Yield prediction and validation of onion (Allium cepa L.) using key variables in narrowband hyperspectral imagery and effective accumulated temperature

•We propose the commercial band pass filters to predict yield of onion.•PLS_VIP and stepwise analysis based on hyperspectral data select the valid band ratios.•Spectral filter (FWHM) is selected by evaluating the prediction models using the valid band ratios.•Onion yield is predicted by 7 band ratios for FWHM 10 nm with EAT.•EAT improves the reproducibility when the models are applied to different years. In this study, hyperspectral imagery was used to develop models for predicting onion yields in two years (2017 and 2018). First, the bands for the full width half maximum (FWHM) measurement of 5 nm in the canopied areas were merged as FWHM 10, 25, and 50 nm. This was based on a commercialized band-pass filter that considered the development of the compact multispectral image sensors. Then, band rationing was performed to correct the unstable reflectance through incomplete radiating normalization. Stepwise and variable importance in projection in partial least squares (PLS_VIP) approaches were applied to select the optimal FWHM by evaluating both models’ performance and the number of overlapped band ratios. The optimal FWHM measurement was 10 nm, with both high model performances and the highest number of overlapped band ratios. The overlapped ratios of 440/450, and 730/760 in variable importance in projection (VIP) 1 were fixed in the onion-yield prediction model. Conversely, the non-fixed, non-overlapped ratios of 420/430, 490/500, 500/510, 590/600, 620/630, 660/670, 670/680, 710/720, 810/820, and 870/880 were reduced one by one; this was dependent on their removal ranking in descending order of the mean ratio of reduction (MROR) based on the RMSE value, using the leave-one-out method. These combinations in both the fixed and non-fixed band ratios were used to develop prediction models with and without effective accumulated temperature (EAT) values. In all combinations, the models’ performance developed with EAT were increased by preventing slight or sharp decrease in performance, compared to those without EAT. The models, in each year, developed by seven band ratios (420/430, 440/450, 500/510, 590/600, 620/630, 670/680, and 730/760) among the combinations were maintained. The prediction models with EAT were cross-validated (by predicting the 2017 yields using the 2018 model and the 2018 yields using the 2017 model) to evaluate the reproducibility in other years. The reproducibility of the model developed by the seven band ratios was optimal with errors o