[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3292":3,"related-3292":38},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":8,"paper":8,"created_at":37},3292,"AI巡田机器狗、采摘机器人、虫情监测仪！智慧农业装备亮相2026年中国农民丰收节山东主场活动","https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQwOTg2NA.html","在2026年中国农民丰收节山东主场活动现场，一批智慧农业新装备集中亮相：巡田机器狗替代人工开展田间作业，搭载多光谱、可见光相机与环境监测传感器，依托自研AI算法可精准完成病虫害识别、作物长势研判、土壤墒情监测；便携式一体虫情监测仪可灵活拆装，冬季无虫害时可拆卸收纳；地形移动作业机器人搭载机械臂，更换不同抓手就能实现果蔬采摘；管式墒情仪采用太阳能供电，直接插地即可投入使用。",null,"![Image 1](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_logo.png)![Image 2](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_shandianxinwen.png)\n*   [头条](https:\u002F\u002Fsdxw.iqilu.com\u002F)\n*   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5](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon222.png)\n\n[](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQwOTg2NA.html)\n\n[济南](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F101744.html?1&101744)[青岛](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F101745.html?1&101745)[淄博](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F101746.html?1&101746)[东营](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F101811.html?1&101811)[烟台](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F101810.html?1&101810)[潍坊](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F101809.html?1&101809)[济宁](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F101808.html?1&101808)[泰安](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F101807.html?1&101807)[威海](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F101806.html?1&101806)[日照](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F101805.html?1&101805)[临沂](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F101804.html?1&101804)[德州](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F101803.html?1&101803)[聊城](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F101802.html?1&101802)[滨州](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F101801.html?1&101801)[枣庄](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F101800.html?1&101800)[菏泽](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F101799.html?1&101799)\n\n[评论](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F107875.html?9&107875)[图片](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F107871.html?9&107871)[文娱](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F107897.html?9&107897)[房产](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F107895.html?9&107895)[专题](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002F107907.html?9&107907)\n\n![Image 6](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Fshang.png)\n\n*   [闪电号>](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Fmp.html?2&0)\n*   [专栏>](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Fmp.html?2&0&%E4%B8%93%E6%A0%8F)\n*   [乡村季风农业家](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Fmpdetail\u002F100200.html?catid=100200)\n\n AI 巡田机器狗、采摘机器人、虫情监测仪！智慧农业装备亮相2026年中国农民丰收节山东主场活动 \n\n[乡村季风农业家](javascript:;)\n\n[2026-09-22 15:43:05](javascript:;)\n\n[Video 3](https:\u002F\u002Fstream7-transcode.iqilu.com\u002F10361\u002Fsucaiku\u002F202609\u002F22\u002Ff4534bc69de84c47b8bceeb954d0626c_902.mp4)\n在 2026年中国农民丰收节山东主场活动现场，一批智慧农业新装备集中亮相，为粮食稳产增产注入科技动能。\n\n巡田机器狗替代人工开展田间作业，搭载多光谱、可见光相机与环境监测传感器，依托自研 AI 算法，可精准完成病虫害识别、作物长势研判、土壤墒情监测；便携式一体虫情监测仪可灵活拆装，冬季无虫害时可拆卸收纳，有效降低设备运维成本；地形移动作业机器人搭载机械臂，更换不同抓手就能实现果蔬采摘；管式墒情仪采用太阳能供电，直接插地即可投入使用，工业级外壳坚固耐用，保障土壤数据精准采集。\n\n从前端田间感知、终端数据分析到后端作业执行，整套设备闭环联动，科学管控田间生产，助力减少农药化肥使用，实现农田提质增产。