Automated and Enhanced Classification of Persea Americana Using Optimized Deep Convolutional Neural Networks With Improved Training Strategies for Agro-Industrial Settings

The agro-industrial sector faces significant challenges in product classification, which directly affect product quality, production efficiency and food safety. This paper proposes a machine learning model that correctly identifies the different attributes of Persea americana. For this, an automatic agro-industrial plant was implemented following industrial standards where advanced image processing techniques were used on a dataset of 346 images for training and 146 images for testing, with three deep convolutional neural networks with improved training strategies and advanced validation techniques including True Skill Statistic (TSS), Cohen's Kappa (K), Threat Score (TS), Heidke Skill Score (HSS) and Probability of Error (Pe). The results showed that the DenseNet model outperforms other state-of-the-art models in accuracy, reaching an F1 score of 99.27%, ResNet 50 reached 99.26% and EfficientNet B4 reached 99.19%, also in the validation phase TSS, K, TS and HSS for all models were higher than 0.98 while the Pe index was higher than 0.55. It is concluded that the DenseNet model is shown to be the effective and reliable technique for the classification of Persea Americana. These promising results open new possibilities for the implementation of machine learning in the agri-food industry. For future research, the possibility of expanding the data set and extending the application of this model to other varieties of fruits is proposed.