Predicting the growth of lettuce from soil infrared reflectance spectra: the potential for crop management - Breure, Timo S.; Milne, Alice E.; Webster, R.; Haefele, Stephan M.; Hannam, Jacqueline A.; Moreno-Rojas, S.; Corstanje, Ronald (Springer, 2020)
How well could one predict the growth of a leafy crop from refectance spectra from the soil and how might a grower manage the crop in the light of those predictions? Topsoil from two felds was sampled and analysed for various nutrients, particle-size distribution and organic carbon concentration. Crop measurements (lettuce diameter) were derived from aerial-imagery. Refectance spectra were obtained in the laboratory from the soil in the near- and mid-infrared ranges, and these were used to predict crop performance by partial least squares regression (PLSR). Individual soil properties were also predicted from the spectra by PLSR. These estimated soil properties were used to predict lettuce diameter with a linear model (LM) and a linear mixed model (LMM): considering diferences between lettuce varieties and the spatial correlation between data points. The PLSR predictions of the soil properties and lettuce diameter were close to observed values. Prediction of lettuce diameter from the estimated soil properties with the LMs gave somewhat poorer results than PLSR that used the soil spectra as predictor variables. Predictions from LMMs were more precise than those from the PLSR using soil spectra. All model predictions improved when the efects of variety were considered. Predictions from the refectance spectra, via the estimation of soil properties, can enable growers to decide what treatments to apply to grow lettuce and how to vary their treatments within their felds to maximize the net proft from the crop
Keywords: LiDAR; Partial least squares regression; Linear mixed model; Fen soil; Crop growth; IR spectroscopy