Revolutionizing Smart Agriculture: Enhancing Apple Quality with Machine Learning

El Mehdi Iyoubi, Raja El Boq, Kenza Izikki, Samir Tetouani, Omar Cherkaoui and Aziz Soulhi

Data and Metadata, 2024, vol. 3, .592

Abstract: Agriculture 4.0 is a field that has spread widely around the world in this century, as it has undergone an exceptionally rapid evolution, especially when it comes to fruit recognition. Decisions about their quality are crucial to maximize profits and meet customer expectations. In the past, apples or even other fruits were based solely on visual assessments by experts, which led to errors. These old methods no longer consider the genetic evolution of apples, as they only consider their size, color, and skin imperfections. Digitizing this process saves energy and reduces costs and human error as well. Recent technological advances, which combine AI and CAO at the same time for fruit sorting, make it possible to achieve high levels of quality and meet the growing challenges of food safety on a global scale. This study proposes a machine learning-based multiclass model to improve the accuracy and efficiency of apple quality assessment. The model is trained on a large image dataset of three apple varieties: Gala, Fuji, and Golden Delicious (G.D). The model automatically classifies apples based on attributes such as color, shape, and imperfections, and evaluates their conformity. Experimental results demonstrate the effectiveness of this model, which achieves 97% accuracy in identifying apple varieties and assessing their quality. This approach significantly reduces inspection time and errors, optimizing operations in the production chain.

Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:3:y:2024:i::p:.592:id:1056294dm2024592

DOI: 10.56294/dm2024.592

Access Statistics for this article

More articles in Data and Metadata from AG Editor
Bibliographic data for series maintained by Javier Gonzalez-Argote ().