以科技赋能农耕，用装备守护耕耘，让智慧农业护航齐鲁大地岁岁丰收！\n\n《乡村季风》记者 郑莹莹 马成波\n\n来源： 乡村季风 \n\n编辑： 郑莹莹 \n\n责编： 梁丽 \n\n审校： 张洁 \n\n主编： 方翔 \n\n 相关推荐 \n\n[![Image 7](https:\u002F\u002Fimg12.iqilu.com\u002F1\u002Fextraction\u002F202609\u002F23\u002F86b0f4ade3b24b269f107f8e38e0e8ee-1.jpg)](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMjcyNw.html)\n\n[省水省肥还增产！丰收节解锁智慧种植新利器#智能水肥机#智慧农业#中国农民丰收节#农业黑科技#节水节肥#现代化种植](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMjcyNw.html)\n\n 分享到：\n\n[![Image 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14](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_qq.png)](https:\u002F\u002Fconnect.qq.com\u002Fwidget\u002Fshareqq\u002Findex.html?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMjYyMg.html&title=%E2%80%9C%E8%BF%B7%E4%BD%A0%E2%80%9D%E5%86%9C%E6%9C%BA%E7%A1%AC%E6%A0%B8%E5%8A%A9%E5%86%9C%EF%BC%8C%E4%B8%93%E4%B8%BA%E5%B1%B1%E5%9C%B0%E4%B8%98%E9%99%B5%E6%89%93%E9%80%A0#%E4%B8%AD%E5%9B%BD%E5%86%9C%E6%B0%91%E4%B8%B0%E6%94%B6%E8%8A%82#%E5%86%9C%E6%9C%BA%E5%B1%95#%E5%86%9C%E6%9C%BA#%E5%B0%8F%E5%9E%8B%E5%86%9C%E6%9C%BA&source=&summary=%E6%9D%A5%E8%87%AA%E9%97%AA%E7%94%B5%E6%96%B0%E9%97%BB%E5%AE%A2%E6%88%B7%E7%AB%AF&desc=&pics=https:\u002F\u002Fimg12.iqilu.com\u002F10361\u002Fsucaiku\u002Fcompress\u002F202609\u002F23\u002F6f4156f96c1747b6bdf4f46a222fba67.png)\n\n[![Image 15](https:\u002F\u002Fimg12.iqilu.com\u002F10361\u002Fsucaiku\u002Fcompress\u002F202609\u002F23\u002Faf5e6c9a65864a19a0fc6905e3584006.png)](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMjAzMg.html)\n\n[东阿阿胶牵头培育的“鲁西黑驴”获批国家畜禽新品种](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMjAzMg.html)\n\n 分享到：\n\n[![Image 16](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_weixin.png)](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMjAzMg.html&title=%E4%B8%9C%E9%98%BF%E9%98%BF%E8%83%B6%E7%89%B5%E5%A4%B4%E5%9F%B9%E8%82%B2%E7%9A%84%E2%80%9C%E9%B2%81%E8%A5%BF%E9%BB%91%E9%A9%B4%E2%80%9D%E8%8E%B7%E6%89%B9%E5%9B%BD%E5%AE%B6%E7%95%9C%E7%A6%BD%E6%96%B0%E5%93%81%E7%A7%8D&pic=&summary=%E6%9D%A5%E8%87%AA%E9%97%AA%E7%94%B5%E6%96%B0%E9%97%BB%E5%AE%A2%E6%88%B7%E7%AB%AF&appkey=#_loginLayer_1596177107797)[![Image 17](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_pengyou.png)](https:\u002F\u002Fsns.qzone.qq.com\u002Fcgi-bin\u002Fqzshare\u002Fcgi_qzshare_onekey?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMjAzMg.html&title=%E4%B8%9C%E9%98%BF%E9%98%BF%E8%83%B6%E7%89%B5%E5%A4%B4%E5%9F%B9%E8%82%B2%E7%9A%84%E2%80%9C%E9%B2%81%E8%A5%BF%E9%BB%91%E9%A9%B4%E2%80%9D%E8%8E%B7%E6%89%B9%E5%9B%BD%E5%AE%B6%E7%95%9C%E7%A6%BD%E6%96%B0%E5%93%81%E7%A7%8D&summary=%E6%9D%A5%E8%87%AA%E9%97%AA%E7%94%B5%E6%96%B0%E9%97%BB%E5%AE%A2%E6%88%B7%E7%AB%AF&desc=&site=)[![Image 18](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_qq.png)](https:\u002F\u002Fconnect.qq.com\u002Fwidget\u002Fshareqq\u002Findex.html?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMjAzMg.html&title=%E4%B8%9C%E9%98%BF%E9%98%BF%E8%83%B6%E7%89%B5%E5%A4%B4%E5%9F%B9%E8%82%B2%E7%9A%84%E2%80%9C%E9%B2%81%E8%A5%BF%E9%BB%91%E9%A9%B4%E2%80%9D%E8%8E%B7%E6%89%B9%E5%9B%BD%E5%AE%B6%E7%95%9C%E7%A6%BD%E6%96%B0%E5%93%81%E7%A7%8D&source=&summary=%E6%9D%A5%E8%87%AA%E9%97%AA%E7%94%B5%E6%96%B0%E9%97%BB%E5%AE%A2%E6%88%B7%E7%AB%AF&desc=&pics=https:\u002F\u002Fimg12.iqilu.com\u002F10361\u002Fsucaiku\u002Fcompress\u002F202609\u002F23\u002Faf5e6c9a65864a19a0fc6905e3584006.png)\n\n[![Image 19](https:\u002F\u002Fimg12.iqilu.com\u002F1\u002Fextraction\u002F202609\u002F23\u002F5046dad3db3047c58c60ff919b872503-1.jpg)](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTYwMA.html)\n\n[懒人的福音！