Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms.

ABSTRACT: In this study, we investigated the use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties. For this purpose, a spectroradiometer was utilized to collect hyperspectral data in the VIS-NIR-SWIR range, and 17 AIAs were applied to classify lettuce plants. The results showed that the highest accuracy and precision were achieved using the full hyperspectral curves or the specific spectral ranges of 400-700 nm, 700-1300 nm, and 1300-2400 nm. Four models, AdB, CN2, G-Boo, and NN, demonstrated exceptional R² and ROC values, exceeding 0.99, when compared between all models and confirming the hypothesis and highlighting the potential of AIAs and hyperspectral fingerprints for efficient, pr

SUBMITTER: Falcioni R 

PROVIDER: S-EPMC10059284 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature