Determination of rice leaf blast disease level based on visible-near-infrared spectroscopy.
Determination of rice leaf blast disease level based on visible-near-infrared spectroscopy.
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DOI：
10.1631/jzus.c1000097
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发表时间：
2011-01-01
期刊：
影响因子：
--
通讯作者：
He, Y.
中科院分区：
文献类型：
--
作者：
Cheng ShuXi, Cheng ShuXi;Shao YongNi, Shao YongNi;He, Y.
提出了一种基于可见-近红外光谱技术的水稻叶瘟病害快速检测方法。采用偏最小二乘回归（PLSR）方法对不同发病程度水稻叶片光谱进行了化学计量学分析。建立了基于全波段光谱（模型1）、特征波段光谱（模型2）和特征波长光谱（模型3）的PLSR模型。模型1的疾病检测水平的确定正确率为96.7%。利用PLSR模型的回归系数，筛选出552-558、672-682、719-726、756-768和990-998 nm五个特征波段。模型2的测定正确率为90%。结果表明，所选的5个特征波段与病害检测水平有较好的相关性。基于特征波段进一步选择了5个特征波长。模型3的测定正确率为80%。可见-近红外光谱法检测结果良好，为水稻叶瘟病情的快速检测提供了一条新途径。
A rapid determination of rice leaf blast disease based on visible-near-infrared spectroscopy was proposed. Chemometric analysis was executed on the spectra of the rice leaves with different disease level by using partial least square regression (PLSR). Three PLSR models were established based on full-range spectra (model 1), spectra at feature wavebands (model 2) and spectra at feature wavelengths (model 3). The determination correct rate of the disease detection level was 96.7% for model 1. By-using the obtained regression coefficients of PLSR model, five feature wavebands were selected, which were at 552-558, 672-682, 719-726, 756-768 and 990-998 nm. The determination correct rate was 90% for model 2. The result showed that there was a good correlation between the disease detection level and the five selected feature wavebands. Five feature wavelengths were further selected based on the feature wavebands. The determination correct rate was 80% for model 3. It is concluded that the visible-near-infrared spectroscopy gives a good determination result and is a new way to fast determine rice leaf blast disease level.