A comprehensive approach to detecting chemical adulteration in fruits using computer vision, deep learning, and chemical sensors

• Published in: Intelligent systems with applications 2024-09, Vol.23, p.200402, Article 200402
• Main Authors: Sattar, Abdus, Ridoy, Md. Asif Mahmud, Saha, Aloke Kumar, Babu, Hafiz Md. Hasan, Huda, Mohammad Nurul
• Format: Article
• Language: English
• Subjects: Formaldehyde detection sensor; Hybrid Model; Machine learning, Deep Learning; SensorNet; Toxic chemical
• ISSN: 2667-3053; 2667-3053
• DOI: 10.1016/j.iswa.2024.200402

•Machine-learning algorithms and deep-learning models are evaluated separately to mitigate this issue. Alongside this, a computer vision-based detection method coupled with a hybrid model that combines deep learning and chemical sensors is proposed.•The above-mentioned sensor data, along with the previously captured images of both fresh and chemical-mixed states, are being integrated into a hybrid model.•Using both sensor data and captured images in a proposed model named “SensorNet” provides the highest accuracy of 97.03 %, which is substantial compared to the previous “DurbeenNet” model.•Through the utilization of these fruit samples, chemical sensors provide instantaneous detection, identifying the specific toxic substances present in the contaminated fruits. Contamination of harmful additives in fruits has become a concerning norm these days. Owing to the great popularity of fruits, dishonest vendors frequently use harmful chemicals to contaminate fruits to extend their shelf life, which is extremely dangerous for the general public's health. To mitigate this issue, machine-learning algorithms like Decision Tree Classifier, Naïve Bayes and a deep learning model named “DurbeenNet” are evaluated separately. Alongside, a computer vision-based detection method coupled with a hybrid model is proposed that combines deep learning and chemical sensor. Formaldehyde Detection Sensor is used in this experiment to take reading of the sensor data. Mango, Apple, Banana, and Malta are taken as sample fruits in this study. Sensor data for both fresh and chemical-mixed fruit is newly collected using Formaldehyde Detection Sensor. The above mentioned sensor data along with the previously captures images of both fresh and chemical-mixed state are being integrated to a hybrid model. Among two machine learning algorithms naïve bayes come up with 82 % accuracy. Using both sensor data and captured image data, the proposed model “SensorNet” provides highest accuracy of 97.03 % which is substantial than “DurbeenNet” model's accuracy. Through the utilization of these fruit samples, formaldehyde detection sensor provides instantaneous detection, identifying the specific toxic substances present in the contaminated fruits.