ENHANCING AUTOMATED AGRICULTURAL PRODUCT GRADING WITH ADVANCED DEEP LEARNING TECHNIQUES

Abstract

This paper explores the most effective methods for enhancing automated grading systems for agricultural products using advanced deep-learning technologies. Traditional manual grading is labor-intensive and subjective, leading to inconsistencies in quality assessment. Automated systems leveraging machine vision and deep learning offer a promising alternative. This work systematically finds out the best practices of improving grading accuracy, efficiency and reliability through comparing different deep-learning oriented models including EfficientNets, Residual Network (ResNets), and Convolutional Neural Networks (CNNs). The dataset used in the case study includes various images of agricultural products that were carefully labeled and expanded to provide variation and increase the model’s ability to generalize. Finally, of all models, we received the highest results, so efficiency of EfficientNet was defined as the highest, and its accuracy reached 96. 8%, precision – 96. 3%, recall – 96. 5%, and F1-score – 96. 4 %; the highest specificity and ROC-AUC values were also obtained. , but due to it requiring many computational resources and taking more time to train, more optimization is needed. As for the future work, researchers should improve the scalability of the models, add more data, and adopt IoT connected applications. These studies provide the evidence about the further prospective of complex deep learning models in the development of the automated grading systems while adding values in increasing agricultural yields alongside with the product quality.