The morphological symptoms of phosphorus (P) deficiency in the leaves of mini-cucumber plants at early stages of development have features similar to that of early stage development in healthy plants. That similarity may lead to inappropriate visual diagnostics of phosphorus deficiency in analyzed samples. Because the differences in spectral properties of leaf tissues between phosphorus-deficient and healthy plants can be demonstrated, the feasibility of using near-infrared (NIR) spectroscopy for rapid and nondestructive diagnostics of phosphorus deficiency in mini-cucumber plants was investigated. Leaf reflection spectra in the wavelength range of 10 000-4000 cm⁻¹ were measured before the appearance of morphological changes caused by phosphorus deficiency. Least-squares support vector machine (LS-SVM), a method for recognizing patterns, was applied to identify phosphorus-deficient plants. Parameters (γ, σ²) of LS-SVM were optimized by cross-validation, and several conventional, two-class classification methods such as linear discrimination analysis and K-nearest neighbors were also used comparatively for identification. Identification rates in excess of 86% were achieved with the LS-SVM model for both the training set and the prediction set. The overall results indicated that NIR spectra combined with LS-SVM could be used efficiently for pre-visual diagnostics of phosphorus deficiency in mini-cucumber plants.

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