Rapid soil fertility prediction using X-ray fluorescence data and machine learning algorithms

• Published in: Catena (Giessen) Vol. 197; p. 105003
• Main Authors: Benedet, Lucas, Acuña-Guzman, Salvador F., Faria, Wilson Missina, Silva, Sérgio Henrique Godinho, Mancini, Marcelo, Teixeira, Anita Fernanda dos Santos, Pierangeli, Luiza Maria Pereira, Acerbi Júnior, Fausto Weimar, Gomide, Lucas Rezende, Pádua Júnior, Alceu Linares, Souza, Igor Alexandre de, de Menezes, Michele Duarte, Marques, João José, Guilherme, Luiz Roberto Guimarães, Curi, Nilton
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-02-2021
• Subjects: Portable X-ray fluorescence spectrometry; Proximal sensor; Random Forest; Soil spatial variability; Tropical soils; STD; av. P; SMLR; av. K; GLM; hor; Radj2; XRF; Random Forest; IoT; nRMSE; Soil spatial variability; SDD; MLR; Varex; ex. Mg; Rh; Tropical soils; AIC; BSP; CEC; P-rem; R2; OOB; pXRF; ex. Ca; RMSE; IncMSE; MAE; Proximal sensor; GAM; RF; MSEOOB; ex. Al; Portable X-ray fluorescence spectrometry
• ISSN: 0341-8162; 1872-6887
• DOI: 10.1016/j.catena.2020.105003

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•1975 samples of varied soils were analyzed for fertility and elemental contents.•pXRF data was successfully used to rapidly predict soil fertility properties.•Random Forest outperformed other machine learning algorithms.•Prediction models allowed spatial distribution of soil fertility properties.•Inexpensive and environment-friendly soil fertility prediction was achieved by pXRF.
Conventional soil fertility analyses are laborious, costly, time-consuming, and produce hazardous waste. The high demand of these laboratory-based analyses prompted us to investigate an environment-friendly, rapid, and inexpensive methodology for soil fertility assessment. Portable X-ray fluorescence (pXRF) spectrometry allows the determination of total elemental concentration in soils quickly, simply and without hazardous waste production. However, incipient usage of this technology for the prediction of soil fertility properties has been reported for tropical conditions. Soil samples were collected from seven Brazilian states (n = 1975) aiming to use pXRF data to predict contents of available or exchangeable Ca2+, Mg2+, Al3+, K+ and P by testing different algorithms using 70% of the samples for model training, and the remaining 30% for model validation. In addition to point data predictions, the best performing models were applied to data obtained from a farm within the studied regions with a known cropping history to create soil fertility maps and illustrate another applicability of this approach. The attested use of pXRF data and machine learning algorithms stepwise Generalized Linear Model (GLM) and Random Forest (RF) to predict the contents of relevant soil fertility properties exhibited great potential. Validation of the models corroborated that RF resulted in more accurate predictions than GLM. Validation R2 values ranged from 0.59 to 0.82. Maps created were coherent with expected distributions of soil fertility attributes. This environment-friendly methodology may be used for the assessment of soil fertility properties in a wide range of tropical and subtropical soils with minimum waste generation and reduced costs.