[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1188":3,"related-1188":73},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":63,"slug":66,"view_count":67,"doi":8,"paper":68,"created_at":72},1188,"A Feature Enhancement Framework for Joint Mango Fruit and Stem Detection in Complex Orchard Environments (复杂果园环境中芒果果实与果柄联合检测的特征增强框架)","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888","MDPI Agriculture 2026, 16(17), 1888, 2026年8月31日发表,作者来自华南农业大学工程学院和农业装备技术国家重点实验室。研究提出MangoNET,基于YOLOv11n的芒果果实与果柄联合检测框架,适用于复杂果园环境。P2高分辨率检测头保留小目标的精细空间信息,SPPF-ELAN聚合局部和上下文特征用于部分可见对象,SENet在光照变化下重新校准通道响应,WIoU v3调节具有不同定位质量的边界框样本。数据集包含1782张台农和金煌芒果原始图像,仅对训练集应用数据增强,将训练集大小从1172张增加到2886张图像。MangoNET果实和果柄F1分数分别为0.920和0.916,mAP50和mAP50-95值为0.941和0.690。",null,"Next Article in Journal\n\n[Straw Biochar Incorporation Reduces CH 4 Emissions in Double-Cropping Rice Paddies and Partially Alleviates Warming-Induced Yield Losses](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1889)\n\n Previous Article in Journal\n\n[Tracking Insects in Controlled Experiments](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1887)\n\n Previous Article in Special Issue\n\n[ShuffleNetV2-hSimKD: A Lightweight Network for Plant Disease Detection](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F15\u002F1686)\n\n## Journals\n\n[Active Journals](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournals)[Find a Journal](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournalfinder)[Journal Proposal](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournals\u002Fproposal)[Proceedings Series](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fproceedings)\n\n## [Topics](https:\u002F\u002Fwww.mdpi.com\u002Ftopics)\n\n## Information\n\n[For Authors](https:\u002F\u002Fwww.mdpi.com\u002Fauthors)[For Reviewers](https:\u002F\u002Fwww.mdpi.com\u002Freviewers)[For Editors](https:\u002F\u002Fwww.mdpi.com\u002Feditors)[For Librarians](https:\u002F\u002Fwww.mdpi.com\u002Flibrarians)[For Publishers](https:\u002F\u002Fwww.mdpi.com\u002Fpublishing_services)[For Societies](https:\u002F\u002Fwww.mdpi.com\u002Fsocieties)[For Conference Organizers](https:\u002F\u002Fwww.mdpi.com\u002Fconference_organizers)\n\n[Open Access Policy](https:\u002F\u002Fwww.mdpi.com\u002Fopenaccess)[Institutional Open Access Program](https:\u002F\u002Fwww.mdpi.com\u002Fioap)[Special Issues Guidelines](https:\u002F\u002Fwww.mdpi.com\u002Fspecial_issues_guidelines)[Editorial Process](https:\u002F\u002Fwww.mdpi.com\u002Feditorial_process)[Research and Publication Ethics](https:\u002F\u002Fwww.mdpi.com\u002Fethics)[Article Processing Charge](https:\u002F\u002Fwww.mdpi.com\u002Fapc)[Awards](https:\u002F\u002Fwww.mdpi.com\u002Fawards)[Testimonials](https:\u002F\u002Fwww.mdpi.com\u002Ftestimonials)\n\n## [Author Services](https:\u002F\u002Fwww.mdpi.com\u002Fauthors\u002Fservices)\n\n## Initiatives\n\n[Sciforum](https:\u002F\u002Fsciforum.net\u002F)[MDPI Books](https:\u002F\u002Fwww.mdpi.com\u002Fbooks)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002F)[Scilit](https:\u002F\u002Fwww.scilit.com\u002F)[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002F)[Encyclopedia](https:\u002F\u002Fencyclopedia.pub\u002F)[JAMS](https:\u002F\u002Fjams.pub\u002F)[Proceedings Series](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fproceedings)\n\n## About\n\n[Overview](https:\u002F\u002Fwww.mdpi.com\u002Fabout)[Contact](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fcontact)[Careers](https:\u002F\u002Fcareers.mdpi.com\u002F)[News](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fannouncements)[Press](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fpress)[Blog](http:\u002F\u002Fblog.mdpi.com\u002F)\n\n[Sign In \u002F Sign Up](https:\u002F\u002Fwww.mdpi.com\u002Fuser\u002Flogin)\n\n## Notice\n\n You can make submissions to other journals [here](https:\u002F\u002Fsusy.mdpi.com\u002Fuser\u002Fmanuscripts\u002Fupload). \n\n[_clear_](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)\n\n## Notice\n\nYou are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.\n\n[Continue](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)[Cancel](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)\n\n[_clear_](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)\n\nAll articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to [https:\u002F\u002Fwww.mdpi.com\u002Fopenaccess](https:\u002F\u002Fwww.mdpi.com\u002Fopenaccess).\n\nFeature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.\n\nFeature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.\n\nEditor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.\n\nOriginal Submission Date Received: .\n\n[![Image 5: MDPI Open Access Journals](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Fmdpi-pub-logo-black-small1.svg?da3a8dcae975a41c?1788181901)](https:\u002F\u002Fwww.mdpi.com\u002F)\n\n[_clear_](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888#)[_zoom\\_out\\_map_](https:\u002F\u002Fwww.mdpi.com\u002Ftoggle_desktop_layout_cookie \"Toggle desktop layout\")[_search_](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888#)[_menu_](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888# \"MDPI main page\")\n\n[![Image 6: MDPI Open Access Journals](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Fmdpi-pub-logo-black-small1.svg?da3a8dcae975a41c?1788181901)](https:\u002F\u002Fwww.mdpi.com\u002F)\n\n*   [Journals](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournals)\n    *   \n        *   [Active Journals](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournals)\n        *   [Find a