首页|基于随机森林与长短期记忆网络结合的蓝莓黑腹果蝇发生预测

基于随机森林与长短期记忆网络结合的蓝莓黑腹果蝇发生预测

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黑腹果蝇侵害严重影响蓝莓产量，现已成为遏制贵州省蓝莓产业发展的主要原因之一，快速、准确预测黑腹果蝇发生有利于及时采取防控措施，但目前对蓝莓园黑腹果蝇发生预测的研究尚少。为此，本研究提出了一种蓝莓黑腹果蝇发生预测模型。首先，利用Pearson相关系数分析温度、湿度、风速等相关气候特征指标与黑腹果蝇发生的相关性;然后，利用随机森林算法选出影响黑腹果蝇发生的重要气候特征指标;最后，提出一种随机森林和长短期记忆网络相结合的虫害预测模型。将该模型与其他传统模型的预测效果进行对比，结果表明其在预测黑腹果蝇发生方面表现出良好的性能，均方根误差为2。120 3，平均绝对误差为1。865 9，决定系数为0。979 5。本研究结果可为预测黑腹果蝇发生并及时采取相应防治策略提供技术支持。
Prediction of Blueberry Drosophila melanogaster Occurrence Based on Random Forest Combined with Long Short-Term Memory Network
The infestation of Drosophila melanogaster seriously affects blueberry output and has become one of the main reasons to curb the development of blueberry industry in Guizhou Province.Rapid and accurate prediction of D.melanogaster occurrence is beneficial for timely taking prevention and control measures.How-ever,there are currently few researches on occurrence prediction of D.melanogaster in blueberry orchards.Therefore,a model for predicting the occurrence of D.melanogaster in blueberry orchards was proposed in this study.Firstly,the Pearson correlation coefficient was used to analyze the correlations between meteorological factors such as temperature,humidity and wind speed and occurrence of D.melanogaster.Then,the Random Forest algorithm was used to select important climate features that affect the occurrence of D.melanogaster.Fi-nally,a pest prediction model combined with Random Forest and Long Short-Term Memory Network was pro-posed.Comparing the predictive performance of this model with other traditional models,the results showed that it performed well in D.melanogaster occurrence prediction with the root mean square error as 2.120 3,the average absolute error as 1.865 9,and the coefficient of determination as 0.979 5.The results of this study could provide technical supports for monitoring the occurrence of D.melanogaster and adopting corresponding prevention and control strategies in time.

Drosophila melanogasterBlueberryPrediction of pest occurrenceRandom ForestLong Short-Term Memory NetworkPearson correlation coefficientClimate features

高驰涵、张梅、陈哲、张群英、伍俊舟

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贵州大学电气工程学院,贵州贵阳 550025

贵州省植物园,贵州贵阳 550004

黑腹果蝇 蓝莓 虫害发生预测 随机森林 长短期记忆网络 Pearson相关系数 气候特征

国家自然科学基金项目贵州省科技支撑计划项目贵州省科学技术基金项目

62003106黔科合支撑[2022]一般133黔科合基础-ZK[2021]一般321

2024

10.14083/j.issn.1001-4942.2024.08.023

山东农业科学
山东省农业科学院,山东农学会,山东农业大学

山东农业科学

CSTPCD北大核心
影响因子：0.578
ISSN：1001-4942
年,卷(期)：2024.56(8)