加热5分钟就能喝的乌鸡人参汤](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTYwMA.html)\n\n 分享到：\n\n[![Image 20](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_weixin.png)](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTYwMA.html&title=%E6%87%92%E4%BA%BA%E7%9A%84%E7%A6%8F%E9%9F%B3%EF%BC%81%E5%8A%A0%E7%83%AD5%E5%88%86%E9%92%9F%E5%B0%B1%E8%83%BD%E5%96%9D%E7%9A%84%E4%B9%8C%E9%B8%A1%E4%BA%BA%E5%8F%82%E6%B1%A4&pic=&summary=%E6%9D%A5%E8%87%AA%E9%97%AA%E7%94%B5%E6%96%B0%E9%97%BB%E5%AE%A2%E6%88%B7%E7%AB%AF&appkey=#_loginLayer_1596177107797)[![Image 21](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_pengyou.png)](https:\u002F\u002Fsns.qzone.qq.com\u002Fcgi-bin\u002Fqzshare\u002Fcgi_qzshare_onekey?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTYwMA.html&title=%E6%87%92%E4%BA%BA%E7%9A%84%E7%A6%8F%E9%9F%B3%EF%BC%81%E5%8A%A0%E7%83%AD5%E5%88%86%E9%92%9F%E5%B0%B1%E8%83%BD%E5%96%9D%E7%9A%84%E4%B9%8C%E9%B8%A1%E4%BA%BA%E5%8F%82%E6%B1%A4&summary=%E6%9D%A5%E8%87%AA%E9%97%AA%E7%94%B5%E6%96%B0%E9%97%BB%E5%AE%A2%E6%88%B7%E7%AB%AF&desc=&site=)[![Image 22](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_qq.png)](https:\u002F\u002Fconnect.qq.com\u002Fwidget\u002Fshareqq\u002Findex.html?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTYwMA.html&title=%E6%87%92%E4%BA%BA%E7%9A%84%E7%A6%8F%E9%9F%B3%EF%BC%81%E5%8A%A0%E7%83%AD5%E5%88%86%E9%92%9F%E5%B0%B1%E8%83%BD%E5%96%9D%E7%9A%84%E4%B9%8C%E9%B8%A1%E4%BA%BA%E5%8F%82%E6%B1%A4&source=&summary=%E6%9D%A5%E8%87%AA%E9%97%AA%E7%94%B5%E6%96%B0%E9%97%BB%E5%AE%A2%E6%88%B7%E7%AB%AF&desc=&pics=https:\u002F\u002Fimg12.iqilu.com\u002F1\u002Fextraction\u002F202609\u002F23\u002F5046dad3db3047c58c60ff919b872503-1.jpg)\n\n[![Image 23](https:\u002F\u002Fimg12.iqilu.com\u002F1\u002Fextraction\u002F202609\u002F23\u002F4f3081202f01498bb2032b3cae283fa8-1.jpg)](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTM1NA.html)\n\n[“裙膜升地温”技术让葡萄避免冻害抽干 安全越冬](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTM1NA.html)\n\n 分享到：\n\n[![Image 24](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_weixin.png)](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTM1NA.html&title=%E2%80%9C%E8%A3%99%E8%86%9C%E5%8D%87%E5%9C%B0%E6%B8%A9%E2%80%9D%E6%8A%80%E6%9C%AF%E8%AE%A9%E8%91%A1%E8%90%84%E9%81%BF%E5%85%8D%E5%86%BB%E5%AE%B3%E6%8A%BD%E5%B9%B2)[![Image 25](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_pengyou.png)](https:\u002F\u002Fsns.qzone.qq.com\u002Fcgi-bin\u002Fqzshare\u002Fcgi_qzshare_onekey?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTM1NA.html&title=%E2%80%9C%E8%A3%99%E8%86%9C%E5%8D%87%E5%9C%B0%E6%B8%A9%E2%80%9D%E6%8A%80%E6%9C%AF%E8%AE%A9%E8%91%A1%E8%90%84%E9%81%BF%E5%85%8D%E5%86%BB%E5%AE%B3%E6%8A%BD%E5%B9%B2)[![Image 26](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_qq.png)](https:\u002F\u002Fconnect.qq.com\u002Fwidget\u002Fshareqq\u002Findex.html?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTM1NA.html&title=%E2%80%9C%E8%A3%99%E8%86%9C%E5%8D%87%E5%9C%B0%E6%B8%A9%E2%80%9D%E6%8A%80%E6%9C%AF%E8%AE%A9%E8%91%A1%E8%90%84%E9%81%BF%E5%85%8D%E5%86%BB%E5%AE%B3%E6%8A%BD%E5%B9%B2)\n\n[![Image 27](https:\u002F\u002Fimg12.iqilu.com\u002F1\u002Fextraction\u002F202609\u002F23\u002F8fc17233cf67410c8a323dc82a760247-1.jpg)](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTIyOA.html)\n\n[山东临沭 花生种植高手集结 共同见证“三只虎”大肥力#农科频道#“三只虎杯”中国花生王大赛](https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTIyOA.html)\n\n 分享到：\n\n[![Image 28](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_weixin.png)](http:\u002F\u002Fservice.weibo.com\u002Fshare\u002Fshare.php?