A comparison of partial least squares (PLS) and sparse PLS regressions for predicting yield of Swiss chard grown under different irrigation water sources using hyperspectral data

There is an increasing demand for fresh vegetables such as Swiss chard in cognisance of their nutritive value. Early prediction of Swiss chard yield provides a valuable knowledge base for product management decisions like pre-harvest planning, post-harvest handing, food policy, and marketing. Consequently, the objective of the present study was to investigate the use of hyperspectral data in predicting yield of Swiss chard grown under different irrigation water sources. Swiss chard ground-based hyperspectral data were collected at canopy level using a handheld spectroradiometer at 2 and 2.5 months after planting. Reflectance spectra were transformed to their first-order derivative and partial least squares (PLS) and sparse PLS (SPLS) regressions (R) were used for data analysis. Results showed that 95% and 97% of Swiss chard fresh and dry yields variation, respectively could be explained. SPLSR outperformed PLSR models for predicting Swiss chard fresh and dry yields. Results further showed that models developed using data collected when the crop was 2.5 months old were more accurate than models derived using a 2-month old crop data, except the SPLSR model for predicting dry yield. Fresh yield estimates could be accurately modelled (root mean square error: RMSE = 23.97% of the mean, Nash-Sutcliffe efficiency: NSE = 0.93). However, Swiss chard dry yield could not be reliably predicted (at minimum RMSE = 35.00% of the mean and a maximum NSE of 0.60 were obtained). This study demonstrates the potential of hyperspectral data in predicting Swiss chard fresh yield using combined irrigation treatments data sets. The study offers insight to the potential of large-scale prediction and estimation of Swiss chard yield using space borne and/or airborne hyperspectral data. (C) 2014 Elsevier B.V. All rights reserved.