文献类型： 外文期刊

作者： Gao Rong-Hao ¹ ; Feng Lu ¹ ; Zhang Yue ³ ; Yuan Ji-dong ³ ; Wu Hua-rui ¹ ; Gu Jing-qiu ¹ ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China

关键词： Early disease detection; Hyperspectral imaging; Tomato gray mold; Random forest; Multi-dimensional times series

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.609； 五年影响因子：0.516 ）

ISSN： 1000-0593

年卷期： 2022 年 42 卷 10 期

页码：

收录情况： SCI

摘要： Automatic early detection of plant diseases is essential for precision crop protection. This paper proposes an early diagnosis and detection method for tomato gray mold based on multi-dimensional spectral series (MDSS) and weighted random forest (WRF) algorithm. The aim was to establish a crop disease detection model by utilizing the overall trend of the spectral curve among multiple observation dimensions of the target leaves to realize the diagnosis before the leaf spot is visible. Generally, the third day after healthy leaves were inoculated with the Botrytis cinerea was treated as the first day that was successfully infected. Therefore, hyperspectral images were recorded from both healthy and infected leaves for, days after infection respectively. Then extracted, the region of interest and calculated the average spectrum to form the original spectral samples, whilst (156 x 7) groups were obtained in total after selection. The group samples were split into multi-dimensional spectral series with 1-7 dimensions per the course of the disease to make up multi-dimensional original spectral series. In order to increase the difference between dimensions, the adjacent original spectral series were subtracted to generate multi-dimensional related spectral series. Afterwards, two symbolic methods, symbolic aggregate approximation (SAX) and symbolic Fourier approximation (SFA) , were employed to discretize each spectral series into local discriminant features. Finally, a weighted random forest classification model (MDSS-SAX-SFA-WRF) based on the local discriminant features of multi-dimensional spectral series is established to realize early disease detection. Accordingly, the model based on single-dimensional spectral series (SDSS) is also built as the benchmark to compare with the MDSS-SAX-SFA-WRF model. The experiment results indicate that the MDSS-SAX-SFA-WRF detection model achieves detection accuracies of more than 90% in 56 testing samples containing 2 to 7 spectral series dimensions, and the highest accuracy up to 99% reached in the 5-dimensional sample data, which is 8. 2 percentage higher than that of SDSS-SAX-SFA-MRF detection model on the 5th day of infection. Different from the SDSS-SAX-SFA-MRF model detection performance dropped significantly to the lowest 84% in the 5th-7th days of infection due to random interference. While the discrimination accuracy of the MDSS-SAX-SFA-WRF model still retained a high level of more than 98% in the visible stage of infection without excessive decline. Therefore, the classification model based on the overall change trend of the multi-dimensional spectral curve and weighted random forest (MDSS-SAX-SFA-WRF) proposed in this paper can effectively realize the early detection of tomato gray mold with the strong robustness, which provides a new idea for the early differentiation of crop disease.