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Cotton Blight Identification with Ground Framed Canopy Photo-Assisted Multispectral UAV Images

文献类型： 外文期刊

作者： Wang, Changwei 1 ; Chen, Yongchong 2 ; Xiao, Zhipei 2 ; Zeng, Xianming 1 ; Tang, Shihao 1 ; Lin, Fei 3 ; Zhang, Luxiang 4 ; Meng, Xuelian 5 ; Liu, Shaoqun 2 ;

作者机构： 1.South China Agr Univ, Coll Nat Resources & Environm, Guangzhou 510642, Peoples R China

2.South China Agr Univ, Coll Hort, Guangzhou 510642, Peoples R China

3.South China Agr Univ, Coll Plant Protect, Guangzhou 510642, Peoples R China

4.Guangdong Acad Agr Sci, Inst Agr Econ & Informat, Guangzhou 510640, Peoples R China

5.Louisiana State Univ, Dept Geog & Anthropol, Baton Rouge, LA 70803 USA

关键词： cotton blight; ground framed plot; inversion model; multispectral UAV images; vegetation index

期刊名称：AGRONOMY-BASEL （ 影响因子：3.7； 五年影响因子：4.0 ）

ISSN：

年卷期： 2023 年 13 卷 5 期

页码：

收录情况： SCI

摘要： Cotton plays an essential role in global human life and economic development. However, diseases such as leaf blight pose a serious threat to cotton production. This study aims to advance the existing approach by identifying cotton blight infection and classifying its severity at a higher accuracy. We selected a cotton field in Shihezi, Xinjiang in China to acquire multispectral images with an unmanned airborne vehicle (UAV); then, fifty-three 50 cm by 50 cm ground framed plots were set with defined coordinates, and a photo of its cotton canopy was taken of each and converted to the L*a*b* color space as either a training or a validation sample; finally, these two kinds of images were processed and combined to establish a cotton blight infection inversion model. Results show that the Red, Rededge, and NIR bands of multispectral UAV images were found to be most sensitive to changes in cotton leaf color caused by blight infection; NDVI and GNDVI were verified to be able to infer cotton blight infection information from the UAV images, of which the model calibration accuracy was 84%. Then, the cotton blight infection status was spatially identified with four severity levels. Finally, a cotton blight inversion model was constructed and validated with ground framed photos to be able to explain about 86% of the total variance. Evidently, multispectral UAV images coupled with ground framed cotton canopy photos can improve cotton blight infection identification accuracy and severity classification, and therefore provide a more reliable approach to effectively monitoring such cotton disease damage.

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