Performance comparison of algorithms in the classification of fresh fruit types based on MQ array sensor data

Abstract

Accurate classification of fresh fruit types is essential in the agricultural sector for ensuring quality control, minimizing waste, and enhancing food safety across the supply chain. This study evaluates the performance of four machine learning algorithms—artificial neural network (ANN), K-nearest neighbors (KNN), logistic regression (LR), and random forest (RF)—in classifying fruit freshness based on data obtained from electronic noses equipped with MQ array sensors. Experiments were conducted using a comprehensive dataset comprising various fruit combinations, and model performance was assessed using accuracy, precision, recall, and F1 score metrics. Results indicate that the RF algorithm achieved the highest accuracy (100%) and precision (1.00), demonstrating superior performance in both classification accuracy and computational efficiency. ANN and KNN also performed well, with accuracies of 96.80% and 97.10%, respectively, while LR yielded a lower but still effective accuracy of 91.16%. Statistical analysis confirms that RF's superior performance is statistically significant when compared to the other algorithms. These findings suggest that RF is the most effective algorithm for fruit freshness classification using electronic nose data, offering fast and reliable results that are well-suited for integration into real-time monitoring systems in agricultural and food retail applications.