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTIyOA.html&title=%E5%B1%B1%E4%B8%9C%E4%B8%B4%E6%B2%AD)[![Image 29](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_pengyou.png)](https:\u002F\u002Fsns.qzone.qq.com\u002Fcgi-bin\u002Fqzshare\u002Fcgi_qzshare_onekey?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTIyOA.html&title=%E5%B1%B1%E4%B8%9C%E4%B8%B4%E6%B2%AD)[![Image 30](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_qq.png)](https:\u002F\u002Fconnect.qq.com\u002Fwidget\u002Fshareqq\u002Findex.html?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTIyOA.html&title=%E5%B1%B1%E4%B8%9C%E4%B8%B4%E6%B2%AD)\n\n[![Image 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34](https:\u002F\u002Fsdxw.iqilu.com\u002Fsite\u002Flighting\u002Fimages\u002Ficon_qq.png)](https:\u002F\u002Fconnect.qq.com\u002Fwidget\u002Fshareqq\u002Findex.html?url=https:\u002F\u002Fsdxw.iqilu.com\u002Fw\u002Farticle\u002FYS0yMS0xNzQxMTIwNg.html&title=%E5%AE%89)\n\n![Image 35](http:\u002F\u002Fimg8.iqilu.com\u002Fksdimgs\u002F2020\u002F09\u002F01\u002F336460350286d031fcc6edc622cca778.jpg)\n\n | \n\n 闪电热榜 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42](https:\u002F\u002Fimg11.iqilu.com\u002F21\u002F2025\u002F12\u002F22\u002F98dd3ce409421c836719db34b19a3449.png)](https:\u002F\u002Fapp-h5.iqilu.com\u002Ftopic\u002FMTgyNTE6MWUyZWY.htm?orgid=21#\u002F)\n\n[](https:\u002F\u002Fapp-h5.iqilu.com\u002Ftopic\u002FMTgyNTE6MWUyZWY.htm?orgid=21#\u002F)[聆听金声力量！聚焦第三届“金声奖”获得者彭文馨](https:\u002F\u002Fapp-h5.iqilu.com\u002Ftopic\u002FMTgyNTE6MWUyZWY.htm?orgid=21#\u002F)\n\n[![Image 43](https:\u002F\u002Fimg11.iqilu.com\u002F21\u002F2025\u002F11\u002F25\u002F62","闪电新闻 2026年09月23日","2026-09-23T00:00:00Z","报道",10,false,59,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,14,12,9,8,1,"省级丰收节上的智慧农业装备集中展示，涵盖巡田机器狗、采摘机器人、虫情监测仪等，具备一定行业参考价值，但属活动报道，信息增量有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","山东","农业机器人","田间监测",[33,34],"中国农民丰收节 山东 智慧农业装备","巡田机器狗 采摘机器人 虫情监测仪","中国农民丰收节山东智慧农业装备-3292",0,"2026-09-24T00:03:57.106255Z",{"total":39,"page":22,"page_size":39,"items":40},6,[41,89,119,148,175,209],{"id":42,"title":43,"url":44,"summary":45,"summary_zh":46,"content":8,"source_name":47,"source_url":44,"published_at":48,"category":49,"cover_url":8,"hotness":13,"is_selected":14,"score":50,"score_detail":51,"sources":56,"tags":58,"search_phrases":61,"slug":64,"view_count":36,"doi":65,"paper":66,"created_at":88},3257,"Blueberry flow and mass estimation from harvester conveyor videos via foundation-model-assisted labeling and vision-based sensing","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112432","Despite rising labor costs and growing labor shortages, adoption of machine harvesting in fresh market blueberry production has been limited by issues such as high rates of bruising and the significant loss of fruits on the ground. Machine settings strongly affect fruit quality and harvesting performance, yet they are manually configured based on operator experience, with no real-time performance feedback. Towards addressing these limitations, this paper presents a sensing proof-of-concept for analyzing berry flow through the harvester by estimating the pixel-wise proportion of blue vs green berries and volumetric flow rate from overhead RGB-D images of the harvester’s conveyor. An