Classifying cultivars of rice (Oryza sativa L.) based on corrected canopy reflectance spectra data using the orthogonal projections to latent structures (O-PLS) method

To improve the accuracy in discriminating plant species or genotypes in the field with canopy spectral data, a number of statistical methods incorporating measurement techniques have been developed. This study analyzed canopy reflectance spectra collected at the booting stage by using partial least square regression in combination with discriminant analysis (PLS-DA) to establish a classification model for the discrimination of three mega rice cultivars. To improve the model's capability to interpret and sharpen the separation between cultivars, PLS-DA was combined with orthogonal projection to the latent structure (O-PLS) to derive the OPLS-DA models by removing noise and the Y-orthogonal variation. The ground-based high-resolution reflectance spectra (330–1030 nm) were acquired from paddy field experiments during the growing periods, and were recalculated at intervals of 10 nm. With the PLS-DA approach, the total accuracy for discriminating three cultivars in the calibration datasets was 90% and was above 80% for individual cultivars. In the validation datasets, a similar capability for cultivar discrimination was obtained for both pooled and individual cultivars. However, the Y-orthogonal variation might be embedded within the PLS-DA model. Using the OPLS-DA approach, the large variation within rice cultivars (the intra variation) was effectively removed to improve the performance of both group separation and model establishment. The overall accuracy reached 100% in the calibration datasets and had superior discrimination than the PLS-DA model in the validation datasets. Therefore, the OPLS-DA method is recommended for establishing a classification model for the cultivar discrimination of rice in the vegetative phase using remotely sensed canopy reflectance spectra.

► Classifying rice cultivars based on corrected canopy reflectance spectra data.
► Using PLS to construct a classification model (PLS-DA) for discriminant analysis.
► PLS-DA combined with OPLS (OPLS-DA) to remove the intra variation.
► The OPLS-DA models could improve group separation and model establishment.