A Comprehensive Analysis of Image Processing Methods for Agricultural Product Quality Control

In the post-harvest stages of agricultural products, labor shortages and poor-quality control lead to significant market losses. The automated industries for agricultural products that use machine learning are evolving, with a recent focus on deep learning (DL). Computer vision (CV) serves as an essential part in the automation method for fruits and vegetables, where image processing (IP) ensures proper product categorization, addressing the issue of product loss. This paper focuses on IP techniques in sequential procedures, which can improve efficiency and precision in agricultural product sorting and grading. Deep neural networks and algorithms are effective because they achieve high classification accuracy with a low loss percentage.