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Hyperspectral Imaging and Machine Learning for Diagnosing Rice Bacterial Blight Symptoms Caused by Xanthomonas oryzae pv. oryzae, Pantoea ananatis and Enterobacter asburiae

文献类型： 外文期刊

作者： Zhang, Meng 1 ; Tang, Shuqi 1 ; Lin, Chenjie 1 ; Lin, Zichao 1 ; Zhang, Liping 2 ; Dong, Wei 2 ; Zhong, Nan 1 ;

作者机构： 1.South China Agr Univ, Coll Engn, Guangzhou 510642, Peoples R China

2.Anhui Acad Agr Sci, Agr Econ & Informat Res Inst, Hefei 230001, Peoples R China

3.Guangdong Prov Key Lab Agr Artificial Intelligence, Guangzhou 510642, Peoples R China

4.Natl Ctr Int Collaborat Res Precis Agr Aviat Pesti, Guangzhou 510642, Peoples R China

关键词： Pantoea ananatis; Enterobacter asburiae; Xanthomonas oryzae pv. oryzae; rice bacterial blight; hyperspectral imaging; convolutional neural networks; generative adversarial networks

期刊名称：PLANTS-BASEL （ 影响因子：4.1； 五年影响因子：4.5 ）

ISSN： 2223-7747

年卷期： 2025 年 14 卷 5 期

页码：

收录情况： SCI

摘要： In rice, infections caused by Pantoea ananatis or Enterobacter asburiae closely resemble the bacterial blight induced by Xanthomonas oryzae pv. oryzae, yet they differ in drug resistance and management strategies. This study explores the potential of combining hyperspectral imaging (HSI) with machine learning for the rapid and accurate detection of rice bacterial blight symptoms caused by various pathogens. One-dimensional convolutional neural networks (1DCNNs) were employed to construct a classification model, integrating various spectral preprocessing techniques and feature selection algorithms for comparison. To enhance model robustness and mitigate overfitting due to limited spectral samples, generative adversarial networks (GANs) were utilized to augment the dataset. The results indicated that the 1DCNN model, after feature selection using uninformative variable elimination (UVE), achieved an accuracy of 86.11% and an F1 score of 0.8625 on the five-class dataset. However, the dominance of Pantoea ananatis in mixed bacterial samples negatively impacted classification performance. After removing mixed-infection samples, the model attained an accuracy of 97.06% and an F1 score of 0.9703 on the four-class dataset, demonstrating high classification accuracy across different pathogen-induced infections. Key spectral bands were identified at 420-490 nm, 610-670 nm, 780-850 nm, and 910-940 nm, facilitating pathogen differentiation. This study presents a precise, non-destructive approach to plant disease detection, offering valuable insights into disease prevention and management in precision agriculture.

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