Classification of Common Bean Landraces of Three Species Using a Neuroevolutionary Approach with Probabilistic Color Characterization

The common bean is a widely cultivated food source. Many domesticated species of common bean varieties, known as landraces, are cultivated in Mexico by local farmers, exhibiting various colorations and seed mixtures as part of agricultural practices. In this work, we propose a methodology for classifying bean landrace samples using three two-dimensional histograms with data in the CIE L*a*b* color space while additionally integrating chroma (C*) and hue (h°) to develop a new proposal from histograms, employing deep learning for the classification task. The results indicate that utilizing three histograms based on L*, C*, and h° brings an average accuracy of 85.74 ± 2.37 compared to three histograms using L*, a*, and b*, which reported an average accuracy of 82.22 ± 2.84. In conclusion, the new color characterization approach presents a viable solution for classifying common bean landraces of both homogeneous and heterogeneous colors.