TY - JOUR
T1 - Diagnosis of Plant Cold Damage Based on Hyperspectral Imaging and Convolutional Neural Network
AU - Yang, Wei
AU - Yang, Ce
AU - Hao, Ziyuan
AU - Xie, Chuanqi
AU - Li, Minzan
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - Cold damage is one of the disasters that cause significant loss and irreversible damage in crop production. To avoid yield loss, high-throughput phenotyping can be used to select the crop varieties with cold stress resistance. Nowadays, non-destructive spectral image analysis has become an effective way and is widely used in high-throughput phenotyping, which reflects the structural, physiological, biochemical characteristics and traits of plant structure and composition, plant growth and development processes and outcomes. This study used convolutional neural network (CNN) model to extract spectral features in the visible-near-infrared range to estimate cold damage of corn seedlings. The hyperspectral images of cold treated corn seedlings from five varieties were used as research objects in this study. The spectral range of the images was 450-885nm. Gaussian low-pass filter and the Savitzky-Golay smoothing method combined with the first-order derivative was used to do pre-processing for spectral data. For each corn variety, 3600 pixel samples obtained from the selected region of interests in each variety of corn seedlings were used for the CNN modeling. After the CNN modeling, 400 pixel samples extracted from the hyperspectral images were used as the testing set for each variety. Finally, a 10-layer knot CNN model was determined by analyzing the classification accuracy and computational efficiency. CNN detected the cold damage level of different types of corn seedlings as W22 (41.8 %), BxM (35%), B73 (25.6%), PH207 (20%), Mo17 (14%), which had high correlation with the ranking given by chemical method. The coefficient of correlation between cold damage detection results of CNN and results from chemical method is 0.8219. Therefore, it proves that spectral analysis based on CNN modeling can provide reference for detecting cold damage in corn seedlings.
AB - Cold damage is one of the disasters that cause significant loss and irreversible damage in crop production. To avoid yield loss, high-throughput phenotyping can be used to select the crop varieties with cold stress resistance. Nowadays, non-destructive spectral image analysis has become an effective way and is widely used in high-throughput phenotyping, which reflects the structural, physiological, biochemical characteristics and traits of plant structure and composition, plant growth and development processes and outcomes. This study used convolutional neural network (CNN) model to extract spectral features in the visible-near-infrared range to estimate cold damage of corn seedlings. The hyperspectral images of cold treated corn seedlings from five varieties were used as research objects in this study. The spectral range of the images was 450-885nm. Gaussian low-pass filter and the Savitzky-Golay smoothing method combined with the first-order derivative was used to do pre-processing for spectral data. For each corn variety, 3600 pixel samples obtained from the selected region of interests in each variety of corn seedlings were used for the CNN modeling. After the CNN modeling, 400 pixel samples extracted from the hyperspectral images were used as the testing set for each variety. Finally, a 10-layer knot CNN model was determined by analyzing the classification accuracy and computational efficiency. CNN detected the cold damage level of different types of corn seedlings as W22 (41.8 %), BxM (35%), B73 (25.6%), PH207 (20%), Mo17 (14%), which had high correlation with the ranking given by chemical method. The coefficient of correlation between cold damage detection results of CNN and results from chemical method is 0.8219. Therefore, it proves that spectral analysis based on CNN modeling can provide reference for detecting cold damage in corn seedlings.
KW - CNN
KW - cold damage
KW - corn
KW - deep learning
KW - high-throughput phenotyping
KW - hyperspectral imaging
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U2 - 10.1109/ACCESS.2019.2936892
DO - 10.1109/ACCESS.2019.2936892
M3 - Article
AN - SCOPUS:85076707832
SN - 2169-3536
VL - 7
SP - 118239
EP - 118248
JO - IEEE Access
JF - IEEE Access
M1 - 8809748
ER -