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基于可见/近红外光谱技术的茄子叶片灰霉病早期检测研究

Keywords: 可见/近红外光谱,灰霉病,主成分分析,BP神经网络,红外光谱技术,子叶,灰霉病,早期,检测研究,GRAYMOLD,DETECTION,EARLY,CINEREA,BASED,LEAVES,EGGPLANT,快速,运用,正确率,识别率,检测效果,检测模型,显示,结果

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Abstract:

应用可见/近红外光谱技术对茄子叶片进行灰霉病害还未在叶片表面出现病症时的早期检测.采用化学计量学方法建立早期检测模型.主成分分析用于对光谱数据进行降维,得到若干个最重要的主成分.但直接从聚类图中无法进行是否染病的鉴别.因而将其作为变量输入BP神经网络,从而减少了计算量,提高了建模精度.检测结果显示,模型具有良好的检测效果,能够达到100%的识别率,正确率也能达到88%.说明运用可见/近红外光谱技术能够实现当病症还未在叶片表面出现时的快速准确的早期检测,为灰霉病早期检测提供了新的途径.

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