In this paper, the model for predicting yields of chinese cabbages of each cultivar (joined-up in 2015 and wrapped-up in 2016) was developed after the reflectance of hyperspectral imagery was merged as 10 nm, 25 nm and 50 nm of FWHM (full width at half maximum). Band rationing was employed to minimize the unstable reflectance of multi-temporal hyperspectral imagery. The stepwise analysis was employed to select key band ratios to predict yields in all cultivars. The key band ratios selected for each of FWHM were used to develop the yield prediction models of chinese cabbage for all cultivars (joined-up & wrapped-up) and each cultivar (joined-up, wrapped-up). Effective accumulated temperature (EAT) was added in the models to evaluate its improvement of performances. In all models, the performance of models was improved with adding of EAT. The models with EAT for each of FWHM showed the predictability of yields in all cultivars as R2≥0.80, RMSE≤694 g/plant and RE≤28.3%. Such as this result, if the yield can be predicted regardless of the cultivar, it is considered to be advantageous when predicting the yield over a wide area because it is not require a cultivar classification work as pre-processing in imagery.

Abstract
Introduction
Materials and Methods
    1. Study area
    2. Hyperspectral image acquisition and processing
    3. Stepwise Multiple Linear Regression
    4. Effective Accumulated Temperature
    5. Modeling performance analysis
Results and Discussion
    1. Effective accumulated temperature and yields ofchinese cabbage
    2. Reflectance and ratio curves
    3. Prediction model without effective accumulatedtemperature
    4. Prediction model with effective accumulatedtemperature
References

• Ye-Seong Kang(Department of Bio-industrial Machinery Engineering, Gyeongsang National University (Institute of Agriculture and Life Science))
• Sae-Rom Jun(Department of Bio-industrial Machinery Engineering, Gyeongsang National University (Institute of Agriculture and Life Science))
• Si-Hyeong Jang(Department of Bio-industrial Machinery Engineering, Gyeongsang National University (Institute of Agriculture and Life Science))
• Jun-Woo Park(Department of Bio-industrial Machinery Engineering, Gyeongsang National University (Institute of Agriculture and Life Science))
• Hye-Young Song(Department of Bio-industrial Machinery Engineering, Gyeongsang National University (Institute of Agriculture and Life Science))
• Chan-Seok Ryu(Department of Bio-industrial Machinery Engineering, Gyeongsang National University (Institute of Agriculture and Life Science)) Corresponding author