Blueberries offer multiple health benefits, and their cultivation has expanded to warm tropical regions. However, references for foliar nutritional content are lacking in the literature. Proximal sensing may enhance nutritional characterization to optimize blueberry production. We aimed (i) to characterize the nutrient contents of healthy plants of three blueberry varieties adapted to warm climates (Emerald, Jewel, and Biloxi) using a reference method for foliar analysis (inductively coupled plasma (ICP)) and a portable X-ray fluorescence (pXRF) spectrometer on fresh and dry leaves and (ii) to differentiate blueberry varieties based on their nutrient composition. Nutrient content was statistically compared per leaf moisture condition (fresh or dry) with ICP results and used to differentiate the varieties via the random forest algorithm. P and Zn contents (ICP) in leaves were different among varieties. Dry leaf results (pXRF) were strongly correlated with ICP results. Most nutrients determined using ICP presented good correlation with pXRF data (R² from 0.66 to 0.93). The three varieties were accurately differentiated by pXRF results (accuracy: 87%; kappa: 0.80). Predictions of nutrient contents based on dry leaves analyzed by pXRF outperformed those based on fresh leaves. This approach can also be applied to other crops to facilitate nutrient assessment in leaves.

Citriculture has worldwide importance, and monitoring the nutritional status of plants through leaf analysis is essential. Recently, proximal sensing has supported this process, although there is a lack of studies conducted specifically for citrus. The objective of this study was to evaluate the application of portable X-ray fluorescence spectrometry (pXRF) combined with machine learning algorithms to predict the nutrient content (B, Ca, Cu, Fe, K, Mg, Mn, P, S, and Zn) of citrus leaves, using inductively coupled plasma optical emission spectrometry (ICP-OES) results as a reference. Additionally, the study aimed to differentiate 15 citrus scion/rootstock combinations via pXRF results and investigate the effect of the sample condition (fresh or dried leaves) on the accuracy of pXRF predictions. The samples were analyzed with pXRF both fresh and after drying and grinding. Subsequently, the samples underwent acid digestion and analysis via ICP-OES. Predictions using dried leaves yielded better results (R² from 0.71 to 0.96) than those using fresh leaves (R² from 0.35 to 0.87) for all analyzed elements. Predictions of scion/rootstock combinations were also more accurate with dry leaves (Overall accuracy = 0.64, kappa index = 0.62). The pXRF accurately predicted nutrient contents in citrus leaves and differentiated leaves from 15 scion/rootstock combinations. This can significantly reduce costs and time in the nutritional assessment of citrus crops.