A two-stage classification approach for the detection of spider mite- infested cotton using UAV multispectral imagery. Issue 10 (3rd October 2018)

• Record Type: Journal Article
• Authors: Huang, Huasheng; Deng, Jizhong; Lan, Yubin; Yang, Aqing; Deng, Xiaoling; Zhang, Lei; Wen, Sheng; Jiang, Yan; Suo, Gaoyu; Chen, Pengchao
• Is Part Of: Remote sensing letters. Volume 9:Issue 10(2018)
• Page Start: 933
• Page End: 941
• Publication Date: 2018-10-03
• Subjects: Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2018.1498600
• Languages: English
• ISSNs: 2150-704X

Abstract

ABSTRACT: Spider mites are important pests that cause severe economic damage to cotton. They feed on underside of leaves, piercing the chloroplast-containing cells, resulting in foliar damage and yield reduction. This paper proposed a two-stage classification approach for mite-infestation detection based on machine learning methods. Two cotton fields were selected for study, and the UAV imagery collection and concurrent ground investigation were conducted on July 20-21th, 2017. Mosaicking and geo-registration were performed on the collected multispectral imagery. Support Vector Machine (SVM) was used for scene classification, and a transferred Convolutional Neural Network (CNN) was applied for mite-infestation identification. Experimental results showed that our method outperformed others in terms of accuracy, which demonstrated that our approach has potential in mite-infestation detection using UAV multispectral imagery.