Joaquin J. Casanova , Nicolas T. Bergmann , Jessica E.R. Kalin , Garett C. Heineck , Ian C. Burke
{"title":"A comparison of protocols for high-throughput weeds mapping","authors":"Joaquin J. Casanova , Nicolas T. Bergmann , Jessica E.R. Kalin , Garett C. Heineck , Ian C. Burke","doi":"10.1016/j.atech.2025.101076","DOIUrl":null,"url":null,"abstract":"<div><div>Increasing herbicide resistance in the US demands novel approaches to integrated weed management, including targeted chemical use and non-chemical methods. More targeted chemical applications and non-chemical alternative methods help expose weeds to multiple modes of action, decreasing the formation of resistant populations. However, generating prescription maps and evaluating non-chemical methods require field-scale mapping of weeds. Typical methods for weeds mapping either involve laborious mapping on the ground or impractical low-altitude UAV imaging. Additionally, the literature describes an array of imaging techniques demonstrated in very select circumstances. To give clear guidelines for future research, this paper compares three imaging techniques, two weed count model types, and two ground validation methods (quadrat counts and seedbank counts) for remote weeds mapping on five sites experiencing infestations of different common weed species. Overall, the multispectral imaging techniques using Poisson count models and weed counts in quadrats as ground truth outperformed other methods and can be recommended as a pipeline for rapid mapping weeds in field crops. However, though seedbank density did not map well when using imagery, 50 seedbank samples were adequate for assessing seedbank.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101076"},"PeriodicalIF":5.7000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart agricultural technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772375525003090","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
Increasing herbicide resistance in the US demands novel approaches to integrated weed management, including targeted chemical use and non-chemical methods. More targeted chemical applications and non-chemical alternative methods help expose weeds to multiple modes of action, decreasing the formation of resistant populations. However, generating prescription maps and evaluating non-chemical methods require field-scale mapping of weeds. Typical methods for weeds mapping either involve laborious mapping on the ground or impractical low-altitude UAV imaging. Additionally, the literature describes an array of imaging techniques demonstrated in very select circumstances. To give clear guidelines for future research, this paper compares three imaging techniques, two weed count model types, and two ground validation methods (quadrat counts and seedbank counts) for remote weeds mapping on five sites experiencing infestations of different common weed species. Overall, the multispectral imaging techniques using Poisson count models and weed counts in quadrats as ground truth outperformed other methods and can be recommended as a pipeline for rapid mapping weeds in field crops. However, though seedbank density did not map well when using imagery, 50 seedbank samples were adequate for assessing seedbank.