Year: 2025
Type: article
Abstract: The increasing concern over pesticide residues in fruits and vegetables necessitates the development of efficient monitoring systems to ensure food safety. This research presents a comprehensive solution for real-time pesticide monitoring using a hardware setup based on Arduino and IoT integration. The proposed system incorporates a pH sensor, gas sensor, ESP camera, LCD module, and ESP32 camera for continuous monitoring of fruits and vegetables. The hardware setup collects data on pH levels and gas emissions, providing crucial information about the quality and safety of the produce. The ESP32 camera captures images of the fruits or vegetables, and the data is transmitted to a mobile device in real-time for user-friendly monitoring. Additionally, the system enables remote access to the ESP32 camera output, enhancing accessibility and convenience for users. To enhance the analysis of pesticide contamination, a multi-stage process is proposed for the ESP32 camera output. Initially, the captured images are transmitted to a PC for pre-processing, segmentation, and feature extraction. Subsequently, a machine learning model is employed to analyze the extracted features and identify the presence of pesticides. The output of the ML model provides valuable information by displaying the name of the specific fruit or vegetable and indicating the level of pesticide contamination. This integrated approach offers a practical and efficient solution for real-time monitoring of pesticide residues in fruits and vegetables. The combination of Arduino-based hardware, IoT connectivity, and machine learning analysis ensures a comprehensive and user-friendly system for assessing the safety of agricultural produce. The proposed methodology holds the potential to contribute significantly to food safety practices and facilitate informed decision-making in the agricultural and consumer sectors.
Authors: K Umamaheswari, Dr U Somalatha
Institution: Vemu Institute of Technology
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4411221319