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

Abstract

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