DESIGN AND IMPLEMENTATION OF ADVANCED PESTICIDE RESIDUE DETECTING TECHNIQUE FOR FRUITS AND VEGETABLES USING IOT AND ML BASED METHODS

Abstract

Abstract - The widespread use of pesticides in agriculture has led to concerns about pesticide residue in fruits and vegetables, posing serious health risks to consumers. Traditional methods of pesticide residue detection, such as gas chromatography and liquid chromatography, are slow, expensive, and laborious, making them unsuitable for rapid detection and widespread use. Biosensors offer a cost-effective alternative for screening pesticide residues in food matrices, but they lack the ability for remote control and information sharing. To address these challenges, this paper proposes a novel approach using IoT (Internet of Things) and ML (Machine Learning) technologies for pesticide residue detection in fruits and vegetables. The integration of IoT sensors allows for real-time data collection from agricultural fields and post-harvest storage facilities. ML algorithms are employed for data analysis and prediction, enabling accurate and efficient detection of pesticide residues. The necessity of pesticides in agriculture is emphasized due to the increasing population and demand for food, leading to adulteration and overuse of pesticides. The adverse impact of pesticides on human health, particularly organophosphate pesticides like chlorpyrifos, underscores the urgency for reliable detection methods. Various types of pesticides are discussed, highlighting the need for targeted detection methods. The problem statement emphasizes the critical challenge of pesticide contamination in the agricultural sector and the necessity for effective monitoring systems to ensure food safety. The motivation behind the project is driven by concerns about public health, environmental impact, regulatory compliance, and consumer awareness. The paper provides an overview of embedded systems architecture and applications, emphasizing their relevance in addressing food safety concerns through real-time monitoring and detection of pesticide residues. In conclusion, the proposed IoT and ML-based approach offers a promising solution for pesticide residue detection in fruits and vegetables, contributing to enhanced food safety, environmental sustainability, and consumer confidence in the food supply chain. Keywords: Pesticide residue detection, Food safety, Biosensors, Gas chromatography, Liquid chromatography, Maximum residue levels (MRLs), Ethylene gas sensors, IR sensors, Chromogenic substrates, Chemometric-aided spectrofluorimetric method, Electronic nose, NDVI (Normalized Difference Vegetation Index), THz spectroscopy, D-SERS (Disposable Surface Enhanced Raman Spectroscopy).