超光谱成像用于估计叶子,花朵和水果的宏观营养素度,并预测草产量
Cao Dinh Dung1,2,3, Stephen J Trueman4, Helen M Wallace1,2,4
1Centre for Bioinnovation, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD, 4556, Australia.
Environmental science and pollution research international
|October 19, 2023
概括
超光谱成像可以估计草植物的营养水平,帮助种植者及时做出更好的产量和质量的决定. 这项技术对优化作物营养和管理充满希望.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 植物营养 植物营养
背景情况:
- 优化草产量和质量取决于有效管理植物营养状况.
- 准确评估营养度 (,,,,) 对于知情的农业实践至关重要.
研究的目的:
- 评估超光谱成像,以估计草植物各个部分 (叶子,花朵,未成熟和成熟的果实) 的营养度.
- 评估超光谱成像在预测草植物产量方面的潜力.
- 为了确定部分最小平方回归 (PLSR) 模型对营养估计的准确性.
主要方法:
- 超光谱成像在400-1,000纳米频谱中使用.
- 开发了部分最小平方回归 (PLSR) 模型,以将光谱数据与营养度相关联.
- 使用确定系数 (R2p) 和性能与偏差 (RPD) 的比率来评估预测准确度.
主要成果:
- 超光谱成像显示了对和度的良好预测准确性,特别是在叶子和花朵中.
- 与成熟水果相比,叶子,花朵和未成熟的水果的预测准确性通常更高.
- 产量和果实质量与测试的植被指数的线性关系有限,差异植被指数显著.
结论:
- 超光谱成像是一种有前途的技术,用于非破坏性地评估草作物的营养状况.
- 这种方法可以帮助种植者做出快速,数据驱动的营养管理决策.
- 实施高光谱成像可以带来优化草产量和改善水果质量.
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