Near-infrared spectroscopy and X-ray fluorescence data fusion for olive leaf analysis and crop nutritional status...

Leaf analysis is a useful way of diagnosing the nutritional status of the plants and therefore fast methods of analysis are demanded to aid in fertilization management decisions. In this work, a strategy based on the combined use of near-infrared spectroscopy (NIR) and portable energy dispersive X-Ray Fluorescence (EDXRF) is proposed as a suitable cheap and rapid alternative to traditional wet analytical methodologies. The approach has the major benefit of minimal sample preparation since leaves need to be only dried and ground. The ability of both techniques individually and applying two strategies of data fusion for the prediction of the most important plant nutrients, namely N, P, K, Ca, Mg, Mn, Zn, and B was tested. Predictive models were constructed using Partial Least Squares (PLS) to correlate the spectra with the nutrient contents. Models of unequal prediction performance in terms of the ratio of predictive deviation (RPD) were obtained for the different parameters when considering both techniques separately. Low-level data fusion, which consists of a concatenation of the raw data from both techniques, showed little improvement and even decreased the predictive ability for some elements. Better results were obtained with mid-level data fusion, that is, merging data after a feature extraction step performed by means of Principal components analysis (PCA). The results show that a fair quantitative prediction is possible for Ca, K and Mn with RPDs ≥ 2 for external validation, whereas models for N and P allowed a semiquantitative estimation. Mg and B models were less satisfactory and can be used only for distinguish between low and high levels, while Zn content cannot be predicted. Finally, the potential of the fusion of FT-NIR and EDXRF spectroscopic data for the fast screening of olive crop nutritional status has been tested. Deficiencies in important elements like N and K has been successfully detected.