文献类型： 外文期刊
作者： Pan, Qian 1 ; Gao, Maofang 1 ; Wu, Pingbo 2 ; Yan, Jingwen 2 ; AbdelRahman, Mohamed A. E. 3 ;
作者机构： 1.Chinese Acad Agr Sci, Minist Agr & Rural Affairs, Key Lab Agr Remote Sensing, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China
2.Shantou Univ, Key Lab Digital Signal & Image Proc Guangdong Pro, Shantou 515063, Peoples R China
3.Natl Author Remote Sensing & Space Sci NARSS, Div Environm Studies & Land Use, Cairo 11769, Egypt
关键词： wheat rust; ensemble learning; CNN; snapshot ensembling; SGDR-S
期刊名称：SENSORS （ 影响因子：3.847； 五年影响因子：4.05 ）
ISSN：
年卷期： 2022 年 22 卷 16 期
页码：
收录情况： SCI
摘要： Rust is a common disease in wheat that significantly impacts its growth and yield. Stem rust and leaf rust of wheat are difficult to distinguish, and manual detection is time-consuming. With the aim of improving this situation, this study proposes a method for identifying wheat rust based on ensemble learning (WR-EL). The WR-EL method extracts and integrates multiple convolutional neural network (CNN) models, namely VGG, ResNet 101, ResNet 152, DenseNet 169, and DenseNet 201, based on bagging, snapshot ensembling, and the stochastic gradient descent with warm restarts (SGDR) algorithm. The identification results of the WR-EL method were compared to those of five individual CNN models. The results show that the identification accuracy increases by 32%, 19%, 15%, 11%, and 8%. Additionally, we proposed the SGDR-S algorithm, which improved the f1 scores of healthy wheat, stem rust wheat and leaf rust wheat by 2%, 3% and 2% compared to the SGDR algorithm, respectively. This method can more accurately identify wheat rust disease and can be implemented as a timely prevention and control measure, which can not only prevent economic losses caused by the disease, but also improve the yield and quality of wheat.
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