Rapid prediction of soil available sulphur using visible near-infrared reflectance spectroscopy

Abstract

Rapid and accurate prediction of soil available S, an important secondary nutrient, is crucial for its site-specific management in a cultivated region. Although traditional chemical analysis of any nutrient is an accurate method, but often costly, time-consuming and destructive in nature. Recently visible near-infrared (VIS-NIR) reflectance spectroscopic technique has gained its popularity for rapid, non-destructive and cost-effective assessment of soil nutrients. Hence, a study was carried out in an intensively cultivated region of Katol block of Nagpur, Maharashtra, during 2018-20 for rapid prediction of soil available S using spectroscopic technique. Both spectroscopic and chemical analyses were carried out using 132 georeferenced surface soil samples (0-15 cm depth). The descriptive statistical analysis showed that the available S content varied from 1.09 to 47.88 mg/kg. Multivariate models namely partial least square regression (PLSR) and random forest (RF) were applied to develop spectral models for S prediction from spectral dataset. Several statistical diagnostics like coefficient of determination (R2), root mean square error (RMSE), ratio of performance deviation (RPD) and ratio of performance to interquartile distance (RPIQ) were used to evaluate the performances of two models. The best prediction of S was achieved from nonlinear RF model (R2 = 0.71, RMSE = 8.86, RPD =1.18, RPIQ = 1.69) as compared to linear PLSR model (R2 = 0.53, RMSE = 9.04, RPD = 1.16, RPIQ = 1.66) datasets. Therefore, the result suggested applying non-linear multivariate model (RF) for obtaining best predictability for S from spectroscopic technique.

Use of machine learning for monitoring the growth stages of an agricultural crop

“…Further work from the same research group applied an RF model to predict the soil sulfur content (R 2 = 0.71, RMSE = 8.86) using a hand-held visible near-infrared (VIS-NIR) reectance spectroscopic technique. 23 In addition, the group have applied a hand-held VIS-NIR spectroscopic technique combined with a PLSR model to predict soil organic carbon (R 2 = 0.53, RMSE = 9.04). 24 The ability to maintain global crop security with a focus to end hunger and promote sustainable agriculture relies on an effective approach to crop management.…”