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贝叶斯优化昆虫陷分布,以提高农业生态系统害虫监测效率

Eric Yanchenko¹, Thomas M Chappell², Anders S Huseth³

¹Global Connectivity Program, Akita International University, Akita, Japan.

Frontiers in insect science

|February 6, 2025

概括

贝叶斯优化 (BO) 改进了对农业害虫的昆虫陷放置,例如Helico. zea. 这种方法可以确定最佳的陷位置,以获得最大的信息,提高害虫监测效率,但资源有限.

科学领域:

农业昆虫学农业昆虫学
计算生态学计算生态学
虫害管理虫害管理虫害管理

背景情况:

昆虫陷网络对于农业害虫监测至关重要,但通常缺乏对效率和精度的优化.
目前的陷放置策略经常依赖于方便或固定的密度,而不是动态的生态数据.
有效的侵入性害虫监测需要根据分散模式和生态学做出快速决策.

研究的目的:

使用贝叶斯优化 (BO) 算法优化昆虫陷网络的空间分布.
为了确定陷位置,最大限度地获得信息来监测农业害虫Helicoverpa zea.
在资源有限的情况下提高害虫监测的效率.

主要方法:

使用贝叶斯优化 (BO) 算法来确定最佳的陷网络分布.
利用了四年来从北卡罗来纳州21个地点的激素陷监测数据,覆盖约7,000平方公里.
使用三年的数据训练了BO模型,并用第四年的数据验证了它.

主要成果:

BO方法成功地确定了陷位置,为害虫密度估计提供了最有价值的信息.
对固定数量的陷位置或所需的精度水平的场景实现了优化.
证明了随着数据的积累和优先级的发展,优化陷放置的能力.

结论:

贝叶斯优化 (BO) 提供了一种强大的,数据驱动的方法来优化昆虫陷网络的放置.
这种方法使得害虫监测更有效,更精确,特别是对于像Helicoverpa zea.这样的重要害虫.
为害虫管理从业人员提供信息化,资源意识的陷安置决策.

关键词:

这是一架直升机的飞行器.适应性采样采样方式生态效率生态效率.综合性害虫防治综合性害虫防治采样效率采样效率采样效率采样效率

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