Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging
Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging
复制标题
利用高光谱成像检测番茄叶片上的早疫病和晚疫病
DOI：
10.1038/srep16564
复制
发表时间：
2015-11-17
影响因子：
4.6
通讯作者：
He, Yong
中科院分区：
文献类型：
--
作者：
Xie, Chuanqi;Shao, Yongni;He, Yong
This study investigated the potential of using hyperspectral imaging for detecting different diseases on tomato leaves. One hundred and twenty healthy, one hundred and twenty early blight and seventy late blight diseased leaves were selected to obtain hyperspectral images covering spectral wavelengths from 380 to 1023 nm. An extreme learning machine (ELM) classifier model was established based on full wavelengths. Successive projections algorithm (SPA) was used to identify the most important wavelengths. Based on the five selected wavelengths (442, 508, 573, 696 and 715 nm), an ELM model was re-established. Then, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) at the five effective wavelengths were extracted to establish detection models. Among the models which were established based on spectral information, all performed excellently with the overall classification accuracy ranging from 97.1% to 100% in testing sets. Among the eight texture features, dissimilarity, second moment and entropy carried most of the effective information with the classification accuracy of 71.8%, 70.9% and 69.9% in the ELM models. The results demonstrated that hyperspectral imaging has the potential as a non-invasive method to identify early blight and late blight diseases on tomato leaves.
