Application of Convolutional Neural Networks for Tomato Fruit Ripeness Identification

Julio Ignasius Wangjaya Rumbekwan; Joko Aryanto

doi:10.58526/jsret.v4i1.645

Authors

Julio Ignasius Wangjaya Rumbekwan University of Technology Yogyakarta https://orcid.org/0009-0007-5768-5804
Joko Aryanto University of Technology Yogyakarta

DOI:

https://doi.org/10.58526/jsret.v4i1.645

Keywords:

Human Visualization, Identification, Tomato Maturity, Plant Stalk, CNN

Abstract

The identification of tomato ripeness is essential in agriculture to ensure quality and reduce spoilage. Traditional methods that rely on human observation are often inaccurate. This research aims to develop a CNN-based system to accurately identify tomato ripeness, focusing on the ripe and unripe categories. The images were taken while the tomatoes were still on the plant stalk with data collection involving 1.000 images of tomatoes, obtained from Kaggle andtaking pictures of tomatoesat the Poktan Welan Asri Petinggen garden tour site, Yogyakarta. The dataset was divided into training (700 images), testing (150 images), and validation (150 images). The experimental designuses the CNN model with image processing steps such as resizing, labeling, and data augmentation. Testing on this system achieved an accuracy of 92.67%. These findings demonstrate the effectiveness of CNN in detecting tomato ripeness, providing a reliable solution for farmers and agricultural stakeholders

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