Journal](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournalfinder)\n        *   [Journal Proposal](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournals\u002Fproposal)\n        *   [Proceedings Series](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fproceedings)\n\n*   [Topics](https:\u002F\u002Fwww.mdpi.com\u002Ftopics)\n*   [Information](https:\u002F\u002Fwww.mdpi.com\u002Fauthors)\n    *   \n        *   [For Authors](https:\u002F\u002Fwww.mdpi.com\u002Fauthors)\n        *   [For Reviewers](https:\u002F\u002Fwww.mdpi.com\u002Freviewers)\n        *   [For Editors](https:\u002F\u002Fwww.mdpi.com\u002Feditors)\n        *   [For Librarians](https:\u002F\u002Fwww.mdpi.com\u002Flibrarians)\n        *   [For Publishers](https:\u002F\u002Fwww.mdpi.com\u002Fpublishing_services)\n        *   [For Societies](https:\u002F\u002Fwww.mdpi.com\u002Fsocieties)\n        *   [For Conference Organizers](https:\u002F\u002Fwww.mdpi.com\u002Fconference_organizers)\n\n        *   [Open Access Policy](https:\u002F\u002Fwww.mdpi.com\u002Fopenaccess)\n        *   [Institutional Open Access Program](https:\u002F\u002Fwww.mdpi.com\u002Fioap)\n        *   [Special Issues Guidelines](https:\u002F\u002Fwww.mdpi.com\u002Fspecial_issues_guidelines)\n        *   [Editorial Process](https:\u002F\u002Fwww.mdpi.com\u002Feditorial_process)\n        *   [Research and Publication Ethics](https:\u002F\u002Fwww.mdpi.com\u002Fethics)\n        *   [Article Processing Charge](https:\u002F\u002Fwww.mdpi.com\u002Fapc)\n        *   [Awards](https:\u002F\u002Fwww.mdpi.com\u002Fawards)\n        *   [Testimonials](https:\u002F\u002Fwww.mdpi.com\u002Ftestimonials)\n\n*   [Author Services](https:\u002F\u002Fwww.mdpi.com\u002Fauthors\u002Fservices)\n*   [Initiatives](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Finitiatives)\n    *   \n        *   [Sciforum](https:\u002F\u002Fsciforum.net\u002F)\n        *   [MDPI Books](https:\u002F\u002Fwww.mdpi.com\u002Fbooks)\n        *   [Preprints.org](https:\u002F\u002Fwww.preprints.org\u002F)\n        *   [Scilit](https:\u002F\u002Fwww.scilit.com\u002F)\n        *   [SciProfiles](https:\u002F\u002Fsciprofiles.com\u002F)\n        *   [Encyclopedia](https:\u002F\u002Fencyclopedia.pub\u002F)\n        *   [JAMS](https:\u002F\u002Fjams.pub\u002F)\n        *   [Proceedings Series](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fproceedings)\n\n*   [About](https:\u002F\u002Fwww.mdpi.com\u002Fabout)\n    *   \n        *   [Overview](https:\u002F\u002Fwww.mdpi.com\u002Fabout)\n        *   [Contact](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fcontact)\n        *   [Careers](https:\u002F\u002Fcareers.mdpi.com\u002F)\n        *   [News](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fannouncements)\n        *   [Press](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fpress)\n        *   [Blog](http:\u002F\u002Fblog.mdpi.com\u002F)\n\n[Sign In \u002F Sign Up](https:\u002F\u002Fwww.mdpi.com\u002Fuser\u002Flogin)[Submit](https:\u002F\u002Fsusy.mdpi.com\u002Fuser\u002Fmanuscripts\u002Fupload?journal=agriculture)\n\nSearch for Articles:\n\nTitle \u002F Keyword\n\nAuthor \u002F Affiliation \u002F Email\n\nJournal\n\n[Agriculture](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)\n\nArticle Type\n\n[All Article Types](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)\n\n[Advanced Search](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888#)\n\nSection\n\n[Select an Option](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)\n\nSpecial Issue\n\n[Select an Option](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)\n\nVolume\n\nIssue\n\nNumber\n\nPage\n\nLogical Operator Operator\n\nSearch Text\n\nSearch Type\n\n_add\\_circle\\_outline_\n\n_remove\\_circle\\_outline_\n\n[Journals](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournals)\n\n[Agriculture](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Fagriculture)\n\n[Volume 16](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16)\n\n[Issue 17](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17)\n\n[10.3390\u002Fagriculture16171888](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888#)\n\n[![Image 7: agriculture-logo](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fjournals\u002Fagriculture-logo.png?d9aa6395761dfdff)](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Fagriculture)\n\n[Submit to this Journal](https:\u002F\u002Fsusy.mdpi.com\u002Fuser\u002Fmanuscripts\u002Fupload?form%5Bjournal_id%5D%3D95)[Review for this Journal](https:\u002F\u002Fsusy.mdpi.com\u002Fvolunteer\u002Fjournals\u002Freview)[Propose a Special Issue](https:\u002F\u002Fwww.mdpi.com\u002Fjournalproposal\u002Fsendproposalspecialissue\u002Fagriculture)\n\n[►▼ Article Menu](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888#)\n\n## Article Menu\n\n*   [Academic Editor](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888#academic_editors)[![Image 8](https:\u002F\u002Fwww.mdpi.com\u002Fprofiles\u002F356481\u002Fthumb\u002FFrancesco_Marinello.jpg)Francesco Marinello](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F356481?