automated labeling pipeline combining a domain-specific YOLOv8 patch detector with SAM2-based mask generation was developed to generate a realistic large-scale berry dataset from unlabeled images captured during commercial over-the-row blueberry harvesting operations. Compared to a baseline SegFormer model trained on a smaller dataset of manually annotated images, our fine-tuned SegFormer – trained and validated on the large-scale automatically generated dataset – achieves superior performance, with green berry IoU improving from 0.377 to 0.817 and recall increasing from 0.414 to 0.911. A scene-level YOLOv8-l detection model trained and evaluated on the same dataset achieves mAP(50-95) of 0.966 and 0.898 for blue and green berries, respectively. Both models’ performance is further confirmed by an independent evaluation on a manually annotated subset of the validation images. We also compared two approaches for predicting harvest yields across five machine-setting trials: a depth-based method using the RGB-D sensor and a detection-based method using only RGB images, achieving R 2 = 0.989 and R 2 = 0.894 , respectively, against ground-truth load cell measurements from these trials. To support future research, we publicly release our datasets and models.","尽管劳动力成本上升且劳动力短缺日益加剧，但鲜食蓝莓生产中机器采收的采用仍受到果实瘀伤率高和落地果实损失严重等问题的限制。机器设置对果实品质和采收性能有很大影响，但目前仍依赖操作人员经验进行手动配置，且没有实时性能反馈。针对这些局限，本文提出了一种传感概念验证方法，通过估计采收机传送带上方RGB-D图像中蓝莓与绿果的逐像素比例以及体积流量，来分析通过采收机的果实流。我们开发了一种自动标注流程，将领域特定的YOLOv8小块检测器与基于SAM2的掩膜生成相结合，从商业跨行蓝莓采收作业期间采集的无标注图像中生成逼真的大规模蓝莓数据集。与在较小规模人工标注图像数据集上训练的基线SegFormer模型相比，我们经过微调的SegFormer——在大规模自动生成数据集上训练和验证——取得了更优性能，绿果IoU从0.377提升至0.817，召回率从0.414提升至0.911。在同一数据集上训练和评估的场景级YOLOv8-l检测模型，对蓝莓和绿果分别达到0.966和0.898的mAP(50-95)。两个模型的性能还通过在验证图像人工标注子集上的独立评估得到进一步确认。我们还比较了在五次机器设置试验中预测采收产量的两种方法：使用RGB-D传感器的基于深度的方法和使用仅RGB图像的基于检测的方法，相对于这些试验中来自称重传感器的真实测量值，分别达到R² = 0.989和R² = 0.894。为支持未来研究，我们公开了数据集和模型。","Computers and Electronics in Agriculture","2026-09-22T00:00:00Z","论文",83,{"impact":52,"substance":53,"depth":54,"authority":18,"freshness":20,"relevant":22,"comment":55},18,23,19,"该研究提出基于基础模型辅助标注与视觉传感的蓝莓流量与产量估计方法，方法新颖、数据规模大且公开数据集与模型，对智慧农业与农业机器人领域有较高参考价值。",[57],{"name":47,"url":44},[27,28,30,59,60],"机器视觉","蓝莓采收",[62,63],"蓝莓采收 机器视觉 产量估计","蓝莓收获机 传送带 视觉检测","蓝莓采收机器视觉产量估计-3257","10.1016\u002Fj.compag.2026.112432",{"doi":65,"openalex_id":67,"authors":68,"venue":47,"cited_by_count":36,"oa_url":44,"card":81,"direction":85,"ingested_from":87},"W7214013690",[69,71,73,75,78],{"name":70,"orcid":8},"A. M. Aahad",{"name":72,"orcid":8},"Pico Sankari",{"name":74,"orcid":8},"Wei Q. Yang",{"name":76,"orcid":77},"Siniša Todorović","https:\u002F\u002Forcid.org\u002F0000-0001-5793-5921",{"name":79,"orcid":80},"Joseph R. Davidson","https:\u002F\u002Forcid.org\u002F0000-0003-4388-2210",{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"用收割机传送带RGB-D视频估计蓝莓流量与质量，并公开数据集和模型。","YOLOv8+SAM2自动标注，SegFormer分割，RGB-D深度与检测法估","自动标注使绿果IoU从0.377升至0.817，深度法估产R²达0.989。","农业人工智能与决策模型","可延伸至收割机参数实时闭环调控，用视觉反馈优化机器设置以减损提质。","openalex","2026-09-23T23:30:01.713624Z",{"id":90,"title":91,"url":92,"summary":93,"summary_zh":8,"content":8,"source_name":94,"source_url":8,"published_at":95,"category":49,"cover_url":8,"hotness":13,"is_selected":14,"score":96,"score_detail":97,"sources":101,"tags":103,"search_phrases":106,"slug":109,"view_count":36,"doi":8,"paper":110,"created_at":118},3253,"Visuomotor Robotic Pruning in Planar Orchills Using Hybrid Reinforcement Learning（基于混合强化学习的平面果园视觉运动机器人修剪）","https:\u002F\u002Farxivtldr.org\u002Fabs\u002F2609.24906","arXiv发表研究：休眠期树木修剪是劳动密集型但对维持现代高产果园至关重要的工作。