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Multi-scale predictive modeling of phenology and carotenoid content in carrots using spectral techniques, colorimetry, and artificial intelligence

agricultural-science
data-mining-and-machine-learning
data-science

Abstract

Objective. This study presents an integrated, multi-scale approach for the non-destructive estimation of phenological stages and carotenoid content in carrots by combining spectral techniques, colorimetry, and artificial intelligence.

Methods. Six commercial varieties, including orange, yellow, white, and purple genotypes, were evaluated under field and laboratory conditions using multispectral drone imagery, high-resolution spectroradiometric signatures, RGB images, and CIELAB color measurements. A hierarchical modeling framework was developed across two phases: (i) spectral modeling using UAV-based multispectral indices, textural and geometric metrics, and laboratory-generated hyperspectral signatures; and (ii) colorimetric index from RGB images.

Results. Using UAV-based multispectral field data, phenological prediction indices achieved high classification performance (F1-scores > 0.90) when modeled with a Random Forest classifier, supported by distinct spectral signatures associated with canopy development and senescence. In parallel, carotenoid content estimation using a Random Forest regression model demonstrated strong predictive accuracy (R² = 0.897; RMSE = 0.584), with the Plant Senescence Reflectance Index (PSRI) and Carotenoid Reflectance Index (CRI) identified as the most influential predictors. A complementary laboratory-based Random Forest regression model using high-resolution spectral signatures achieved near-perfect predictive performance (R²= 0.987). SHAP analysis identified physiologically relevant wavelengths in the green (540–550 nm) and red-edge (~700 nm) regions as the primary drivers of carotenoid concentration. Likewise, a novel colorimetric index (ICarot), derived from CIELAB parameters, enabled accurate image-based carotenoid estimation (R² = 0.85).

Conclusion. This study introduces an innovative multi-sensor framework for precision agriculture and automated postharvest quality control, enabling rapid, objective, and scalable phenotyping in carrot production systems. Through the integration of spectral, colorimetric, and AI-based approaches, the proposed methodology effectively captures both internal nutritional attributes and external quality traits within a unified, non-destructive assessment pipeline.

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