Improving Date Fruit Classification Accuracy Using CNN Models: An Empirical Analysis

Classification of date fruits determines agricultural research, quality control, and market distribution. This paper presents a Convolutional Neural Network (CNN) model intended for the classification of nine various types of date fruits: Ajwa, Galaxy, Medjool, Meneifi, Nabtat Ali, Rutab, Shaishe, Sokari, and Sugaey. With astonishing accuracy in classification 91% the CNN model shows utilising deep learning its ability to distinguish among several data types. Accuracy, recall, F1-score, and confusion matrix define the model's performance; low misclassifications and excellent classification ability are found in the findings. Though the confusion matrix offers specifics on areas needing work, evaluation of training and validation losses reveals effective learning with balanced performance. This paper shows how effectively CNN models might raise accuracy and efficiency in date fruit classification, so supporting agricultural quality management and market analysis.