Intelligent Pesticide Residue Detection Systems Using Image Processing and AI Tools

Authors:

Neha Joshi¹, Nikita Koli²

^1,2Department of Electronics, Willingdon College.

Abstract - The chase of rapid agricultural productivity has adopted a dangerous reliance on chemical intrusion, often at the cost of essential food safety. Obsessed by dwindling profit margins and limited farming land, many farmers now regularly over-apply pesticides, many times ignoring statutory Maximum Residue Limits (MRLs). This pervasive chemical saturation has initiated a risky feedback loop which include increasing toxic bioaccumulation in the human body correlated with intensifying global cancer rates, while the land itself suffers from profound degradation. As soil fertility collapses, farmers find themselves tethered to a system that yields progressively inferior, nutrient-depleted crops. This study scrutinizes how modern computational frameworks can interrupt this cycle by enhancing food safety oversight. By assessing the practice of advanced image processing and deep learning architectures explicitly Convolutional Neural Networks (CNNs) and Vision Transformers; this work reconnoitres the evolution from slow, destructive laboratory testing to non-invasive, high-speed digital diagnostics. Furthermore, it highlights the integration of sophisticated technologies specifically image processing and deep learning as a robust methodology for recognizing chemical residues on fresh produce.

Keywords- Maximum Residue Limits (MRLs), Image processing, deep learning architectures, Convolutional Neural Networks (CNNs), Vision Transformers