Everton Castelão Tetila , Fábio Amaral Godoy da Silveira , Anderson Bessa da Costa , Willian Paraguassu Amorim , Gilberto Astolfi , Hemerson Pistori , Jayme Garcia Arnal Barbedo
{"title":"YOLO performance analysis for real-time detection of soybean pests","authors":"Everton Castelão Tetila , Fábio Amaral Godoy da Silveira , Anderson Bessa da Costa , Willian Paraguassu Amorim , Gilberto Astolfi , Hemerson Pistori , Jayme Garcia Arnal Barbedo","doi":"10.1016/j.atech.2024.100405","DOIUrl":null,"url":null,"abstract":"<div><p>In this work, we evaluated the You Only Look Once (YOLO) architecture for real-time detection of soybean pests. We collected images of the soybean plantation in different days, locations and weather conditions, between the phenological stages R1 to R6, which have a high occurrence of insect pests in soybean fields. We employed a 5-fold cross-validation paired with four metrics to evaluate the classification performance and three metrics to evaluate the detection performance. Experimental results showed that YOLOv3 architecture trained with a batch size of 32 leads to higher classification and detection rates compared to batch sizes of 4 and 16. The results indicate that the evaluated architecture can support specialists and farmers in monitoring the need for pest control action in soybean fields.</p></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":null,"pages":null},"PeriodicalIF":6.3000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772375524000108/pdfft?md5=382788833cf85d7699f1d78ec90adcf4&pid=1-s2.0-S2772375524000108-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart agricultural technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772375524000108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
In this work, we evaluated the You Only Look Once (YOLO) architecture for real-time detection of soybean pests. We collected images of the soybean plantation in different days, locations and weather conditions, between the phenological stages R1 to R6, which have a high occurrence of insect pests in soybean fields. We employed a 5-fold cross-validation paired with four metrics to evaluate the classification performance and three metrics to evaluate the detection performance. Experimental results showed that YOLOv3 architecture trained with a batch size of 32 leads to higher classification and detection rates compared to batch sizes of 4 and 16. The results indicate that the evaluated architecture can support specialists and farmers in monitoring the need for pest control action in soybean fields.