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)   \n*   [Recommended Articles](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888#)\n*   [Related Info Link](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888#related)\n\n    *   [Google Scholar](http:\u002F\u002Fscholar.google.com\u002Fscholar?q=A%20Feature%20Enhancement%20Framework%20for%20Joint%20Mango%20Fruit%20and%20Stem%20Detection%20in%20Complex%20Orchard%20Environments)\n\n*   [More by Authors Links](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888#authors)\n\n    *   [on DOAJ](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)\n    \n\n        *   [Lu, J.](http:\u002F\u002Fdoaj.org\u002Fsearch\u002Farticles?source=%7B%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22%5C%22Jiahuan%20Lu%5C%22%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22bibjson.author.name%22%7D%7D%7D)\n\n        *   [Deng, Q.](http:\u002F\u002Fdoaj.org\u002Fsearch\u002Farticles?source=%7B%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22%5C%22Qihan%20Deng%5C%22%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22bibjson.author.name%22%7D%7D%7D)\n\n        *   [Zheng, W.](http:\u002F\u002Fdoaj.org\u002Fsearch\u002Farticles?source=%7B%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22%5C%22Weiping%20Zheng%5C%22%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22bibjson.author.name%22%7D%7D%7D)\n\n        *   [Cai, B.](http:\u002F\u002Fdoaj.org\u002Fsearch\u002Farticles?source=%7B%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22%5C%22Binglong%20Cai%5C%22%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22bibjson.author.name%22%7D%7D%7D)\n\n        *   [Zeng, S.](http:\u002F\u002Fdoaj.org\u002Fsearch\u002Farticles?source=%7B%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22%5C%22Shan%20Zeng%5C%22%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22bibjson.author.name%22%7D%7D%7D)\n\n        *   [Li, J.](http:\u002F\u002Fdoaj.org\u002Fsearch\u002Farticles?source=%7B%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22%5C%22Jiehao%20Li%5C%22%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22bibjson.author.name%22%7D%7D%7D)\n\n    *   [on Google Scholar](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)\n    \n\n        *   [Lu, J.](http:\u002F\u002Fscholar.google.com\u002Fscholar?q=Jiahuan%20Lu)\n\n        *   [Deng, Q.](http:\u002F\u002Fscholar.google.com\u002Fscholar?q=Qihan%20Deng)\n\n        *   [Zheng, W.](http:\u002F\u002Fscholar.google.com\u002Fscholar?q=Weiping%20Zheng)\n\n        *   [Cai, B.](http:\u002F\u002Fscholar.google.com\u002Fscholar?q=Binglong%20Cai)\n\n        *   [Zeng, S.](http:\u002F\u002Fscholar.google.com\u002Fscholar?q=Shan%20Zeng)\n\n        *   [Li, J.](http:\u002F\u002Fscholar.google.com\u002Fscholar?q=Jiehao%20Li)\n\n    *   [on PubMed](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)\n    \n\n        *   [Lu, J.](http:\u002F\u002Fwww.pubmed.gov\u002F?cmd=Search&term=Jiahuan%20Lu)\n\n        *   [Deng, Q.](http:\u002F\u002Fwww.pubmed.gov\u002F?cmd=Search&term=Qihan%20Deng)\n\n        *   [Zheng, W.](http:\u002F\u002Fwww.pubmed.gov\u002F?cmd=Search&term=Weiping%20Zheng)\n\n        *   [Cai, B.](http:\u002F\u002Fwww.pubmed.gov\u002F?cmd=Search&term=Binglong%20Cai)\n\n        *   [Zeng, S.](http:\u002F\u002Fwww.pubmed.gov\u002F?cmd=Search&term=Shan%20Zeng)\n\n        *   [Li, J.](http:\u002F\u002Fwww.pubmed.gov\u002F?cmd=Search&term=Jiehao%20Li)\n\n[Article Views](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888#metrics)\n\n[Citations-](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888#metrics)\n\n*   [Table of Contents](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888#table_of_contents)\n\n \n    *   [Abstract](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888#)\n\nAltmetric[_share_ Share](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888# \"Share\")[_announcement_ Help](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888# \"Help\")[_format\\_quote_ Cite](javascript:void(0);)\n\n## Need Help?\n\n### Support\n\nFind support for a specific problem in the support section of our website.\n\n[Get Support](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fcontactform)\n\n### Feedback\n\nPlease let us know what you think of our products and services.\n\n[Give Feedback](https:\u002F\u002Fwww.mdpi.com\u002Ffeedback\u002Fsend)\n\n### Information\n\nVisit our dedicated information section to learn more about MDPI.\n\n[Get Information](https:\u002F\u002Fwww.mdpi.com\u002Fauthors)\n\n[_clear_](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)\n\n## JSmol Viewer\n\n[_clear_](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)\n\n_first\\_page_\n\n[Download PDF](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888\u002Fpdf?version=1788183021)\n\n_settings_\n\n[Order Article Reprints](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888\u002Freprints)\n\n Font 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\n**This is an early access version, the complete PDF, HTML, and XML versions will be available soon.**\n\nOpen Access Article\n\n# A Feature Enhancement Framework for Joint Mango Fruit and Stem Detection in Complex Orchard Environments\n\n by \n\n Jiahuan Lu\n\n![Image 9](https:\u002F\u002Fwww.mdpi.com\u002Fprofiles\u002F3614159\u002Fthumb\u002FJiahuan_Lu.png)Jiahuan Lu\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F3614159?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Jiahuan%20Lu)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Jiahuan+Lu%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Jiahuan+Lu)\n\n 1,2[](mailto:jhlu@scau.edu.cn)[![Image 10: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788181901)](https:\u002F\u002Forcid.org\u002F0009-0004-3223-6453), \n\n Qihan Deng\n\n![Image 11](https:\u002F\u002Fwww.mdpi.com\u002Fbundles\u002Fmdpisciprofileslink\u002Fimg\u002Funknown-user.png)Qihan Deng\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F5504892?