Cereal Grain

Cereal grain research focuses on improving the efficiency and accuracy of grain quality inspection, a crucial step in food production and trade. Current research employs machine learning and computer vision techniques, including deep learning models and anomaly detection algorithms, to analyze large-scale image datasets of various cereal grains (wheat, maize, rice, sorghum) for automated quality assessment and damage detection. This work aims to enhance food security and optimize grain handling processes by automating tasks currently performed manually, leading to increased speed and consistency in grain inspection.