Towards a non-destructive foliar nutrient estimation in 'Clementina de Nules' mandarin through hyperspectral imaging-based leaf age discrimination

Abstract Background Non-destructive foliar nutrient estimation is a critical component of sustainable fertilisation strategies in citrus orchards. Diagnosis relies on analysing spring flush leaves at a specific stage, but field sampling often includes mixed ages, causing inaccurate estimations and poor fertilisation decisions. This study explored visible/near-infrared hyperspectral imaging (500–980 nm) to discriminate leaf age and estimate nutrient concentrations in Clementina de Nules mandarin. Methods Hyperspectral images were analysed using partial least squares discriminant analysis (PLS-DA) to classify leaf age and partial least squares regression (PLS-R) to predict nutrient concentrations. Eleven nutrients were evaluated, and optimal wavelengths identified to reduce dimensionality and enable portable sensor development. Results The PLS-DA model achieved 99.4% accuracy in distinguishing young from mature leaves, addressing a critical challenge for reliable diagnostics. Among the eleven nutrients, PLS-R models moderately predicted phosphorus, potassium, calcium, iron, and copper, with test set R 2 values of 0.60–0.69 and prediction-to-deviation ratios (RPD) of 1.5–1.8, allowing differentiation of high and low nutrient levels. Accuracy was Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation maintained while reducing dimensionality by selecting ten optimal wavelengths per nutrient. Conclusion This study improves the reliability of non-destructive nutrient diagnosis in citrus by addressing leaf age variability and provides a starting point for future in-field, real-time monitoring systems. The approach supports optimised fertilisation strategies aligned with precision agriculture and European Green Deal sustainability goals, offering cost-effective, field-deployable solutions for citrus management.