Non-Destructive Appraisal of Macro- and Micronutrients in Persimmon Leaves Using Vis/NIR Hyperspectral Imaging

Maylin Acosta, Isabel Rodríguez-Carretero, José Blasco, José Miguel de Paz and Ana Quiñones ()
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Maylin Acosta: Centro Para el Desarrollo de la Agricultura Sostenible, Instituto Valenciano de Investigaciones Agrarias (IVIA), CV-315, km 10.7, Moncada, 46113 Valencia, Spain
Isabel Rodríguez-Carretero: Centro Para el Desarrollo de la Agricultura Sostenible, Instituto Valenciano de Investigaciones Agrarias (IVIA), CV-315, km 10.7, Moncada, 46113 Valencia, Spain
José Blasco: Centro de Agroingeniería, Instituto Valenciano de Investigaciones Agrarias (IVIA), CV-315, km 10.7, Moncada, 46113 Valencia, Spain
José Miguel de Paz: Centro Para el Desarrollo de la Agricultura Sostenible, Instituto Valenciano de Investigaciones Agrarias (IVIA), CV-315, km 10.7, Moncada, 46113 Valencia, Spain
Ana Quiñones: Centro Para el Desarrollo de la Agricultura Sostenible, Instituto Valenciano de Investigaciones Agrarias (IVIA), CV-315, km 10.7, Moncada, 46113 Valencia, Spain

Agriculture, 2023, vol. 13, issue 4, 1-12

Abstract: Visible and near-infrared (Vis/NIR) hyperspectral imaging (HSI) was used for rapid and non-destructive determination of macro- and micronutrient contents in persimmon leaves. Hyperspectral images of 687 leaves were acquired in the 500–980 nm range over 6 months, covering a complete vegetative cycle. The average reflectance spectrum of each leaf was extracted, and foliar ionomic analysis was used as a reference method to determine the actual concentration of the nutrients in the leaves. Analyses were performed via emission spectrometry (ICP-OES) for macro- and micronutrients after microwave digestion and using the Kjeldahl method to quantify nitrogen. Partial least square regression (PLS-R) was used to predict the nutrient concentration based on spectral data from the leaf using actual values of each element as predictor variables. Several methods were used to pre-process the spectra, including Savitzky–Golay (SG) smoothing, standard normal variate (SNV) and first (1D) and second derivatives (2D). Seventy-five percent of the samples were used to calibrate and validate the model by cross-validation, whereas the remaining twenty-five % were used as an independent test set. The best performance of the models for the test set achieved an R 2 = 0.80 for nitrogen. Results were also satisfactory for phosphorous, calcium, magnesium and boron, with determination coefficient R 2 values of 0.63, 0.66, 0.58 and 0.69, respectively. For the other nutrients, lower prediction rates were attained (R 2 = 0.48 for potassium, R 2 = 0.38 for iron, R 2 = 0.24 for copper, R 2 = 0.23 for zinc and R 2 = 0.22 for manganese). The variable importance in projection (VIP) was used to extract the most influential bands for the best-predicted nutrients, which were N, K and B.

Keywords: hyperspectral imaging; Vis/NIR; spectroscopy; chemometrics; variable selection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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