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Qihan%20Deng)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Qihan+Deng%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Qihan+Deng)\n\n 1,2, \n\n Weiping Zheng\n\n![Image 12](https:\u002F\u002Fwww.mdpi.com\u002Fbundles\u002Fmdpisciprofileslink\u002Fimg\u002Funknown-user.png)Weiping Zheng\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002Fauthor\u002FVWh5MW9JVG9WbFhCRlFXN05Ybm5BUm9NVGI2QlFDZ1VVSkl2bmZiTjMzbz0=?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Weiping%20Zheng)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Weiping+Zheng%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Weiping+Zheng)\n\n 1,2, \n\n Binglong Cai\n\n![Image 13](https:\u002F\u002Fwww.mdpi.com\u002Fbundles\u002Fmdpisciprofileslink\u002Fimg\u002Funknown-user.png)Binglong Cai\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002Fauthor\u002FUEhjMTJLRVNvZktqTFc3cFV2VHBGNGYwaXRncVVHSEk0bU93YzhJQVptOD0=?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Binglong%20Cai)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Binglong+Cai%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Binglong+Cai)\n\n 1,2, \n\n Shan Zeng\n\n![Image 14](https:\u002F\u002Fwww.mdpi.com\u002Fbundles\u002Fmdpisciprofileslink\u002Fimg\u002Funknown-user.png)Shan Zeng\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F2204687?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Shan%20Zeng)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Shan+Zeng%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Shan+Zeng)\n\n 1,2[](mailto:shanzeng@scau.edu.cn)[![Image 15: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788181901)](https:\u002F\u002Forcid.org\u002F0000-0002-2497-3363) and \n\n Jiehao Li\n\n![Image 16](https:\u002F\u002Fwww.mdpi.com\u002Fprofiles\u002F893272\u002Fthumb\u002FJiehao_Li.jpg)Jiehao Li\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F893272?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Jiehao%20Li)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Jiehao+Li%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Jiehao+Li)\n\n 1,2,*[](mailto:jiehao.li@scau.edu.cn)[![Image 17: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1788181901)](https:\u002F\u002Forcid.org\u002F0000-0002-4946-4434)\n\n1\n\nCollege of Engineering, South China Agricultural University, Guangzhou 510642, China\n\n2\n\nNational Key Laboratory of Agricultural Equipment Technology, South China Agricultural University, Guangzhou 510642, China\n\n*\n\nAuthor to whom correspondence should be addressed. \n\n_Agriculture_**2026**, _16_(17), 1888; https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16171888 (registering DOI) \n\nSubmission received: 30 July 2026 \u002F Revised: 27 August 2026 \u002F Accepted: 28 August 2026 \u002F Published: 31 August 2026\n\n (This article belongs to the Special Issue [Smart Sensor-Based Systems for Crop Monitoring](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Fagriculture\u002Fspecial_issues\u002F84MWNN99A6))\n\n[Download _keyboard\\_arrow\\_down_](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888)\n\n[Download PDF](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888\u002Fpdf?version=1788183021)\n\n[Download Supplementary Material](javascript:void(0);)\n\n[Versions Notes](https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1888\u002Fnotes)\n\n## Abstract\n\n Reliable joint detection of mango fruits and stems is an essential upstream perception task for robotic harvesting, but remains challenging because stems are small, slender, frequently occluded, and visually degraded by illumination variation. This study proposes MangoNET, a YOLOv11n-based framework for joint mango fruit and stem detection in complex orchard environments. A P2 high-resolution detection head preserves fine spatial information for small targets, while SPPF-ELAN aggregates local and contextual features for partially visible objects. SENet recalibrates channel responses under illumination variation, and WIoU v3 regulates bounding-box samples with different localization qualities. A dataset containing 1782 original images of Tainong and Jinhuang mangoes was collected from two orchards and data augmentation was applied only to the training set, increasing its size from 1172 to 2886 images through rotation, contrast adjustment, and Gaussian noise addition. MangoNET achieved fruit and stem F1-scores of 0.920 and 0.916, respectively, with mAP50 and mAP50–95 values of 0.941 and 0.690. Compared with YOLOv11n, mAP50 and mAP50–95 increased by 1.6 and 2.9 percentage points, respectively, while stem recall increased from 0.877 to 0.906. Source-image-independent five-fold cross-validation yielded mean mAP50 and mAP50–95 values of 0.944 and 0.711, respectively. Pilot evaluations using images acquired by a UAV and an RGB-D camera in a geographically distinct orchard suggested that MangoNET could maintain detection performance in a different orchard environment. MangoNET s","Agriculture 2026, 16(17), 1888","2026-08-31T08:00:00Z","论文",10,false,69,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,20,18,12,4,1,"提出芒果果实与果柄联合检测的特征增强框架，方法新颖，数据详实，对农业机器人采摘有实际价值。