Detection of grey mold disease on tomato leaves at different infected stages using hyperspectral imaging

Chuanqi Xie, Ce Yang, Yong He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This study used hyperspectral imaging technique to detect grey mold disease on tomato leaves. Hyperspectral images of diseased samples at 24h, 48h, 72h, 96h and 120h after inoculation and healthy samples were taken in the wave range of 380-1023 nm. A total of ten pixels from each sample were identified as the region of interest (ROI), and the average reflectance values of ROI were calculated. The dependent variables of healthy samples were set as 0, and diseased samples were set as 1, 2, 3, 4 and 5 according to infection stages, respectively. K-nearest neighbor (KNN) model was built to classify the samples using the full wave band set. To reduce data volume, competitive adaptive reweighted sampling (CARS) was used to select sensitive bands. Then, the KNN model was built based on just the selected bands. This later procedure of reducing spectral dimensionality and classifying infection stages was defined as CARS-KNN. Performances of KNN classifier on all wave bands and CARS-KNN were compared. The overall classification results of the testing sets using all wave bands and KNN classifier were 59.72% and 65.28% for CARS-KNN. When differentiating infected samples from control ones, the results were 94.44% for KNN and 95.83% for CARS-KNN. In addition, early disease detection (1dpi) obtained the results of 66.67% in KNN and 75.00% in CARS-KNN. Therefore, it demonstrated that hyperspectral imaging has the potential to be used for early detection of grey mold disease on tomato leaves.

Original languageEnglish (US)
Title of host publication2016 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2016
PublisherAmerican Society of Agricultural and Biological Engineers
ISBN (Electronic)9781510828759
DOIs
StatePublished - Jan 1 2016
Event2016 ASABE Annual International Meeting - Orlando, United States
Duration: Jul 17 2016Jul 20 2016

Publication series

Name2016 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2016

Other

Other2016 ASABE Annual International Meeting
CountryUnited States
CityOrlando
Period7/17/167/20/16

Keywords

  • Classification
  • Competitive adaptive reweighted sampling (CARS)
  • K-nearest neighbor (KNN)
  • Principal component analysis (PCA)
  • Reflectance

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    Xie, C., Yang, C., & He, Y. (2016). Detection of grey mold disease on tomato leaves at different infected stages using hyperspectral imaging. In 2016 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2016 (2016 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2016). American Society of Agricultural and Biological Engineers. https://doi.org/10.13031/aim.20162462686