Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
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Yield Prediction of Chinese Cabbage (Brassicaceae) Using Broadband Multispectral Imagery Mounted Unmanned Aerial System in the Air and Narrowband Hyperspectral Imagery on the Ground

  • Kang, Ye Seong (Division of Bio-System Engineering, Gyeongsang National University) ;
  • Ryu, Chan Seok (Division of Bio-System Engineering, Gyeongsang National University) ;
  • Kim, Seong Heon (Division of Bio-System Engineering, Gyeongsang National University) ;
  • Jun, Sae Rom (Division of Bio-System Engineering, Gyeongsang National University) ;
  • Jang, Si Hyeong (Division of Bio-System Engineering, Gyeongsang National University) ;
  • Park, Jun Woo (Division of Bio-System Engineering, Gyeongsang National University) ;
  • Sarkar, Tapash Kumar (Division of Bio-System Engineering, Gyeongsang National University) ;
  • Song, Hye young (Division of Bio-System Engineering, Gyeongsang National University)
  • Received : 2018.03.03
  • Accepted : 2018.05.25
  • Published : 2018.06.01
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Abstract

Purpose: A narrowband hyperspectral imaging sensor of high-dimensional spectral bands is advantageous for identifying the reflectance by selecting the significant spectral bands for predicting crop yield over the broadband multispectral imaging sensor for each wavelength range of the crop canopy. The images acquired by each imaging sensor were used to develop the models for predicting the Chinese cabbage yield. Methods: The models for predicting the Chinese cabbage (Brassica campestris L.) yield, with multispectral images based on unmanned aerial vehicle (UAV), were developed by simple linear regression (SLR) using vegetation indices, and forward stepwise multiple linear regression (MLR) using four spectral bands. The model with hyperspectral images based on the ground were developed using forward stepwise MLR from the significant spectral bands selected by dimension reduction methods based on a partial least squares regression (PLSR) model of high precision and accuracy. Results: The SLR model by the multispectral image cannot predict the yield well because of its low sensitivity in high fresh weight. Despite improved sensitivity in high fresh weight of the MLR model, its precision and accuracy was unsuitable for predicting the yield as its $R^2$ is 0.697, root-mean-square error (RMSE) is 1170 g/plant, relative error (RE) is 67.1%. When selecting the significant spectral bands for predicting the yield using hyperspectral images, the MLR model using four spectral bands show high precision and accuracy, with 0.891 for $R^2$, 616 g/plant for the RMSE, and 35.3% for the RE. Conclusions: Little difference was observed in the precision and accuracy of the PLSR model of 0.896 for $R^2$, 576.7 g/plant for the RMSE, and 33.1% for the RE, compared with the MLR model. If the multispectral imaging sensor composed of the significant spectral bands is produced, the crop yield of a wide area can be predicted using a UAV.

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References

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