在全年作物种植中基于功能数据分析的产量建模
Hidetoshi Matsui1, Keiichi Mochida2,3,4
1Faculty of Data Science, Shiga University, Banba, Hikone, Shiga 522-8522, Japan.
Horticulture research
|July 11, 2024
概括
使用功能数据分析 (FDA) 预测作物产量有助于优化农业管理. 该方法模拟了对草和番茄产量的环境影响,提高了气候适应性园艺的资源效率和可持续性.
科学领域:
- 农业科学 农业科学
- 统计建模 统计建模
- 园艺科学 园艺科学
背景情况:
- 有效的农业管理依赖于准确的作物产量预测.
- 纵向作物种植,特别是在像植物工厂这样的受控环境中,呈现出由环境因素影响的复杂动态.
- 了解这些动态对于优化资源使用和确保可持续生产至关重要.
研究的目的:
- 开发和验证一种方法来建模环境参数与纵向栽培中的作物产量之间的关系.
- 通过使用先进的统计技术,评估环境波动对作物产量的影响.
- 为优化生长参数和提高园艺资源效率提供见解.
主要方法:
- 用功能数据分析 (FDA) 来建模复杂的关系.
- 使用变系函数回归模型 (VCFRM) 来分析时间序列数据.
- 该模型可视化季节性变化和环境因素 (太阳辐射,温度) 与作物产量之间的动态相互作用.
主要成果:
- 在草和番茄生产中,VCFRM成功模拟了环境变量与作物产量之间的关系.
- 季节性变化和环境因素对产量的动态影响被可视化.
- 基于FDA的模型的可解释性为优化作物生产提供了可操作的见解.
结论:
- 证明了基于VCFRM的产量建模用于纵向作物种植的可行性.
- 该方法提供了稳定,高效的作物生产策略.
- 这种方法对于解决植物工厂式园艺中的气候适应性挑战至关重要.
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