Detection of Rotten Fruits Using XGBoost-Based Deep Learning Algorithm with Explainable Artificial Intelligence Models

Abstract: Achieving high accuracy rates in the field of image processing often exceeds the limits of a single model. Therefore, hybridizing XGBoost and deep learning models is a common approach to obtaining more accurate and reliable results. Deep learning models are highly capable of extracting complex and meaningful features from images. However, to effectively classify these features, the use of a powerful machine learning algorithm like XGBoost can further enhance performance. Hybrid models combine the best features of both models, allowing them to achieve higher accuracy rates that would not be possible if used individually. High accuracy improves the model's reliability and effectiveness in application, thereby preventing misclassification and improving overall performance. Therefore, hybridization of models is essential for better results. In this paper, after flattening the extracted features, an XGBoost-based model was trained by utilizing decision trees, and the model achieved an accuracy of 98.813% on the test data. SHAP and XAI LIME were employed to explain the model, providing visualizations of how the features impacted the model's decisions positively or negatively based on their weight values, and demonstrating how these features influenced the decision-making process.