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

Abstract

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 of RMSE = 172 g/m2, RE = 30.3% in 2017 using the 2018 model and RMSE = 215 g/m2 and RE = 33.2% in 2018 using the 2017 model. Consequently, the key band ratios for predicting the onion yields were identified as the seven band ratios with EAT at 420/430, 440/450, 500/510, 590/600, 620/630, 670/680, and 730/760. Ultimately, these findings will provide compact multispectral image sensors specialized in onion-yield prediction that can monitor wide agriculture fields mounted on various platforms.