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","农业人工智能","目标检测","芒果检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52,58],{"code":32,"score":45,"supported":46,"title":47,"abstract":50,"tags":51},19,true,[48,49],"芒果","orchard",[48],[],{"code":53,"score":54,"supported":14,"title":55,"abstract":56,"tags":57},"I2",2,[],[],[28,29],{"code":59,"score":22,"supported":14,"title":60,"abstract":61,"tags":62},"I1",[],[],[27],[64,65],"农业人工智能 智慧农业 目标检测 芒果检测","农业人工智能 智慧农业","农业人工智能智慧农业目标检测芒果检测-1188",3,{"doi":8,"openalex_id":8,"authors":69,"venue":8,"cited_by_count":70,"oa_url":8,"card":8,"direction":8,"ingested_from":71},[],0,"agent","2026-09-01T00:03:40.048486Z",{"total":74,"page":22,"page_size":74,"items":75},6,[76,135,194,256,327,376],{"id":77,"title":78,"url":79,"summary":80,"summary_zh":8,"content":8,"source_name":81,"source_url":79,"published_at":82,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":83,"score_detail":84,"sources":89,"tags":91,"primary_topic":94,"secondary_topics":95,"classification":96,"search_phrases":112,"slug":115,"view_count":67,"doi":116,"paper":117,"created_at":134},3808,"Orchard worker detection using spatial-context attention on 2D LiDAR data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104604","Orchard worker detection using spatial-context attention on 2D LiDAR data。Biosystems Engineering","Biosystems Engineering","2026-09-30T00:00:00Z",79,{"impact":85,"substance":86,"depth":19,"authority":87,"freshness":13,"relevant":22,"comment":88},16,21,14,"提出基于二维激光雷达与空间上下文注意力的果园工人检测方法，属农业人工智能与智能农机细分领域的方法创新，具备一定专业参考价值。",[90],{"name":81,"url":79},[27,28,29,92,93],"果园管理","激光雷达",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":97},{"candidates":98,"agricultural_evidence":46},[99,104,108],{"code":32,"score":100,"supported":46,"title":101,"abstract":102,"tags":103},11,[49],[49],[],{"code":53,"score":54,"supported":14,"title":105,"abstract":106,"tags":107},[],[],[28,29],{"code":59,"score":22,"supported":14,"title":109,"abstract":110,"tags":111},[],[],[27],[113,114],"2D LiDAR 果园 工人检测","农业人工智能 智慧农业 果园管理 激光雷达","2DLiDAR果园工人检测-3808","10.1016\u002Fj.biosystemseng.2026.104604",{"doi":116,"openalex_id":118,"authors":119,"venue":81,"cited_by_count":70,"oa_url":79,"card":8,"direction":8,"ingested_from":133},"W7214801786",[120,122,124,127,130],{"name":121,"orcid":8},"Jintack Jeon",{"name":123,"orcid":8},"Huijong Chung",{"name":125,"orcid":126},"Gookhwan Kim","https:\u002F\u002Forcid.org\u002F0000-0002-7278-3476",{"name":128,"orcid":129},"Yong–Joo Kim","https:\u002F\u002Forcid.org\u002F0000-0002-1212-9018",{"name":131,"orcid":132},"Dae-Hyun Lee","https:\u002F\u002Forcid.org\u002F0000-0001-9544-5974","openalex","2026-09-30T23:30:05.310491Z",{"id":136,"title":137,"url":138,"summary":139,"summary_zh":8,"content":8,"source_name":140,"source_url":8,"published_at":141,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":142,"score_detail":143,"sources":148,"tags":150,"primary_topic":153,"secondary_topics":154,"classification":155,"search_phrases":182,"slug":184,"view_count":67,"doi":8,"paper":185,"created_at":193},2490,"Lightweight Detection of Blueberries at Different Maturity Stages in Complex Orchard Environments(YOLOv12n+SHSA2C2f+知识蒸馏)","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1949","云南农业大学大数据学院联合云南省农业大数据工程技术研究中心 2026 年 9 月 10 日在《Agriculture》发表。针对小果、密集、遮挡、色彩过渡细微等复杂田间条件下的蓝莓成熟期检测难题,构建 4680 图像 60573 标注框田间数据集,重新设计 YOLOv12n 的 P3\u002FP4\u002FP5 检测结构为 P2\u002FP3\u002FP4 架构,引入轻量化 SHSA2C2f 单头自注意力特征增强模块,DINOv3 (ViT-S\u002F16) 引导的非对称知识蒸馏。精度导向 M4 模型 mAP@0.5 达 92.68%±0.04%、mAP@0.5:0.95 86.38%±0.10%,参数量仅 0.79 M;经 TensorRT FP16 转换后保留 92.75% mAP@0.5,可在 Jetson Orin Nano 上以 15.9 FPS 运行。","MDPI Agriculture 2026-09-10","2026-09-09T16:00:00Z",80,{"impact":19,"substance":144,"depth":19,"authority":145,"freshness":146,"relevant":22,"comment":147},23,13,8,"面向复杂果园的蓝莓成熟期轻量化检测研究，方法新颖、数据规模扎实且给出边缘部署实测性能，具备较高专业参考价值。",[149],{"name":140,"url":138},[27,28,151,29,152],"边缘计算","蓝莓",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":156},{"candidates":157,"agricultural_evidence":46},[158,163,169,173,177],{"code":32,"score":159,"supported":46,"title":160,"abstract":161,"tags":162},9,[49],[],[152],{"code":164,"score":67,"supported":46,"title":165,"abstract":166,"tags":168},"I6",[],[167],"农业大数据",[],{"code":53,"score":54,"supported":14,"title":170,"abstract":171,"tags":172},[],[],[28,29],{"code":59,"score":22,"supported":14,"title":174,"abstract":175,"tags":176},[],[],[27],{"code":178,"score":22,"supported":14,"title":179,"abstract":180,"tags":181},"I5",[],[],[151],[183,65],"农业人工智能 智慧农业 目标检测 边缘计算","农业人工智能智慧农业目标检测边缘计算-2490",{"doi":8,"openalex_id":8,"authors":186,"venue":8,"cited_by_count":70,"oa_url":8,"card":187,"direction":191,"ingested_from":71},[],{"tldr":188,"method":189,"finding":190,"direction":191,"opportunity":192},"提出轻量YOLOv12n改进模型，实现复杂果园中多成熟期蓝莓的实时检测。","4680张田间图像数据集，P2\u002FP3\u002FP4结构、SHSA2C2f模块与DINOv","M4模型mAP@0.5达92.68%，仅0.79M参数，Jetson Orin Nano上15.9 ","农业人工智能与决策模型","可探索多作物通用轻量检测框架，或结合成熟度计数用于产量预测与采摘决策。","2026-09-15T00:04:27.061523Z",{"id":195,"title":196,"url":197,"summary":198,"summary_zh":199,"content":8,"source_name":200,"source_url":197,"published_at":201,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":83,"score_detail":202,"sources":206,"tags":208,"primary_topic":211,"secondary_topics":212,"classification":213,"search_phrases":235,"slug":238,"view_count":67,"doi":239,"paper":240,"created_at":255},1382,"Instance Segmentation and Multi-Object Tracking for Fruit Quality Grading and Dynamic Yield Estimation in Egyptian Citrus Orchards","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102529","Egypt ranks among the world’s leading citrus producers and exporters; however, orchard operations remain constrained by subjective, labor-intensive manual quality grading and time-consuming yield estimation that require specialized expertise. This study presents an AI-driven orchard-intelligence framework for automated fruit quality grading and dynamic yield estimation under the unstructured conditions of Egyptian orange farms. To address the lack of in-field public datasets, a domain-specific instance-segmentation dataset of 618 original pixel-level annotated