Predicting the Seasonal Dynamics of Fruit Fly Anastrepha fraterculus Populations in Apple Orchards Using Artificial Neural Networks.

IF 1.7 3区 农林科学 Q2 ENTOMOLOGY
Emílio de Souza Pimentel, Jhersyka da Silva Paes, Yuri Jivago Ramos, Juliana Magalhaes Soares, Allana Grecco Guedes, Letícia Caroline da Silva Sant'Ana, Ricardo Siqueira da Silva, Marcelo Coutinho Picanço
{"title":"Predicting the Seasonal Dynamics of Fruit Fly Anastrepha fraterculus Populations in Apple Orchards Using Artificial Neural Networks.","authors":"Emílio de Souza Pimentel, Jhersyka da Silva Paes, Yuri Jivago Ramos, Juliana Magalhaes Soares, Allana Grecco Guedes, Letícia Caroline da Silva Sant'Ana, Ricardo Siqueira da Silva, Marcelo Coutinho Picanço","doi":"10.1007/s13744-025-01312-3","DOIUrl":null,"url":null,"abstract":"<p><p>The fruit fly Anastrepha fraterculus (Wiedemann) (Diptera: Tephritidae) is one of the main pests in apple orchards. Artificial neural networks (ANNs) are tools with good ability to predict phenomena such as the seasonal dynamics of pest populations. Thus, the objective of this work was to determine a prediction model for the seasonal dynamics of A. fraterculus in apple orchards using ANNs. Insect densities and climatic elements were monitored for 8 years in two commercial apple orchards. Of the 1452 ANNs determined, the one with meteorological data with a 35-day lag was selected. This ANN presented the highest correlation (0.693) between predictions and pest densities, the lowest square root mean validation error (0.066), and five neurons in the hidden layer. Among the model's predictors, wind speed and relative humidity showed positive correlations with pest density, while precipitation was negatively correlated. The predicted population curves, based on the fruiting period and temperature, reached a peak in the number of A. fraterculus individuals per trap per day. ANN was able to adequately predict pest density in different orchards, plant fruiting stages, and years. Therefore, this ANN model is promising for predicting A. fraterculus densities in apple orchards.</p>","PeriodicalId":19071,"journal":{"name":"Neotropical Entomology","volume":"54 1","pages":"94"},"PeriodicalIF":1.7000,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neotropical Entomology","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s13744-025-01312-3","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENTOMOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

The fruit fly Anastrepha fraterculus (Wiedemann) (Diptera: Tephritidae) is one of the main pests in apple orchards. Artificial neural networks (ANNs) are tools with good ability to predict phenomena such as the seasonal dynamics of pest populations. Thus, the objective of this work was to determine a prediction model for the seasonal dynamics of A. fraterculus in apple orchards using ANNs. Insect densities and climatic elements were monitored for 8 years in two commercial apple orchards. Of the 1452 ANNs determined, the one with meteorological data with a 35-day lag was selected. This ANN presented the highest correlation (0.693) between predictions and pest densities, the lowest square root mean validation error (0.066), and five neurons in the hidden layer. Among the model's predictors, wind speed and relative humidity showed positive correlations with pest density, while precipitation was negatively correlated. The predicted population curves, based on the fruiting period and temperature, reached a peak in the number of A. fraterculus individuals per trap per day. ANN was able to adequately predict pest density in different orchards, plant fruiting stages, and years. Therefore, this ANN model is promising for predicting A. fraterculus densities in apple orchards.

应用人工神经网络预测苹果园果蝇种群的季节动态
飞蛾(双翅目:飞蛾科)是苹果果园的主要害虫之一。人工神经网络(ann)是预测害虫种群季节性动态等现象的良好工具。因此,本研究的目的是利用人工神经网络建立一种预测苹果园内黑僵菌季节动态的模型。对两个商业苹果园的昆虫密度和气候要素进行了8年的监测。在确定的1452个人工神经网络中,选择具有35天滞后气象数据的人工神经网络。该人工神经网络的预测结果与害虫密度的相关性最高(0.693),均方根验证误差最低(0.066),隐含层中有5个神经元。风速、相对湿度与害虫密度呈显著正相关,降水量与害虫密度呈显著负相关。基于结果期和温度的预测种群曲线显示,每天每诱蚊器捕获的异花拟沙蝇个体数达到峰值。人工神经网络能较好地预测不同果园、植物结实期和年份的害虫密度。因此,该人工神经网络模型有望用于预测苹果园内褐蝽的密度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Neotropical Entomology
Neotropical Entomology 生物-昆虫学
CiteScore
3.30
自引率
5.60%
发文量
69
审稿时长
6-12 weeks
期刊介绍: Neotropical Entomology is a bimonthly journal, edited by the Sociedade Entomológica do Brasil (Entomological Society of Brazil) that publishes original articles produced by Brazilian and international experts in several subspecialties of entomology. These include bionomics, systematics, morphology, physiology, behavior, ecology, biological control, crop protection and acarology.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信