Revolutionizing lemon grading: an automated CNN-based approach for enhanced quality assessment

Lemons, renowned for their versatile applications in the culinary world and various industries, have become a staple commodity worldwide. The demand for high-quality lemons continues to soar, underscoring the need for reliable and efficient grading methods. Traditional lemon grading, which is largely reliant on human expertise, often falls prey to subjectivity, human errors, and time-consuming processes. In this paper, we propose a CNN-based lemon grading model to automate and improve the accuracy of lemon quality assessment. The proposed model utilizes CNNs in a modular and sequential fashion for extracting pertinent features from lemon images and categorizing them into distinct quality grades. A comprehensive dataset of lemon images with corresponding quality labels is used to train and validate the model. The experimental results demonstrate the effectiveness of the CNN-based approach in accurately grading lemons, offering a promising solution for enhancing efficiency and objectivity in the lemon grading process.