In situ determination of soybean leaves nutritional status by portable X-ray fluorescence: An initial approach for data collection and predictive modelling

Author(s)

da Silva, Thainara Rebelo ; de Almeida, Eduardo ; Tavares, Tiago Rodrigues ; Melquiades, Fabio Luiz ; Baesso, Murilo Mesquita ; de Camargo, Rachel Ferraz ; Gomes, Marcos Henrique Feresin ; de Carvalho, Hudson Wallace Pereira

• Document type: Journal article
• Source: BIOSYSTEMS ENGINEERING; v. 247, p. 10-pg., 2024-09-16.

Abstract

X-ray fluorescence <bold>(</bold>XRF) analyses are fast, clean, non-destructive, and compatible with on-field operations, which are some advantages over traditional determinations using coupled plasma optical emission spectroscopy (ICP-OES). The aim of this study was to advance in situ XRF approaches for assessing the nutritional status of soybean leaves (i.e., P, S, K, Ca, Mn, Fe, Cu and Zn). More specifically, we propose a protocol to ensure accuracy of in-field analysis and then evaluate the predictive performance of XRF via different data modelling strategies for macro- and micronutrient determination. Therefore, the XRF sensor dwell time of 60 s and the maximum time of 5 min were determined for the analysis of the leaves after leaf abscission, taking into account the influence of moisture loss on the signal intensity of the lighter elements. Regarding the predictive performance of XRF data for nutrients determination, multiple linear regression (MLR) models resulted in lower root mean square errors (RMSE) for P (433 mg kg(-1)), S (204 mg kg(-1)) and K (1957 mg kg(-1)); Partial least squares regression (PLS) for Ca (519 mg kg(-1)); and simple linear regression (SLR) for Mn (9 mg kg(-1)), Fe (18 mg kg(-1)), Zn (5 mg kg(-1)). The different modelling strategies exhibited equivalent RMSE for Cu (2 mg kg(-1)). These prediction errors are within a +/- 20% range, demonstrating that the in situ protocols developed in this research are useful for predicting the nutrients concentration in soybean leaves. Our study shows the possibility of using the in situ XRF sensor for the rapid and practical nutrients determination in soybean leaves, presenting good potential as a crop diagnosis tool. (AU)

• FAPESP's process: 20/16670-9 - Spectral data modelling for tropical soil fertility analysis: association of vis-NIR and XRF techniques for the modernization of the traditional methods of analysis
• Grantee: Tiago Rodrigues Tavares
• Support Opportunities: Scholarships in Brazil - Post-Doctoral