{"title":"多光谱波段选择用于葡萄园病害检测的成像传感器设计——以黄萎病为例","authors":"H. Al-Saddik, J. Simon, O. Brousse, F. Cointault","doi":"10.1017/S2040470017000802","DOIUrl":null,"url":null,"abstract":"Disease detection and control is thus one of the main objectives of vineyard research in France. Monitoring diseases manually is fastidious and time consuming, so current research aims to develop an automatic detection of vineyard diseases. This project explored the use of a high-resolution multi-spectral camera embedded on a UAV (Unmanned Aerial Vehicle) to identify the infected zones in a field. In-field spectrometry studies were performed to identify the best spectral bands for the sensor design. The best models were found to be the function of the grapevine variety considered and the 520-600-650-690-730-750-800 nm bands were found to be the most efficient for all types of grapevines, with an overall classification accuracy of more than 94%.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"12 1","pages":"150-155"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Multispectral band selection for imaging sensor design for vineyard disease detection: case of Flavescence Dorée\",\"authors\":\"H. Al-Saddik, J. Simon, O. Brousse, F. Cointault\",\"doi\":\"10.1017/S2040470017000802\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Disease detection and control is thus one of the main objectives of vineyard research in France. Monitoring diseases manually is fastidious and time consuming, so current research aims to develop an automatic detection of vineyard diseases. This project explored the use of a high-resolution multi-spectral camera embedded on a UAV (Unmanned Aerial Vehicle) to identify the infected zones in a field. In-field spectrometry studies were performed to identify the best spectral bands for the sensor design. The best models were found to be the function of the grapevine variety considered and the 520-600-650-690-730-750-800 nm bands were found to be the most efficient for all types of grapevines, with an overall classification accuracy of more than 94%.\",\"PeriodicalId\":7228,\"journal\":{\"name\":\"Advances in Animal Biosciences\",\"volume\":\"12 1\",\"pages\":\"150-155\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Animal Biosciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1017/S2040470017000802\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Animal Biosciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/S2040470017000802","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multispectral band selection for imaging sensor design for vineyard disease detection: case of Flavescence Dorée
Disease detection and control is thus one of the main objectives of vineyard research in France. Monitoring diseases manually is fastidious and time consuming, so current research aims to develop an automatic detection of vineyard diseases. This project explored the use of a high-resolution multi-spectral camera embedded on a UAV (Unmanned Aerial Vehicle) to identify the infected zones in a field. In-field spectrometry studies were performed to identify the best spectral bands for the sensor design. The best models were found to be the function of the grapevine variety considered and the 520-600-650-690-730-750-800 nm bands were found to be the most efficient for all types of grapevines, with an overall classification accuracy of more than 94%.