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通过使用多叶SPAD值和机器学习方法提高的营养指数估计

Yuan Wang¹, Peihua Shi², Yinfei Qian³

¹State Key Laboratory of Soil and Sustainable Agriculture, Changshu National Agro-Ecosystem Observation and Research Station, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China.

Frontiers in plant science

|December 25, 2024

概括

通过使用多叶SPAD读数和机器学习,提高了精确的米管理. 这种方法提高了叶子度 (LNC) 和营养指数 (NNI) 预测,以实现可持续农业.

科学领域:

农业科学农业科学
植物科学植物科学
数据科学数据科学数据科学

背景情况:

优化化肥对于大米产量和环境可持续性至关重要.
传统的诊断可能是劳动密集型和不那么精确.
开发用于季节性评估的高效工具对于现代农业至关重要.

研究的目的:

评估多叶SPAD测量与机器学习相结合的有效性,以改善大米中的营养诊断.
确定关键的叶子位置和统计指标,以提高叶子度 (LNC) 和营养指数 (NNI) 的预测准确度.

主要方法:

收集了从第一个到第五个完全扩展的叶子的SPAD值,遍及五个地点和15个处于关键生长阶段的水品种.
使用机器学习模型,包括随机森林和极端梯度提升,用于LNC和NNI估计.
整合统计指标 (例如,最大,中位数SPAD值) 与原始SPAD数据一起.

主要成果:

与机器学习集成的多页SPAD数据显著提高了LNC和NNI估计的准确性.
第二个完全扩展的Leaf From the Top (2LFT) 是LNC最关键的预测因素.
第三个完全扩展的Leaf From the Top (3LFT) 是NNI估计的关键.
统计SPAD指标进一步提高了预测模型的性能.

结论:

将多叶SPAD数据与先进的机器学习相结合,为米的评估提供了精确有效的方法.
这种方法支持提高使用效率,并通过有针对性的管理促进可持续的水种植.
这些发现为开发实用工具提供了基础,用于实时监测大米中的状况.

关键词:

叶子中的度在叶子中的度.机器学习是机器学习.多片叶的SPAD值可以使用.的营养指数的营养指数诊断大米气的诊断方法统计指标统计指标

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