一种利用真菌孢子信息定量鉴定黄锈病的新光谱指标

IF 4.2 3区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yu Ren, H. Ye, Wenjiang Huang, Huiqin Ma, Anting Guo, Chao Ruan, Linyi Liu, Binxiang Qian
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引用次数: 1

摘要

摘要小麦黄锈病(锈病)是冬小麦(Triticum aestivum L.)常见的真菌病害。在黄锈侵染期间,真菌孢子出现在叶片表面,呈平行于叶脉的黄色窄条纹。通过分析真菌孢子对病叶光谱的影响,找到对黄锈敏感的条带,建立了黄锈孢子指数(YRSI)。利用2年的叶片光谱数据对估计的精度和稳定性进行了评价,并与8种常用的黄锈检测指标进行了比较。结果表明,使用YRSI对2017年光谱数据的病死率估算排名第一(R2 = 0.710, RMSE = 0.097),优于已发表的2002年光谱数据验证指标(R2 = 0.587, RMSE = 0.120)。采用随机森林(RF)、k近邻(KNN)和支持向量机(SVM)算法,以黄锈病、健康和蚜虫小麦光谱数据为混合数据集,对YRSI和8个常用指标的识别能力进行了测试。YRSI提供了最好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new spectral index for the quantitative identification of yellow rust using fungal spore information
ABSTRACT Yellow rust (Puccinia striiformis f. sp. Tritici) is a frequently occurring fungal disease of winter wheat (Triticum aestivum L.). During yellow rust infestation, fungal spores appear on the surface of the leaves as yellow and narrow stripes parallel to the leaf veins. We analyzed the effect of the fungal spores on the spectra of the diseased leaves to find a band sensitive to yellow rust and established a new vegetation index called the yellow rust spore index (YRSI). The estimation accuracy and stability were evaluated using two years of leaf spectral data, and the results were compared with eight indices commonly used for yellow rust detection. The results showed that the use of the YRSI ranked first for estimating the disease ratio for the 2017 spectral data (R2 = 0.710, RMSE = 0.097) and outperformed the published indices (R2 = 0.587, RMSE = 0.120) for the validation using the 2002 spectral data. The random forest (RF), k-nearest neighbor (KNN), and support vector machine (SVM) algorithms were used to test the discrimination ability of the YRSI and the eight commonly used indices using a mixed dataset of yellow-rust-infested, healthy, and aphid–infested wheat spectral data. The YRSI provided the best performance.
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来源期刊
Big Earth Data
Big Earth Data Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
7.40
自引率
10.00%
发文量
60
审稿时长
10 weeks
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