本文针对现代平面树形训练系统（V型棚架苹果、UFO樱桃）的修剪任务，提出端到端管线学习闭环视觉运动控制器。控制器完全使用仿真和合成生成数据进行训练，以零样本方式部署到真实果园。管线包括平面果园树网格的合成生成、基于物理的果园仿真器构建、通过运动规划自动收集成功修剪轨迹，以及结合离线演示与在线仿真推出相结合的新型混合强化学习算法。控制器使用腕部相机的光流输入，避免完整三维重建需求，连续引导切割器穿越杂乱分支环境到指定切割点并在真实果园38次物理试验中展示零样本仿真到真实迁移。在V-Trellis苹果上达到49.9%成功率，UFO樱桃上46.0%。","arXiv (Abhinav Jain, Cindy Grimm, Stefan Lee)","2026-09-20T00:00:00Z",80,{"impact":52,"substance":98,"depth":52,"authority":99,"freshness":20,"relevant":22,"comment":100},22,13,"仿真到真实零样本迁移的果园修剪机器人研究，方法新颖、数据扎实，对智慧果园机械化有参考价值。",[102],{"name":94,"url":92},[27,28,30,104,105],"强化学习","果园管理",[107,108],"V-Trellis 苹果 机器人修剪","UFO 樱桃 零样本迁移","V-Trellis苹果机器人修剪-3253",{"doi":8,"openalex_id":8,"authors":111,"venue":8,"cited_by_count":36,"oa_url":8,"card":112,"direction":85,"ingested_from":117},[],{"tldr":113,"method":114,"finding":115,"direction":85,"opportunity":116},"提出端到端视觉运动控制器，用仿真合成数据训练，零样本迁移到真实果园完成修剪。","合成数据生成、物理仿真器、运动规划收集轨迹、混合强化学习、腕部相机光流。","真实果园38次试验零样本迁移成功，苹果49.9%、樱桃46.0%成功率。","可探索多季节、多树种泛化及真实数据微调，提升复杂冠层下的鲁棒性。","agent","2026-09-23T00:04:33.854148Z",{"id":120,"title":121,"url":122,"summary":123,"summary_zh":8,"content":8,"source_name":124,"source_url":8,"published_at":48,"category":49,"cover_url":8,"hotness":13,"is_selected":125,"score":126,"score_detail":127,"sources":131,"tags":133,"search_phrases":136,"slug":139,"view_count":36,"doi":8,"paper":140,"created_at":147},3252,"具身智能农业机器人关键技术与发展趋势——上海大学苗中华等\"感知-决策-模拟-进化-诊断\"五位一体研究框架","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fnyjxxb\u002Fmulu\u002F455891.html","智慧农业(中英文)期刊发表上海大学苗中华、朱子煜、张伟、薛振锋、孙腾、张异凡、谢涛、何创新、李楠，山东农业大学苑进，北京市农林科学院赵春江，上海交通大学刘成良团队论文：全球农业智能化转型背景下，传统农业机器人面临非结构化环境、作业对象不确定性及控制参数时变等技术瓶颈，导致农业机器人实用效果差，产业化落地困难。具身智能作为实现通用人工智能的关键路径，为突破上述瓶颈提供了新范式。本文系统阐述具身智能理论驱动的农业机器人技术体系与发展路径，创新性构建\"感知-决策-模拟-进化-诊断\"五位一体研究框架。具身感知聚焦开放环境场景理解、作业目标主动感知及多模态融合感知；具身决策涵盖端到端导航、大模型驱动的机械臂操作及多机协同算法；具身模拟着力于高保真场景重建、生成及虚实迁移技术；具身进化整合无监督学习、强化学习及模仿学习机制；具身诊断构建作业状态监测与质量评估体系。","智慧农业(中英文)·上海大学",true,91,{"impact":128,"substance":53,"depth":54,"authority":129,"freshness":13,"relevant":22,"comment":130},24,15,"核心期刊论文，提出具身智能驱动的农业机器人五位一体框架，方法体系新颖、团队权威，对智慧农业技术路线有较强参考价值。",[132],{"name":124,"url":122},[27,28,30,134,135],"具身智能","多模态感知",[137,138],"上海大学 苗中华 农业机器人","具身智能 农业机器人 五位一体","上海大学苗中华农业机器人-3252",{"doi":8,"openalex_id":8,"authors":141,"venue":8,"cited_by_count":36,"oa_url":8,"card":142,"direction":85,"ingested_from":117},[],{"tldr":143,"method":144,"finding":145,"direction":85,"opportunity":146},"系统阐述具身智能驱动的农业机器人技术体系，提出五位一体研究框架。","构建'感知-决策-模拟-进化-诊断'五位一体框架，综述关键技术。","具身智能可突破非结构化环境等瓶颈，为农业机器人提供新范式。","可探索具身智能在农业非结构化场景的落地验证与虚实迁移效率提升。","2026-09-23T00:04:33.744630Z",{"id":149,"title":150,"url":151,"summary":152,"summary_zh":8,"content":8,"source_name":153,"source_url":8,"published_at":48,"category":49,"cover_url":8,"hotness":13,"is_selected":14,"score":154,"score_detail":155,"sources":157,"tags":159,"search_phrases":162,"slug":165,"view_count":36,"doi":8,"paper":166,"created_at":174},3251,"农机化研究2026(8)：基于MobileNetV4-DSFPN的芍药田间机器人视觉导航——安徽理工+皖西学院+中科院合肥物质科学研究院 mIoU 94.11% FPS 23.1","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F166165.html","农机化研究2026年第8期发表安徽理工大学徐善永、程军辉、张俊卿，皖西学院邢雪景，中国科学院合肥物质科学研究院联合论文：精准分割田间可行驶区域并实时提取导航线是实现农业机器人在田间自主作业的关键环节。针对芍药田间背景复杂、现有语义模型计算复杂度高、实时性差等问题，提出轻量化的MobileNetV4-DSFPN语义分割模型，采用改进MobileNetV4作为高效编码器；解码器部分构建基于深度可分离卷积的轻量级特征金字塔网络（DSFPN）。