Summary of Fruit Classification System with Deep Learning and Neural Architecture Search, by Christine Dewi et al.

Fruit Classification System with Deep Learning and Neural Architecture Search

by Christine Dewi, Dhananjay Thiruvady, Nayyar Zaidi

First submitted to arxiv on: 4 Jun 2024

GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This paper investigates fruit identification using various methodologies, including machine learning and deep learning. The authors identify 15 distinct categories of fruits, including Avocado, Banana, Cherry, and others. Neural Architecture Search (NAS) is used to automate the design of neural networks for tasks like fruit detection. The proposed model achieves a high mAP of 99.98%, outperforming previous studies using Fruit datasets. A comparative analysis is also performed with another study on the topic.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Fruit identification involves looking at pictures and categorizing them by what type of fruit they are. Researchers use different methods to do this, like computer programs and machine learning. In this study, scientists found 15 main categories of fruits, like Avocado, Banana, and Apple. They also used a special tool called Neural Architecture Search (NAS) that helps design the best way for computers to recognize fruits. The new model is really good at recognizing fruits, with an accuracy of 99.98%. This study compares well with other research on fruit recognition.