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Monitor Cotton Budding Using SVM and UAV Images

文献类型： 外文期刊

作者： Xia, Lang 1 ; Zhang, Ruirui 1 ; Chen, Liping 3 ; Huang, Yanbo 5 ; Xu, Gang 1 ; Wen, Yao 3 ; Yi, Tongchuan 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Beijing Key Lab Intelligent Equipment Technol Agr, Beijing 100097, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Natl Ctr Int Res Agr Aerial Applicat Technol, Beijing 100097, Peoples R China

5.ARS, USDA, Crop Prod Syst Res Unit, POB 350, Stoneville, MS 38776 USA

关键词： SVM; budding rate; UAV

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.679； 五年影响因子：2.736 ）

ISSN：

年卷期： 2019 年 9 卷 20 期

页码：

收录情况： SCI

摘要： Featured Application the study can be used to monitor the cotton budding among large field fast. Abstract Monitoring the cotton budding rate is important for growers so that they can replant cotton in a timely fashion at locations at which cotton density is sparse. In this study, a true-color camera was mounted on an unmanned aerial vehicle (UAV) and used to collect images of young cotton plants to estimate the germination of cotton plants. The collected images were preprocessed by stitching them together to obtain the single orthomosaic image. The support-vector machine method and maximum likelihood classification method were conducted to identify the cotton plants in the image. The accuracy evaluation indicated the overall accuracy of the classification for SVM is 96.65% with the Kappa coefficient of 93.99%, while for maximum likelihood classification, the accuracy is 87.85% with a Kappa coefficient of 80.67%. A method based on the morphological characteristics of cotton plants was proposed to identify and count the overlapping cotton plants in this study. The analysis showed that the method can improve the detection accuracy by 6.3% when compared to without it. The validation based on visual interpretation indicated that the method presented an accuracy of 91.13%. The study showed that the minimal resolution of no less than 1.2 cm/pixel in practice for image collection is necessary in order to recognize cotton plants accurately.

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