FOOD SCIENCE ›› 2018, Vol. 39 ›› Issue (19): 233-240.doi: 10.7506/spkx1002-6630-201819036

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Diagnosis of Symptom Appearance of Potato Dry Rot Disease Using Time Series Hyperspectral Imaging Based on Dynamic Time Warping

JIN Xiu, QI Haijun, LI Shaowen*   

  1. School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
  • Online:2018-10-15 Published:2018-10-24

Abstract: In the present study, we aimed to develop a method to diagnose the incubation period and symptom appearance of potato dry rot using time series hyperspectral imaging based on dynamic time warping (DTW). The symptom features during the development of dry rot were analyzed and extracted. The time series key point, namely the start point of symptom appearance was analyzed by DTW clustering algorithm based on the time series characteristics. The threshold segmentation algorithm was used to extract the region of interest (ROI) during the data preprocessing, and the probability density algorithm was applied to eliminate the abnormal spectral data. By comparison of the spectra and appearance of potatoes during the development of dry rot, non-monotonic characteristics were observed in the spectra. Further, the spectral characteristics were extracted by kernel principal component analysis (KPCA). Finally, the time series key point was predicted by using fuzzy clustering model (FCM) based on the symptoms features with an accuracy of only 66.7%. By contrast, the prediction accuracy of DTW on the basis of time series features was as high as 94.4%. This study confirmed that the time series hyperspectral imaging based on DTW could effectively diagnose the symptom appearance of potato dry rot.

Key words: potato dry rot disease, time series hyperspectral, time series key point, fuzzy clustering, dynamic time warping

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