Automated Fruit Sorting in Smart Agriculture System: Analysis of Deep Learning-based Algorithms

Abstract

Automated fruit sorting plays a crucial role in smart agriculture, enabling efficient and accurate classification of fruits based on various quality parameters. Traditionally, rulebased and machine-learning methods have been employed for fruit sorting, but in recent years, deep learning-based approaches have gained significant attention. This paper investigates deep learning methods for fruit sorting and justifies their prevalence in the field. Therefore, it is necessary to address these limitations and improve the effectiveness of CNN-based fruit sorting methods. This research paper presents a comprehensive analysis of CNN-based methods, highlighting their strengths and limitations. This analysis aims to contribute to advancing automated fruit sorting in smart agriculture and provide insights for future research and development in deep learning-based fruit sorting techniques.