Non-invasive detection and classification of chemically ripened mangoes and bananas through multiple deep learning frameworks using real-time dataset

Abstract Chemically ripened mangoes and bananas are increasingly common worldwide and pose significant health risks due to the presence of carcinogens and other harmful substances. Owing to their cumbersome processes, the existing gold standard laboratory-based techniques for discriminating naturally or chemically ripened fruit often face challenges. This study aims to overcome these limitations by developing an onsite device specifically designed to detect chemically ripened mangoes and bananas to provide a faster and more cost-effective solution. This research uses advanced computer vision (CV) and deep learning (DL) techniques to detect and analyze chemically ripened mangoes and bananas. This research work employed several models, including K-nearest neighbor (KNN), random forest, support vector machine (SVM), convolutional neural networks (CNNs), and regional CNNs. In this study, the authors created their own real-time dataset for both naturally and chemically ripened mangoes and bananas. The proposed deep learning and machine learning models were trained and tested on a custom dataset of both fruits mango and banana images to discriminate chemically ripened fruits in an effective manner. Among all the models, the CNN achieved the highest accuracy of 93.24% and 96.25%, demonstrating its superior capability for this application. To scale up this approach, the authors implemented the system in real time via the Raspberry Pi board and a Pi camera. This prototype was instrumental for the authors to capture live images of fruits and process them via trained models to detect chemically ripened fruits. This approach enables efficient and accurate real-time detection, making this system feasible for practical applications. This work has the potential to leverage CV and DL techniques to combat fruit adulteration, providing a reliable and automated solution for ensuring food safety. The findings of this work infer that a CNN can accurately detect adulterated fruits, making it a promising tool for future developments in this field. Graphical Abstract