International Journal of Science, Engineering and TechnologyAuthors: Naykodi Sanika Dnyaneshwar, Bhosale Sachin Bajirao, Bangar Abhishek Popat, Karad Vandana A
Abstract: The extensive and uncontrolled use of agrochemicals in contemporary agriculture has sparked serious worries about consumer health and food safety. Conventional laboratory-based analytical methods, like high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS), offer high precision but are limited by high costs, time-consuming sample preparation, produce destruction, and a lack of portability for regular users. This research examines the architecture, techniques, and integration of low-cost, AI-driven multimodal sensor fusion systems intended for real-time pesticide contamination risk screening in order to close this gap. Multimodal frameworks greatly improve classification accuracy by storing fruit or vegetable samples in a controlled chamber and combining heterogeneous inputs, namely optical-spectral signatures from an AS7341 multispectral sensor and volatile organic compound (VOC), temperature, and humidity telemetry from a BME680 sensor aggregated via an ESP32 microcontroller. We look at the underlying machine learning pipelines, discuss important technical issues including data heterogeneity and sensor cross-sensitivity, and suggest future paths for edge-computed, mobile-integrated food safety screening.
