Smart Grain Analysis Using IOT and CNN

Abstract

Food safety, storage management, and agricultural commerce all depend on the evaluation of grain quality. Grain inspection is still done by hand in many places using basic laboratory testing techniques or visual observation. These conventional methods are subjective, time-consuming, and frequently lead to uneven grading. Farmers may experience unfair pricing and post-harvest losses as a result of this. Automated systems that can provide precise and real-time grain quality evaluation are therefore becoming more and more necessary. The smart grain analysis system presented in this paper combines CNN-based image processing with Internet of Things (IoT) sensing. Grain image capture and automated quality classification are done by the system using a Raspberry Pi device. IoT sensors are used simultaneously to monitor environmental parameters like temperature, humidity, moisture content, and volatile organic compounds. For monitoring and visualization in real time, the gathered data is sent to the Thing Speak cloud platform. Reduced human error, increased grading accuracy, and a scalable, affordable solution appropriate for actual storage and procurement settings are the goals of the suggested system.