Smart Detection System for Pesticide Contamination in Fruits and Vegetables

Abstract

Pesticide contamination in fruits and vegetables has become a serious concern due to its detrimental effects on human health and the environment. Currently, the widespread utilization of pesticides such as Glyphosate, Chlorpyrifos, Neonicotinoids, Mancozeb, and Pyrethroids during fruit and vegetable cultivation has been associated with adverse health effects in humans. To grow more food for more people, people developed innovative techniques. These days, fruits and vegetables are essential for providing us with the nutrition and energy we require. But occasionally, chemicals are applied to aid in their growth. The goal of the present study is to use an Arduino Mega 2560 microprocessor, which is integrated with an LCD display, spectral triad sensor, pH sensor, gas sensor, and buzzer, to identify pesticides in fruits and vegetables. The system uses real-time sensor data collection and Random Forest analysis powered by machine learning (ML) and the Internet of Things (IoT). The spectral triad sensor captures comprehensive spectral data while the gas and pH sensors monitor the presence of pesticides and acidity. Using the Random Forest method, the machine learning model examines the sensor data to identify potential pesticide contamination. The results are displayed on LCD in addition to a buzzer alert.