kernel inspection using a prompt-guided vision language model with evidence-grounded reasoning","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112392","Wild rice kernel inspection using a prompt-guided vision language model with evidence-grounded reasoning。Computers and Electronics in Agriculture","使用提示引导的视觉语言模型结合基于证据的推理进行野生稻谷粒检验。《计算机与电子农业》","Computers and Electronics in Agriculture",75,{"impact":163,"substance":110,"depth":73,"authority":162,"freshness":75,"relevant":20,"comment":239},"提出提示引导的视觉语言模型用于野生稻籽粒检测，方法新颖且发表于农业信息领域核心期刊，对智能育种与种子质检有参考价值。",[241],{"name":236,"url":233},[25,26,27,243,244],"水稻","种子检测",[246,119],"农业人工智能 计算机视觉 智慧农业 种子检测","农业人工智能计算机视觉智慧农业种子检测-2120","10.1016\u002Fj.compag.2026.112392",{"doi":248,"openalex_id":250,"authors":251,"venue":236,"cited_by_count":34,"oa_url":233,"card":259,"direction":98,"ingested_from":57},"W7212185866",[252,254,257],{"name":253,"orcid":8},"Yinka Sikiru",{"name":255,"orcid":256},"Angshuman Thakuria","https:\u002F\u002Forcid.org\u002F0000-0003-4196-3770",{"name":258,"orcid":8},"Chyngyz Erkinbaev",{"tldr":260,"method":261,"finding":262,"direction":98,"opportunity":263},"用提示引导的视觉语言模型对野生稻米粒进行检测，并给出基于证据的推理。","提示引导的视觉语言模型，结合证据锚定推理，用于野生稻米粒图像检测。","该方法能实现野生稻米粒检测，并输出可解释的证据推理过程。","可探索视觉语言模型在作物种子\u002F米粒质检中的可解释性与少样本迁移能力。","2026-09-11T23:30:01.974562Z",{"id":266,"title":267,"url":268,"summary":269,"summary_zh":270,"content":8,"source_name":236,"source_url":268,"published_at":195,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":271,"score_detail":272,"sources":274,"tags":276,"search_phrases":279,"slug":282,"view_count":34,"doi":283,"paper":284,"created_at":303},2115,"EdgeHatch: Detection of chicken hatching stages using machine learning on an edge device","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112380","EdgeHatch: Detection of chicken hatching stages using machine learning on an edge device。Computers and Electronics in Agriculture","EdgeHatch：基于边缘设备的机器学习鸡孵化阶段检测。《计算机与电子农业》",74,{"impact":18,"substance":110,"depth":73,"authority":162,"freshness":75,"relevant":20,"comment":273},"核心期刊论文，将机器学习与边缘设备用于鸡孵化阶段识别，方法新颖、面向家禽精准养殖，具备行业参考价值。",[275],{"name":236,"url":268},[25,26,277,28,278],"边缘计算","精准孵化",[280,281],"农业人工智能 家禽养殖 智慧农业 精准孵化","农业人工智能 家禽养殖","农业人工智能家禽养殖智慧农业精准孵化-2115","10.1016\u002Fj.compag.2026.112380",{"doi":283,"openalex_id":285,"authors":286,"venue":236,"cited_by_count":34,"oa_url":268,"card":298,"direction":56,"ingested_from":57},"W7212107612",[287,290,292,295],{"name":288,"orcid":289},"Luka Mali","https:\u002F\u002Forcid.org\u002F0009-0008-6224-527X",{"name":291,"orcid":8},"Matjaž Gobec",{"name":293,"orcid":294},"Anton Gradišek","https:\u002F\u002Forcid.org\u002F0000-0001-6480-9587",{"name":296,"orcid":297},"Urban Sedlar","https:\u002F\u002Forcid.org\u002F0000-0003-2836-5493",{"tldr":299,"method":300,"finding":301,"direction":56,"opportunity":302},"提出EdgeHatch，在边缘设备上用机器学习检测鸡蛋孵化阶段。","边缘设备部署机器学习模型，分析孵化期图像\u002F数据。","可在边缘端实时识别鸡胚孵化阶段，无需云端。","可扩展至多物种孵化监测，优化边缘模型轻量化与多阶段连续识别。","2026-09-11T23:30:01.667648Z"]