Date fruit classification using CNN and stacking model

Abstract

In North Africa and the Middle East, the date is the most popular fruit, with millions of tons harvested annually. They are a crucial component of the diet due to their exceptional content of essential vitamins and minerals, which confer a high nutritional value. The ability to accurately identify and differentiate between date varieties is therefore of paramount importance in agriculture. It is crucial for improving agricultural practices, ensuring harvest quality, and contributing to the economic development of date-producing regions. In this paper, we propose a hybrid method for classifying date fruit varieties based on two stages. In the first stage, we select the two best-performing pre-trained models from six experimented deep learning models, and we concatenate the feature maps extracted from these two models. In the second stage, we apply different classification methods, including artificial neural networks (ANN), support vector machines (SVM), and logistic regression (LR). The performance achieved by these methods is 97.22%, 98.46%, and 99.07%, respectively. Then, with the stacking model, we combined these methods, and the performance result was increased to 99.38%. This result demonstrates the effectiveness of the hybrid model for identifying date fruit varieties.