images, expanded to 1,545 augmented training images, and 15 videos was constructed. This dataset was used to benchmark single-stage segmentation models (YOLOv8, YOLOv11) against a two-stage (Mask R-CNN), alongside a transformer-based bounding-box detection (RT-DETR), under dense canopy occlusion, clustered fruit instances, and variable natural illumination. Each fine-tuned model was evaluated across two stages: static quality grading and dynamic yield estimation. For static grading (Good vs. Defective), YOLOv11l ranked among the top tier of evaluated segmenters, reaching mean Average Precision (mAP) of 0.932 with balanced precision-recall behavior, while maintaining real-time feasibility at 26.1 ms per image. For dynamic yield estimation, a multi-object tracking pipeline was implemented using BoT-SORT with camera-motion compensation and a virtual line-crossing counting mechanism. The evaluation reveals a three-way dissociation: the highest-mAP static segmenter (YOLOv11l), the best dynamic total counter (RT-DETR, WMAPE = 5.66%, despite the lowest box-level static accuracy), and the best quality-resolved dynamic grader (YOLOv11s, WMAPE = 6.97% at 14.2 ms per frame) are three different systems; RT-DETR did not resolve fruit quality reliably under motion. Counting accuracy and grading capability are thus separable properties requiring separate evaluation. Overall, the framework enables objective fruit quality assessment and actionable in-field fruit counting to support harvest logistics and labor planning, providing a practical foundation for smart agriculture in the Egyptian citrus sector.","埃及位居全球领先的柑橘生产国和出口国之列；然而，果园作业仍受制于主观性强、劳动密集的人工质量分级以及耗时且需专业技能的产量估算。本研究提出了一种人工智能驱动的果园智能框架，用于在埃及橙园的非结构化条件下实现自动化果实质量分级和动态产量估算。为解决田间公开数据集缺乏的问题，构建了一个领域特定的实例分割数据集，包含618张原始像素级标注图像，扩充至1545张增强训练图像，以及15段视频。该数据集用于在密集树冠遮挡、果实簇生和自然光照多变条件下，对单阶段分割模型（YOLOv8、YOLOv11）与两阶段模型（Mask R-CNN）进行基准测试，同时对比基于Transformer的边界框检测模型（RT-DETR）。每个微调模型均经过两个阶段的评估：静态质量分级和动态产量估算。在静态分级（优质与缺陷）中，YOLOv11l在评估的分割模型中位居前列，平均精度（mAP）达到0.932，精确率与召回率表现均衡，同时以每张图像26.1毫秒的处理速度保持实时可行性。在动态产量估算方面，采用基于BoT-SORT的多目标跟踪流程，结合相机运动补偿和虚拟线跨越计数机制。评估结果揭示了三重分离现象：静态分割精度最高的模型（YOLOv11l）、动态总数计数最优的模型（RT-DETR，加权平均绝对百分比误差WMAPE = 5.66%，尽管其框级静态精度最低），以及质量分辨动态分级最佳的模型（YOLOv11s，每帧14.2毫秒时WMAPE = 6.97%）是三个不同的系统；RT-DETR在运动条件下无法可靠分辨果实质量。因此，计数精度和分级能力是可分离的属性，需要分别评估。总体而言，该框架实现了客观的果实质量评估和可操作的田间果实计数，以支持采收物流和劳动力规划，为埃及柑橘产业的智慧农业提供了实用基础。","Smart Agricultural Technology","2026-09-01T00:00:00Z",{"impact":19,"substance":203,"depth":45,"authority":145,"freshness":204,"relevant":22,"comment":205},22,7,"针对埃及柑橘园提出AI驱动的品质分级与动态产量估算框架，方法新颖且数据详实，对智慧农业有参考价值。",[207],{"name":200,"url":197},[27,28,29,209,210],"柑橘","产量估算",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":214},{"candidates":215,"agricultural_evidence":46},[216,221,226,230],{"code":32,"score":19,"supported":46,"title":217,"abstract":219,"tags":220},[218],"citrus",[209,49,218],[209],{"code":59,"score":204,"supported":46,"title":222,"abstract":223,"tags":225},[],[27,224],"smart agriculture",[27],{"code":53,"score":54,"supported":14,"title":227,"abstract":228,"tags":229},[],[],[28,29],{"code":231,"score":22,"supported":14,"title":232,"abstract":233,"tags":234},"I3",[],[],[210],[236,237],"农业人工智能 产量估算 智慧农业 目标检测","农业人工智能 产量估算","农业人工智能产量估算智慧农业目标检测-1382","10.1016\u002Fj.atech.2026.102529",{"doi":239,"openalex_id":241,"authors":242,"venue":200,"cited_by_count":70,"oa_url":197,"card":249,"direction":253,"ingested_from":133},"W7204959516",[243,245,247],{"name":244,"orcid":8},"Saleh A. Farhoud",{"name":246,"orcid":8},"Yasmine A. Zaghloul",{"name":248,"orcid":8},"Omar M. Shehata",{"tldr":250,"method":251,"finding":252,"direction":253,"opportunity":254},"构建柑橘果园AI框架，实现果实质量分级与动态产量估计，并评估多种分割与跟踪模型。","构建实例分割数据集，微调YOLOv8\u002Fv11、Mask R-CNN、RT-DET","静态分级最优为YOLOv11l，动态计数最优为RT-DETR，质量动态分级最优为YOLOv11s，计","智慧农业 \u002F 农业物联网","可探索多任务协同优化，如设计统一模型同时满足静态分级与动态计数，或引入时序信息提升运动场景下的质量分级鲁棒性。","2026-09-02T23:30:04.506009Z",{"id":257,"title":258,"url":259,"summary":260,"summary_zh":261,"content":8,"source_name":262,"source_url":259,"published_at":263,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":264,"score_detail":265,"sources":268,"tags":270,"primary_topic":273,"secondary_topics":274,"classification":275,"search_phrases":301,"slug":304,"view_count":70,"doi":305,"paper":306,"created_at":326},5401,"A HIERARCHICAL DISTRIBUTED EDGE AI FRAMEWORK FOR REAL-TIME APPLE LEAF DISEASE CLASSIFICATION","https:\u002F\u002Fdoi.org\u002F10.22190\u002Ffuacr260506002g","The application of artificial intelligence (AI) in agriculture has transformed crop management, particularly in the detection of plant diseases. Apple trees, a key agricultural commodity, are vulnerable to various diseases, which can severely impact crop yields. Traditional disease detection methods, often reliant on manual inspections, are time-consuming and prone to error. In response, this study proposes a novel two-stage edge AI framework for apple leaf disease classification that integrates RGB imagery with distributed edge computing. The first stage performs binary classification to distinguish between healthy and diseased leaves, achieving an F1 score of 95.50%. This optimization is achieved by utilizing lightweight models on edge devices, thereby enhancing computational efficiency. The second stage employs a consensus-based multiclass classification approach, achieving a 96.44% F1 score in identifying specific diseases. This dual-stage system, supported by an edge AI architecture, ensures real-time disease detection while minimizing resource consumption and enhancing scalability in large-scale agricultural environments. The full system achieved a 95.50% accuracy and F1 score on the special dataset. The results demonstrate the system’s efficacy in controlled laboratory conditions, offering a promising solution for precise, real-time agricultural monitoring. This study underscores the potential of distributed edge AI systems for scalable and efficient disease detection in precision agriculture.","人工智能（AI）在农业中的应用已深刻改变了作物管理方式，尤其是在植物病害检测方面。