Predictive Analysis of Pesticide Residues in Fruits and Vegetables Using Convolutional Neural Network to Enhance Food Safety

Food safety and the detection of harmful pesticide residues in fruits and vegetables have become critical concerns in public health, driving the need for advanced analysis methods. This work intends to develop a web application that uses Convolutional Neural Network (CNN)-based analysis for structured data on pesticide residue evaluation on fruits and vegetables. With the rapid expansion of wireless networks, enhanced security measures are critical in light of security threats and intrusions due to such networks. It introduces a novel approach with a CNN model that optimizes the detection and classification of pesticide residues from fruits and vegetables. Since the CNN-based model has high accuracy and low computational overhead, it efficiently analyzes any structured dataset. To maximize detection abilities, comprehensive data regarding several types of pesticides are taken into consideration. The CNN model is proven to outperform conventional techniques in machine learning through simulations and real-life experiments regarding detection accuracy, reduced false-positive rates, and faster processing times. Its lightweight design enables it to be integrated into scalable web applications, which makes it a dependable and efficient solution in augmenting food safety.