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| 基于高光谱技术的不同病原引起的马铃薯早疫病识别 |
| Identification of potato early blight caused by different pathogens based on hyperspectral technology |
| 投稿时间：2024-04-17 |
| DOI：10.3969/j.issn.1005-5630.202404170072 |
| 中文关键词: 马铃薯早疫病 光谱预处理 特征提取 支持向量机 |
| 英文关键词:potato early blight spectral preprocessing feature extraction support vector machine |
| 基金项目:国家自然科学基金（62065019）；云南省廖桂生专家工作站（202305AF150012)；自然科学基金（YJSJJ22-A18）；云南省基础研究重点项目(202301AS070010) |
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| 中文摘要: |
| 马铃薯早疫病感染性强、破坏性大。为区分不同病原造成的马铃薯早疫病类型，选用希森6号及青薯9号两个马铃薯品种的叶片接种Alternaria solani和Alternaria alternata病原菌。采用高光谱分选仪定时监测马铃薯患病叶片的发病情况及特征；采用多元散射校正、标准正态变量变换、多项式平滑及一阶导数光谱预处理方法结合主成分分析、竞争性自适应重加权采样以及连续投影算法特征提取方法对高光谱数据进行处理。建立支持向量机以及粒子群优化算法和遗传算法优化的支持向量机模型对样本进行分类识别。构建的模型中，SG-CARS-SVM模型识别效果最佳，测试样本识别准确率为99.72%，能够在保证识别速度的情况下兼顾准确率。结果表明，高光谱成像技术对不同病原引起的马铃薯早疫病识别可行性高。 |
| 英文摘要: |
| Potato early blight is highly infectious and destructive. To differentiate the types of potato early blight caused by different pathogens, two potato varieties, Xisen 6 and Qingshu 9, were inoculated with the Alternaria solani and Alternaria alternata pathogens. A hyperspectral sorter was used to monitor the disease progression and characteristics on the infected potato leaves. The hyperspectral data was processed using techniques such as multiple scattering correction, standard normal variable transformation, Savitzky-Golay, and first derivative, combined with methods like principal component analysis, competitive adaptive reweighted sampling, and successive projections algorithm for feature extraction. Support vector machine models were established, as well as SVM models optimized using particle swarm optimization algorithm and genetic algorithm for early blight classification and identification. Among the constructed models, the SG-CARS-SVM model showed the best identification performance, with a test sample identification accuracy of 99.72%, balancing accuracy and speed of identification. The results indicate that hyperspectral imaging technology has high feasibility for distinguishing potato early blight caused by different pathogens. |
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