Estimation of Anthocyanins in Homogeneous Bean Landraces Using Neuroevolution

A pigment of great interest is the anthocyanins. It is due to the nutritional benefits discovered in various foods, such as common beans. In this work, we report the estimation of anthocyanins in homogeneous colored bean landraces using neuroevolution. Two neuroevolution techniques, NEAT and DeepGA, were implemented to find this task’s suitable neural network structure. Both techniques were compared against a Convolutional Neural Network (CNN) experimentally developed called AnthEst-Net architecture, which found competitive results in anthocyanin estimation. The input data of the network architectures were two-color characterizations, two-dimensional histograms, and data vectors. The accuracies obtained on the test set in HSI color space were 85.38 ± 11.77 and 87.89 ± 9.67 for DeepGA and AnthEstNet architecture, respectively. Regarding CIE L*a*b* color space, DeepGA obtained an accuracy of 86.85 ± 11.08, while AnthEstNet got 87.08 ± 14.19. Results suggest that the architecture reported by DeepGA is suitable for anthocyanins estimation.