Automatic Wheat Grain Quality Analyzer

Manual evaluation of grain quality is time- consuming and subjective, prompting the need for automated solutions. This paper introduces a sophisticated approach to grain quality assessment, leveraging advanced deep learning models that outperform conventional built-in models. These models are designed to effectively capture intricate grain patterns through feature extraction and customized convolutional neural networks, enhancing efficiency and precision.To ensure objective evaluations, a weighted rating system is implemented. This system carefully evaluates predefined categories, assigning weights based on their relative importance. The integration of this weight- ing mechanism improves accuracy and consistency throughout the grain quality assessment process.Automated grain quality assessment reduces subjectivity, enhances quality control, and leverages the capabilities of advanced deep learning models. This research contributes to streamlining the evaluation process in the grain industry, optimizing productivity and ensuring consumer satisfaction.