在芍药田间路径数据集上类别平均像素准确率（mPA）和平均交并比（mIoU）分别达到97.16%、94.11%；导航线的平均横向偏差为0.231像素，平均角度偏差为0.842°；车载计算机上平均推理速度达到23.1 FPS。","农机化研究",78,{"impact":129,"substance":98,"depth":52,"authority":99,"freshness":13,"relevant":22,"comment":156},"核心期刊论文，提出轻量化语义分割模型并给出mIoU 94.11%、23.1 FPS等实测数据，方法新颖、结论可靠，对农业机器人视觉导航有实质参考价值。",[158],{"name":153,"url":151},[27,28,30,160,161],"芍药","视觉导航",[163,164],"安徽理工大学 芍药 田间机器人","MobileNetV4 DSFPN 语义分割","安徽理工大学芍药田间机器人-3251",{"doi":8,"openalex_id":8,"authors":167,"venue":8,"cited_by_count":36,"oa_url":8,"card":168,"direction":172,"ingested_from":117},[],{"tldr":169,"method":170,"finding":171,"direction":172,"opportunity":173},"提出轻量MobileNetV4-DSFPN分割芍药田间可行驶区域并提取导航线。","改进MobileNetV4编码器+深度可分离卷积特征金字塔，芍药田间路径数据集。","mIoU达94.11%，横向偏差0.231像素，车载推理23.1 FPS。","智慧农业 \u002F 农业物联网","可探索轻量分割模型在复杂田间多作物、多光照下的泛化与边缘部署优化。","2026-09-23T00:04:33.588064Z",{"id":176,"title":177,"url":178,"summary":179,"summary_zh":180,"content":8,"source_name":181,"source_url":178,"published_at":48,"category":49,"cover_url":8,"hotness":13,"is_selected":14,"score":182,"score_detail":183,"sources":187,"tags":189,"search_phrases":192,"slug":195,"view_count":36,"doi":196,"paper":197,"created_at":208},3194,"Optimization and Analysis of Agricultural Robot Drive Systems","https:\u002F\u002Fdoi.org\u002F10.54254\u002F2753-8818\u002F2026.37178","This study takes the agricultural robot drive system as the research object and carries out optimization research from two dimensions: mechanical structure and intelligent control. At the mechanical level, redundant degrees of freedom are eliminated through topological analysis of mechanism freedom, a 'short-chain direct-drive' transmission scheme is introduced to reduce mechanical losses, and finite element topology optimization is combined to achieve lightweight design of key components. At the control level, artificial intelligence is introduced to establish a perception system based on convolutional neural networks (CNN) and multi-source information fusion, enabling accurate recognition and prediction of complex terrain. A Back Propagation (BP) neural network Proportion Integration Differentiation (PID) control strategy based on the Dung beetle Optimization (DBO)algorithm is proposed, which effectively addresses the shortcomings of traditional algorithms, such as slow convergence and large overshoot. Meanwhile, model predictive control and an improved soft actor-critic reinforcement learning algorithm are integrated to achieve online adaptive regulation of drive parameters and global optimal control to a considerable extent. The optimized drive system effectively improves the working performance and environmental adaptability of agricultural robots in complex terrain and provides new theoretical and technical ideas for the development of intelligent agricultural machinery.","本研究以农业机器人驱动系统为研究对象，从机械结构与智能控制两个维度开展优化研究。在机械层面，通过机构自由度拓扑分析去除冗余自由度，引入“短链直驱”传动方案以降低机械损耗，并结合有限元拓扑优化实现关键零部件的轻量化设计。在控制层面，引入人工智能，建立基于卷积神经网络（CNN）与多源信息融合的感知系统，实现对复杂地形的准确识别与预测；提出基于蜣螂优化（DBO）算法的BP神经网络PID控制策略，有效改善传统算法收敛慢、超调大等不足；同时融合模型预测控制与改进的柔性演员-评论家强化学习算法，实现驱动参数的在线自适应调节及较大程度上的全局最优控制。优化后的驱动系统有效提升了农业机器人在复杂地形下的作业性能与环境适应性，为智能农业机械的发展提供了新的理论与技术思路。","Theoretical and Natural Science",69,{"impact":19,"substance":184,"depth":185,"authority":13,"freshness":13,"relevant":22,"comment":186},20,17,"论文提出短链直驱与DBO-BP-PID等控制优化方案，方法新颖但偏理论，产业影响有限。",[188],{"name":181,"url":178},[27,28,30,190,191],"智能农机","驱动系统",[193,194],"农业机器人 驱动系统 优化","DBO BP神经网络 PID控制","农业机器人驱动系统优化-3194","10.54254\u002F2753-8818\u002F2026.37178",{"doi":196,"openalex_id":198,"authors":199,"venue":181,"cited_by_count":36,"oa_url":178,"card":203,"direction":85,"ingested_from":87},"W7213976318",[200],{"name":201,"orcid":202},"Ziyuan