Fruit Classification System with Deep Learning and Neural Architecture Search

TL;DR

This paper presents a deep learning-based fruit classification system utilizing Neural Architecture Search to optimize neural network design, achieving high accuracy and outperforming previous models in fruit detection tasks.

Contribution

The study introduces a novel fruit classification model that employs Neural Architecture Search to automatically optimize neural network structures for improved detection performance.

Findings

Achieved 99.98% mAP in fruit detection

Outperformed previous studies in accuracy and precision

Validated the effectiveness of NAS in fruit classification

Abstract

The fruit identification process involves analyzing and categorizing different types of fruits based on their visual characteristics. This activity can be achieved using a range of methodologies, encompassing manual examination, conventional computer vision methodologies, and more sophisticated methodologies employing machine learning and deep learning. Our study identified a total of 15 distinct categories of fruit, consisting of class Avocado, Banana, Cherry, Apple Braeburn, Apple golden 1, Apricot, Grape, Kiwi, Mango, Orange, Papaya, Peach, Pineapple, Pomegranate and Strawberry. Neural Architecture Search (NAS) is a technological advancement employed within the realm of deep learning and artificial intelligence, to automate conceptualizing and refining neural network topologies. NAS aims to identify neural network structures that are highly suitable for tasks, such as the detection of fruits. Our suggested model with 99.98% mAP increased the detection performance of the preceding research study that used Fruit datasets. In addition, after the completion of the study, a comparative analysis was carried out to assess the findings in conjunction with those of another research that is connected to the topic. When compared to the findings of earlier studies, the detector that was proposed exhibited higher performance in terms of both its accuracy and its precision.