Comprehensive Quality Analysis of Common Vetch (Vicia sativa L.) Varieties Using Image Processing Techniques and Artificial Intelligence

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Common vetch (Vicia sativa L.) is a cool-season annual legume cultivated for grain and forage, valued for its high nutrient content, broad edaphoclimatic adaptability, and suitability for crop rotations. Physical seed attributes are critical for variety classification, quality evaluation, and breeding selection. This study aimed to characterize the nutritional composition, mineral contents, and physical attributes of nine common vetch varieties and to assess the feasibility of binary variety classification using supervised machine learning (ML). Proximate analyses (e.g., crude protein, tannin), macro/micro minerals, and morpho-physical seed descriptors were determined. Multivariate and discriminant analyses were conducted. Binary classifiers were developed with a multilayer perceptron (MLP) and random forest (RF) under stratified 10-fold cross-validation. The highest values were observed for crude protein (22.66%, Alper), ADF (11.36%, Alınoğlu), NDF (16.47%, Alperen), and tannin (3.12%, Alınoğlu). For mineral profiles, Alper, Ankara Moru, and Doruk emerged as prominent varieties. In pairwise discrimination, Ankara Moru vs. Ayaz achieved 89% (MLP) and 90% (RF) accuracy, followed by Ankara Moru vs. Özveren with 88% (MLP) and 90.50% (RF). These results demonstrate that MLP and RF can classify common vetch varieties from physical attributes with high reliability. Integrating compositional, mineral, and morpho-physical data with supervised learning provides an objective, low-cost pathway for variety identification. The approach has direct implications for quality assessment, planting system design, and breeding. Future work should expand datasets, incorporate color-rich/hyperspectral cues, and compare feature-based models with domain-adapted deep learning on larger, multi-site collections.