Ma","https:\u002F\u002Forcid.org\u002F0000-0002-7931-3258",{"tldr":204,"method":205,"finding":206,"direction":85,"opportunity":207},"从机械结构与智能控制两方面优化农业机器人驱动系统，提升复杂地形适应性与作业性能。","机构自由度拓扑分析、有限元拓扑优化、CNN多源融合感知、DBO-BP-PID与强","优化驱动系统有效提升农业机器人在复杂地形下的作业性能与环境适应性。","可探索轻量化驱动与在线强化学习控制在真实农田多机协同中的泛化性与能耗权衡。","2026-09-22T23:30:39.723894Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":8,"source_name":215,"source_url":212,"published_at":216,"category":49,"cover_url":8,"hotness":13,"is_selected":14,"score":217,"score_detail":218,"sources":221,"tags":223,"search_phrases":226,"slug":229,"view_count":36,"doi":230,"paper":231,"created_at":241},2929,"A REAL-TİME AI-DRİVEN AGRİCULTURAL ROVER INTEGRATİNG PLANT DİSEASE DETECTİON AND GEO-REFERENCED SOİL MOİSTURE ANALYSİS","https:\u002F\u002Fdoi.org\u002F10.30546\u002Femnaa.2026.02.28.123","This paper presents an autonomous agricultural ground robot for real-time monitoring of plant health and soil moisture in large-scale crop fields.The proposed system integrates a deep learning-based perception subsystem with autonomous navigation and a custom ground control station (GCS) to enable continuous and geo-referenced field analysis.Visual data are acquired using an onboard camera and processed in real time on a Raspberry Pi using an object detection model (ODM) to identify disease-related visual symptoms such as discoloration, deformation and leaf degradation.Each detected instance is associated with a confidence score and accurately geo-tagged using GPS data.In parallel, a contact-based soil moisture sensor performs localized measurements at fixed spatial intervals along a grid-based coverage trajectory.All perception outputs are synchronized with navigation data and transmitted via a telemetry link to the GCS where live video with detection overlays, rover trajectory, mission status and sensor telemetry are visualized and logged for postmission analysis.Field experiments conducted under real operating conditions demonstrate stable autonomous operation and achieve an overall plant disease detection accuracy of 85-90%, confirming the effectiveness of the proposed system for precision agriculture applications.","本文提出了一种用于大规模农田植物健康和土壤湿度实时监测的自主农业地面机器人。该系统将基于深度学习的感知子系统与自主导航及自定义地面控制站（GCS）相结合，实现连续且带地理参考的田间分析。视觉数据通过机载相机采集，并利用目标检测模型（ODM）在树莓派上实时处理，以识别与病害相关的视觉症状，如变色、变形和叶片退化。每个检测到的实例均关联置信度分数，并通过GPS数据精确标注地理位置。与此同时，基于接触式的土壤湿度传感器沿网格化覆盖轨迹以固定空间间隔进行局部测量。所有感知输出与导航数据同步，并通过遥测链路传输至地面控制站，在此可视化并记录带有检测叠加层的实时视频、漫游车轨迹、任务状态和传感器遥测数据，以供任务后分析。在实际运行条件下进行的田间实验证明了稳定的自主运行能力，并实现了85-90%的植物病害整体检测准确率，验证了所提系统在精准农业应用中的有效性。","Scientific Journal","2026-09-18T00:00:00Z",77,{"impact":52,"substance":219,"depth":185,"authority":99,"freshness":21,"relevant":22,"comment":220},21,"集成深度学习病害识别与地理参考土壤墒情监测的自主农业机器人论文，田间实测准确率85-90%，方法新颖且数据可靠，对精准农业有参考价值。",[222],{"name":215,"url":212},[27,28,30,224,225],"植物病害检测","土壤墒情监测",[227,228],"农业机器人 病害检测 土壤墒情","Raspberry Pi 植物病害识别","农业机器人病害检测土壤墒情-2929","10.30546\u002Femnaa.2026.02.28.123",{"doi":230,"openalex_id":232,"authors":233,"venue":215,"cited_by_count":36,"oa_url":212,"card":236,"direction":172,"ingested_from":87},"W7213559917",[234],{"name":235,"orcid":8},"Gasimov V.A., Dadashov F.H., Hasanov H.B., Huseynov N.E",{"tldr":237,"method":238,"finding":239,"direction":172,"opportunity":240},"开发实时AI农业机器人，集成植物病害检测与地理参考土壤湿度分析。","Raspberry Pi上部署目标检测模型，结合GPS和接触式土壤湿度传感器。","田间试验实现85-90%的植物病害检测准确率，并稳定自主运行。","可探索多模态传感器融合与边缘计算优化，提升复杂田间环境下的实时检测鲁棒性。","2026-09-19T23:30:11.232025Z"]