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Agriculture)。","[English](https:\u002F\u002Fwww.caas.cn\u002Fen)[邮箱](https:\u002F\u002Fmail.caas.cn\u002F)[数字农科院](https:\u002F\u002Fi.caas.cn\u002F)[](https:\u002F\u002Fwww.caas.cn\u002Fcms\u002Fweb\u002Fsearch\u002Findex.jsp?siteID=cdb01dceb46e48488945d4465e90f221&aba=)\n\n官方微信 \n\n![Image 2](https:\u002F\u002Fwww.caas.cn\u002Fimages\u002F2023-01\u002F020ac4d3d8b6451782a8702c72604336.jpg)农科专家在线微信公众号\n\n![Image 3](https:\u002F\u002Fwww.caas.cn\u002Fimages\u002F2023-01\u002Fbf6579e4651e437db31a22cd2249b319.jpg)中国农科院微信公众号\n\n[![Image 4](https:\u002F\u002Fwww.caas.cn\u002Fstatic2022\u002Fimages\u002Fmenu_logo.png)](https:\u002F\u002Fwww.caas.cn\u002Findex.htm)\n\n*   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019.jpg](https:\u002F\u002Fwww.caas.cn\u002Fimages\u002F2026-09\u002Ffa69a734a4bc4551a1ea07fc7f019a58.jpg)\n\n利用光谱数据判断农产品产地时，现有方法多依赖单一算法，往往只追求预测准确度，却忽略了模型决策逻辑的解释，限制了可靠溯源模型的开发和对判断过程的理解。科研团队以中国六个地区的猕猴桃为对象，融合光谱成像技术与多模型组合策略构建产地溯源模型，并运用可解释性分析方法对最优组合模型进行分层和整体解读，在提升溯源准确率的同时，揭示了光谱数据背后判断产地的内在逻辑。\n\n该研究为破解光谱分析中数据分析模型难以解释的难题提供了系统解决思路，在关键波长选择、模型稳定性提升及预测结果可信度增强等方面具有参考价值，有助于推动光谱技术在农业生产与品质监管中的实际应用。\n\n该研究得到国家重点研发计划、中国农业科学院科技创新工程等项目支持。（通讯员 姬悦）\n\n原文链接：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.05.010\n\n打印本页\n\n关闭本页\n\n[院网信息发布与管理](https:\u002F\u002Fwww.caas.cn\u002Fywxxfbygl\u002Findex.htm)[最新动态](https:\u002F\u002Fwww.caas.cn\u002Fywxxfbygl\u002Findex.htm)\n\n*   [黑土地侵蚀沟时空演变规律获揭示](https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fkyhd\u002F1b6aad7e04244664a16d4fb7847890d7.htm)2026-09-07  \n*   [时代·人民·文明——中国元首外交的思想启迪](https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fszyw\u002F1c34df72522047cda6b6ee907c331b85.htm)2026-09-06  \n*   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