Evaluation of Spectroscopy and Methodological Pre-Treatments to Estimate Soil Nutrients in the Vineyard

Abstract

The characterization of vineyard soil is a key issue for crop management, which directly affects the quality and yield of grapes. However, traditional laboratory analysis of soil properties is tedious and both time and cost consuming, which is not suitable for precision viticulture. For this reason, a fast and convenient soil characterization technique is needed for soil quality assessment and precision soil management. Here, spectroscopy appears as a suitable alternative to assist laboratory analysis. This work focuses on estimating soil properties by spectroscopy. Our study was carried out using 96 soil samples collected from three vineyards in Rias Baixas Designation of Origen (Galicia, Spain). The soils that were characterized include nitrogen (N), organic matter (OM) and clay content (Clay). The presented work compared two regression techniques (partial least squares (PLSR) and random forest (RF)) and four spectral ranges: visible—VIS (350–700 nm), near infrared—NIR (701–1000 nm), short wave infrared—SWIR (1001–2500 nm) and VIS-NIR-SWIR (350–2500 nm) in order to identify the more suitable prediction models. Moreover, the effect of pre-treatments in reflectance data (smoothing Svitzky–Golay, SG, baseline normalization, BN, first derivative, FD, standard normal variate, SNV, logarithm of 1/reflectance or spectroscopy (SP) and detrending, SNV-D) was evaluated. Finally, continuous maps of the soil properties were created based on estimated values of regression models. Our results identified PLSR as the best regression technique, with less computation time than RF. The data improved after applying transformation in reflectance data, with the best results from spectroscopy pre-treatment (logarithm of 1/Reflectance). PLSR performances have obtained determination coefficients (R2) of 0.69, 0.73 and 0.52 for nitrogen, organic matter, and clay, respectively, with acceptable accuracy (RMSE: 0.03, 1.06 and 2.90 %) in a short time. Furthermore, the mapping of soil vineyards generates information of high interest for the precision viticulture management, as well as a comparison between the methodologies used.

Dataset variability and carbonate concentration influence the performance of local visible-near infrared spectral models

“…2) illustrates this finding by the different grouping of individual field datasets due to different pre-processing. This leads to the conclusion that studies that did not optimize the pre-processing scheme for every soil property separately did eventually not make full use of the spectroscopy potential (see also examples in Table 3), which has been shown by other studies as well (Alomar et al, 2021;Rodriguez-Febereiro et al, 2022;Singh et al, 2022). Nevertheless, the property-specific optimization of spectral pre-processing is a tedious process and constrains the fast and cost-effective application of vis-NIR spectroscopy, but some progress has been made simultaneously with our study by Mishra et al (2022).…”

Comparing Machine Learning Algorithms for Soil Salinity Mapping Using Topographic Factors and Sentinel-1/2 Data: A Case Study in the Yellow River Delta of China

“…The model has many advantages, such as being able to evaluate the relationship between multiple independent variables and multiple dependent variables, and a better model being obtained when there are multiple collinearities between independent variables [49]. At present, the PLSR model has been widely used in the remote sensing monitoring of soil components, such as soil salt content [50], soil nutrients [51], soil organic carbon [52] and soil moisture content [53]. The PLSR model can re-project the prediction matrix X and observation matrix Y into a new space to establish a new regression model, which can significantly reduce noise.…”