一个手持式多光谱装置用于评估玉米中的叶子度
Felipe Hermínio Meireles Nogueira1, Adunias Dos Santos Teixeira1, Sharon Gomes Ribeiro2
1Department of Agricultural Engineering, Federal University of Ceara, Fortaleza 60455-760, Brazil.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
一个新的多谱土壤植物分析工具 (MSPAT) 准确地测量了玉米中的叶子,性能优于商业设备. 这项技术有助于优化化肥的使用,以更好地管理作物.
科学领域:
- 农业工程 农业工程
- 植物科学 植物科学
- 遥感 遥感 遥感 遥感
背景情况:
- 精确监测叶子度 (LNC) 对于优化肥在玉米种植中的应用至关重要.
- 现有的LNC评估方法可能耗时,昂贵或缺乏现场可移植性.
- 多光谱成像为实时作物营养状况评估提供了一个有希望的非破坏性方法.
研究的目的:
- 开发和验证多谱土壤植物分析工具 (MSPAT),用于评估玉米中的叶子度.
- 将MSPAT的性能与SPAD-502叶绿素计和参考FieldSpec光谱辐射计进行比较.
- 评估MSPAT在优化玉米作物中化肥管理方面的潜力.
主要方法:
- 配备AS7265x传感器 (410-940纳米) 的MSPAT被用于收集不同肥级别和现象学阶段的玉米的光谱读数.
- 数据分析包括计算正常化比率指数 (NRIs) 和应用多变量方法,如部分最小平方回归 (PLSR) 和主要组件回归 (PCR).
- 用Kjeldahl方法确定叶子的度进行验证.
主要成果:
- 在叶子度预测方面,MSPAT获得了0.79的验证R2,超过了SPAD-502 (R2 = 0.70).
- 使用MSPAT数据的PLSR模型产生了强大的预测性能 (R2 = 0.75),与PCR相比.
- 开发的MSPAT在监测玉米营养状况方面表现出有效性,提供与参考光谱辐射计相似的结果.
结论:
- 便携式MSPAT是一种可行的和有效的工具,用于在田间条件下对玉米的叶子进行非破坏性评估.
- 与当前商用农作物气监测工具相比,MSPAT具有显著的进步.
- 这项技术支持精密农业,通过优化肥管理,潜在地提高作物产量和减少对环境的影响.
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