Year: 2024
Type: article
Abstract: This study presents the design and evaluation of a deep convolutional neural network (CNN) model for accurately classifying fig ripeness stages. Traditionally, fruit ripeness classification has been conducted manually, which presents several drawbacks, including heavy reliance on human labor and inconsistencies in determining fruit ripeness. By leveraging advanced deep learning techniques, specifically CNNs, this research aims to automate the fig ripeness classification process. The CNN architecture was developed and trained using MATLAB software, targeting three ripeness categories: ripe, half-ripe, and unripe. The methodology involved pre-processing the fig images and configuring the CNN model with multiple convolutional, batch normalization, and max pooling layers specifically for fig classification tasks. The final CNN model achieved an impressive accuracy rate of 94.44%, significantly surpassing results from previously reported studies. The developed model is a promising tool for automating fig ripeness classification, contributing to advancements in precision agriculture and smart farming technologies.
Source: ESTEEM Academic Journal
Authors: Siti Juliana Abu Bakar, Hanis Raihana Musa, Mohamed Syazwan Osman, Mohamed Mydin M Abdul Kader, Denis Eka Cahyani, Samsul Setumin
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: bronze
OpenAlex ID: W4403433686