Identification of Rotten Fruits using Deep Learning Techniques

Prajosh, Janush (2023) Identification of Rotten Fruits using Deep Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

Determining whether a fruit is fresh or rotten has traditionally been a challenging task as it has always been done manually. However, using deep learning models offers a more accurate and automated approach to solving this problem. This study evaluates the performance of four deep learning models such as Inception Net V3, SGD-CNN, ResNet50, and Efficient Net V2B0, on three types of datasets that include unbalanced, majority under-sampled, and minority oversampled datasets. The food wastage issue is a significant problem globally, and automating fruit quality assessment using deep learning can enhance the efficiency of the supply chain and reduce waste. This research contributes to the growing field of agricultural AI by displaying the efficacy of different models in determining the fruit's quality. Additionally, it emphasizes the importance of dataset composition in the model's performance and lays the foundation for future advancements in the field.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Chikkankod, Arjun UNSPECIFIED
Uncontrolled Keywords:	Rotten fruits Detection; Deep Learning; Supply chain Management; Food waste reduction; Class balancing techniques
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science S Agriculture > S Agriculture (General) H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Food Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > Business Logistics > Supply Chain Management
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	20 May 2025 14:48
Last Modified:	20 May 2025 14:48
URI:	https://norma.ncirl.ie/id/eprint/7592

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