Date Fruit classification using Convolutional Neural Network Models

Abstract

The production of date fruits is an important part of Algeria’s agricultural sector, known for its cultivation of diverse date palm species. This study introduces a new method for date fruit classification based on deep learning techniques and designed specifically for use in Algeria with the Keras framework. First, we provide an overview of the wide variety of Algerian date palm cultivars, which have distinct morphological and biochemical features. Then we proceed to investigate challenges associated with precise classification, including color, shape, size, and ripeness.We present the CNN model that is based on Keras and captures all vital features of the data images, which are date fruit. We consider con- structing a dataset for training and validation as it is very important for the accuracy of our model. In order to analyze the classification task, we use the CNN-based algorithm with accuracy, precision, recall, and F1-score metrics to show how well our CNN performs in such a setting. At the same time, we talk about its usefulness when being used in the Algerian agriculture sphere, namely how it can streamline quality control processes for some other commodities, provide better resource management options, or promote competition in the market. Overall, deep learning methods, including CNN using Keras, are shown to be able to overcome these challenges within the Algerian agricultural con- text.By embracing innovative technological solutions, Algeria can harness the power of artificial mtelligence to propel its date industry towards greater efficiency, productivity, and sustainabiliry.