Quick Quality Analysis on Cereals, Pulses and Grains Using Artificial Intelligence

Our purpose is to design a system for Quick Quality analysis of cereals, pulses, and grains using Artificial Intelligence implementing hardware and software technologies, which could swiftly analyze the type and quality of bulk grains without human intervention. Our aim is detecting and image processing to assess the grain's quality, which is also used as food for livestock, that is an important problem since high food quality has a great impact on animal health, overcoming the limitations of prior work in this field. In our methodology, the complete grain is analyzed to determine its quality by placing it in a controlled environment with a 13 Megapixel, 4K Camera Module to perform the initial step for image processing procedures, before implementing a Neural network. Surpassing the hurdles of samples to estimate the quality of the complete grains, we demonstrated a full-grain scan technique, resulting in a unique hardware system that can more efficiently estimate the quality of the entire grains. Furthermore, our technology yields faster results since it captures the moving grains on the conveyor belt using a precise, fast camera module, which is analyzed by expeditious NVidia Jetson Nano. We applied automation in every step of this process by using vacuum tubes to collect the grains, a filter to align them, conveyor belt with a variable notch for optimum grain disposal. In summary, our study describes innovative developments stemming from a system that provides proper analysis of bulk grains, cereals, and pulses as well as automation of the system.