苹果树作为重要的农业经济作物，易受多种病害侵袭，严重影响作物产量。传统的病害检测方法通常依赖人工检查，耗时且易出错。为此，本研究提出了一种新颖的两阶段边缘AI框架，用于苹果叶片病害分类，该框架将RGB图像与分布式边缘计算相结合。第一阶段执行二分类以区分健康叶片与患病叶片，F1分数达到95.50%。该优化通过在边缘设备上采用轻量级模型实现，从而提升了计算效率。第二阶段采用基于共识的多类分类方法，在识别具体病害方面F1分数达到96.44%。该双阶段系统在边缘AI架构的支持下，实现了实时病害检测，同时最大限度地降低了资源消耗，并增强了在大规模农业环境中的可扩展性。完整系统在专用数据集上达到了95.50%的准确率和F1分数。结果表明，该系统在受控实验室条件下具有有效性，为精准、实时的农业监测提供了一种有前景的解决方案。本研究凸显了分布式边缘AI系统在精准农业中实现可扩展且高效病害检测的潜力。","Facta Universitatis Series Automatic Control and Robotics","2026-10-18T00:00:00Z",76,{"impact":19,"substance":86,"depth":266,"authority":20,"freshness":146,"relevant":22,"comment":267},17,"该研究提出两级边缘AI框架，在苹果叶片病害识别上取得95%以上F1值，方法新颖、数据可靠，对精准农业实时监测有参考价值，但尚处实验室验证阶段，产业影响有限。",[269],{"name":262,"url":259},[27,28,151,271,272],"作物病害识别","苹果病害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":276},{"candidates":277,"agricultural_evidence":46},[278,284,290,295],{"code":32,"score":19,"supported":46,"title":279,"abstract":281,"tags":283},[280],"apple",[34,282,280],"苹果",[272],{"code":59,"score":204,"supported":46,"title":285,"abstract":286,"tags":289},[],[287,288],"精准农业","precision agriculture",[27],{"code":178,"score":204,"supported":46,"title":291,"abstract":292,"tags":294},[],[151,293],"edge computing",[151],{"code":53,"score":296,"supported":46,"title":297,"abstract":298,"tags":300},5,[],[299],"artificial intelligence",[271,28],[302,303],"苹果叶片病害 边缘AI","分布式边缘计算 病害分类","苹果叶片病害边缘AI-5401","10.22190\u002Ffuacr260506002g",{"doi":305,"openalex_id":307,"authors":308,"venue":262,"cited_by_count":70,"oa_url":259,"card":321,"direction":191,"ingested_from":133},"W7222964752",[309,312,315,318],{"name":310,"orcid":311},"Milan Grujev","https:\u002F\u002Forcid.org\u002F0009-0002-3211-6844",{"name":313,"orcid":314},"Dragan H. Stojanovic","https:\u002F\u002Forcid.org\u002F0000-0003-4893-3814",{"name":316,"orcid":317},"Miloš Ilić","https:\u002F\u002Forcid.org\u002F0000-0002-9696-2874",{"name":319,"orcid":320},"Aleksandar Milosavljević","https:\u002F\u002Forcid.org\u002F0000-0003-3057-7727",{"tldr":322,"method":323,"finding":324,"direction":253,"opportunity":325},"提出两级边缘AI框架，用RGB图像实时分类苹果叶片病害。","分布式边缘计算+轻量模型，先二分类再共识多分类。","两级系统准确率与F1均达95.50%，多分类F1为96.44%。","仅在受控实验室验证，可延伸至田间复杂光照与多作物泛化及边缘能耗优化。","2026-10-20T23:30:33.830748Z",{"id":328,"title":329,"url":330,"summary":331,"summary_zh":8,"content":8,"source_name":332,"source_url":8,"published_at":333,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":334,"score_detail":335,"sources":337,"tags":339,"primary_topic":342,"secondary_topics":343,"classification":344,"search_phrases":364,"slug":367,"view_count":70,"doi":8,"paper":368,"created_at":375},5130,"DefoliationCAM:面向葡萄藤食叶严重度实时评估的边缘AI系统 [预印本]","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.20879","Fuchen Li等提出DefoliationCAM(D-CAM),一种太阳能供电的边缘AI系统,用于估算葡萄藤树冠食叶程度并通过LoRa将结果传输至Web仪表盘。流水线使用YOLOX-Nano在4K图像中定位葡萄藤主干,并使用基于MobileNet的轻量级通道注意力分类器对320×320像素的斑块进行分类。29,674个田间采集斑块被标注为背景或四种损伤等级之一;合并中度和重度类别将类别不平衡比从约40:1降至8.8:1。合成增强使macro-F1从0.768提升至0.838。分类器以更少的参数量(1.08M vs 23.52M)在macro-F1上优于ResNet-50。利用TensorRT FP16在NVIDIA Jetson Orin Nano上每张4K图像处理时间约1.5秒,完整运行周期57-60秒。两台设备为期五周的田间部署验证了自主太阳能运行、LoRa通信和远程食叶监测的可行性。","arXiv 2610.20879 2026 年 10 月 14 日","2026-10-14T00:00:00Z",75,{"impact":85,"substance":203,"depth":19,"authority":100,"freshness":146,"relevant":22,"comment":336},"预印本提出太阳能边缘AI系统，实现葡萄藤食叶严重度实时评估与LoRa远程监测，方法新颖、数据规模大、田间验证充分，具智慧农业应用价值。",[338],{"name":332,"url":330},[27,28,151,340,341],"葡萄藤","病害评估",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":345},{"candidates":346,"agricultural_evidence":46},[347,352,356,360],{"code":32,"score":100,"supported":46,"title":348,"abstract":350,"tags":351},[349],"葡萄",[349],[],{"code":59,"score":22,"supported":14,"title":353,"abstract":354,"tags":355},[],[],[27],{"code":53,"score":22,"supported":14,"title":357,"abstract":358,"tags":359},[],[],[28],{"code":178,"score":22,"supported":14,"title":361,"abstract":362,"tags":363},[],[],[151],[365,366],"DefoliationCAM 葡萄藤 食叶","Jetson Orin Nano 边缘AI 葡萄","DefoliationCAM葡萄藤食叶-5130",{"doi":8,"openalex_id":8,"authors":369,"venue":8,"cited_by_count":70,"oa_url":8,"card":370,"direction":253,"ingested_from":71},[],{"tldr":371,"method":372,"finding":373,"direction":253,"opportunity":374},"提出太阳能边缘AI系统DefoliationCAM，实时评估葡萄藤食叶严重度并远程传输。","YOLOX-Nano定位主干，MobileNet通道注意力分类，TensorRT","轻量分类器macro-F1达0.838，优于ResNet-50，田间部署验证可行。","可探索多作物迁移、损伤等级细化及边缘模型自适应更新以提升泛化性。","2026-10-17T00:13:44.932340Z",{"id":377,"title":378,"url":379,"summary":380,"summary_zh":381,"content":8,"source_name":382,"source_url":379,"published_at":333,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":383,"score_detail":384,"sources":386,"tags":388,"primary_topic":392,"secondary_topics":397,"classification":403,"search_phrases":453,"slug":456,"view_count":22,"doi":457,"paper":458,"created_at":479},4988,"Hierarchical cross-modal spatiotemporal attention network for crop yield prediction in complex Himalayan agroecosystems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1933033","Introduction Accurate crop yield prediction is essential for precision agriculture, climate-resilient farm management, and timely decision-making. However, yield prediction remains challenging because of complex, nonlinear, and multi-scale interactions among crop physiology, soil conditions, weather variability, and management practices. Conventional statistical, machine learning, and deep learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, often have limitations in capturing long-range temporal dependencies, hierarchical spatial relationships, heterogeneous data modalities, and predictive uncertainty. Methods This study proposes a Hierarchical Cross-Modal Spatio-Temporal Attention Network (HCM-STAN) for crop yield prediction using a unified attention-based framework for integrating heterogeneous agro-environmental data. The architecture incorporates four principal components: (i) hierarchical spatio-temporal attention operating across tree, block, orchard, and regional scales; (ii) a cross-modal attention module for adaptive fusion of IoT-based soil sensor observations, weather data, plant physiological measurements, and drone-based multispectral imagery; (iii) a soil-aware attention head that explicitly models soil memory, hysteresis effects, and root-zone dynamics; and (iv) an uncertainty-aware prediction module combining Monte Carlo dropout and quantile regression to generate calibrated prediction intervals. The framework was evaluated using a multi-source dataset collected from Himalayan apple orchards during 2023–2026, comprising 1,247 monitored trees across three commercial orchards. Results The proposed HCM-STAN achieved an R 2 of 0.89 for four-week-ahead crop yield prediction, representing a 15.6% improvement over the CNN-LSTM baseline. Furthermore, zero-shot transfer to mango orchards achieved an R 2 of 0.67, corresponding to approximately 75% of the performance obtained under full training. Discussion The results demonstrate that integrating hierarchical spatio-temporal representation learning, cross-modal attention, soil-aware modelling, and uncertainty estimation can effectively address the heterogeneous and dynamic nature of crop yield prediction in complex mountain agroecosystems. The observed zero-shot transfer performance further indicates the potential of HCM-STAN to generalize across crop systems without crop-specific retraining. Overall, the proposed framework provides a promising foundation for reliable, uncertainty-aware yield forecasting and can support precision agriculture, climate-smart farm management, and data-driven decision-making in heterogeneous agroecosystems.","引言 准确的作物产量预测对于精准农业、气候适应性农场管理以及及时决策至关重要。然而，由于作物生理、土壤条件、天气变异和管理措施之间复杂、非线性且多尺度的相互作用，产量预测仍然具有挑战性。传统的统计方法、机器学习以及深度学习方法，包括卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆（LSTM）网络，在捕捉长程时间依赖关系、层次化空间关系、异构数据模态以及预测不确定性方面往往存在局限。方法 本研究提出了一种用于作物产量预测的层次化跨模态时空注意力网络（HCM-STAN），采用统一的基于注意力的框架来整合异构农业环境数据。该架构包含四个主要组成部分：（i）在树、地块、果园和区域尺度上运行的层次化时空注意力；（ii）用于自适应融合基于物联网的土壤传感器观测、天气数据、植物生理测量和无人机多光谱影像的跨模态注意力模块；（iii）显式建模土壤记忆、滞后效应和根区动态的土壤感知注意力头；（iv）结合蒙特卡洛 dropout 和分位数回归以生成校准预测区间的不确定性感知预测模块。该框架使用2023—2026年在喜马拉雅苹果园采集的多源数据集进行了评估，该数据集包含三个商业果园中1，247棵监测树木。结果 所提出的 HCM-STAN 在提前四周作物产量预测中达到了0.89的 R²，相较于 CNN-LSTM 基线提升了15.6%。此外，零样本迁移至芒果园达到了0.67的 R²，相当于完整训练下所获性能的约75%。讨论 结果表明，整合层次化时空表征学习、跨模态注意力、土壤感知建模和不确定性估计，能够有效应对复杂山地农业生态系统中作物产量预测的异构性和动态性。观察到的零样本迁移性能进一步表明，HCM-STAN 具有在无需作物特定再训练的情况下跨作物系统泛化的潜力。总体而言，该 p","Frontiers in Plant Science",81,{"impact":19,"substance":203,"depth":19,"authority":87,"freshness":159,"relevant":22,"comment":385},"提出分层跨模态时空注意力网络，融合物联网土壤传感、气象、生理与无人机多光谱数据，苹果园四周产量预测R²达0.89并实现芒果园零样本迁移，方法新颖、数据扎实，对智慧农业产量预报有参考价值。",[387],{"name":382,"url":379},[27,28,389,390,391],"产量预测","多模态融合","遥感",{"code":53,"slug":393,"name":394,"path":395,"parent_code":396},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[398,399],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":231,"slug":400,"name":401,"path":402,"parent_code":396},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":14,"version":40,"method":41,"evidence":404},{"candidates":405,"agricultural_evidence":46},[406,414,418,424,428,435,441,447],{"code":53,"score":100,"supported":46,"title":407,"abstract":408,"tags":413},[],[409,410,411,412],"深度学习","机器学习","machine learning","deep learning",[28,390],{"code":32,"score":159,"supported":46,"title":415,"abstract":416,"tags":417},[],[282,48,49,280],[],{"code":231,"score":146,"supported":46,"title":419,"abstract":420,"tags":423},[],[421,422],"无人机多光谱","multispectral",[389,391],{"code":59,"score":204,"supported":46,"title":425,"abstract":426,"tags":427},[],[287,288],[27],{"code":429,"score":74,"supported":46,"title":430,"abstract":431,"tags":434},"H2",[],[432,433],"土壤","soil",[],{"code":178,"score":74,"supported":46,"title":436,"abstract":437,"tags":440},[],[438,439],"物联网","IoT",[],{"code":442,"score":67,"supported":46,"title":443,"abstract":444,"tags":446},"H6",[],[445],"农业生态",[],{"code":448,"score":67,"supported":46,"title":449,"abstract":450,"tags":452},"H7",[],[451],"气候适应",[],[454,455],"喜马拉雅 苹果园 产量预测","HCM-STAN 作物产量预测","喜马拉雅苹果园产量预测-4988","10.3389\u002Ffpls.2026.1933033",{"doi":457,"openalex_id":459,"authors":460,"venue":382,"cited_by_count":70,"oa_url":379,"card":474,"direction":253,"ingested_from":133},"W7221795850",[461,464,466,468,470,472],{"name":462,"orcid":463},"Shabir Ahmad Mir","https:\u002F\u002Forcid.org\u002F0000-0003-2311-218X",{"name":465,"orcid":8},"Umarfarooque Momin",{"name":467,"orcid":8},"Sudeep Kumar E",{"name":469,"orcid":8},"Itfaq Ahmad Mir",{"name":471,"orcid":8},"Ab Rouf Khan",{"name":473,"orcid":8},"Ghulam Mohammad Mir",{"tldr":475,"method":476,"finding":477,"direction":191,"opportunity":478},"提出分层跨模态时空注意力网络HCM-STAN，融合多源农业数据预测喜马拉雅山区苹果产量。","用IoT土壤、气象、生理和无人机多光谱数据，结合分层时空与跨模态注意力及不确定性","四周前产量预测R²达0.89，比CNN-LSTM提升15.6%，零样本迁移芒果R²为0.67。","可探索跨作物零样本迁移的领域自适应机制，并验证模型在更多山地农业生态系统的泛化能力。","2026-10-15T23:30:08.750140Z"]