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Rumbekwan, Julio Ignasius Wangjaya
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Computer Science & IT Economics, Econometrics & Finance Education Engineering Social Sciences

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Application of Convolutional Neural Networks for Tomato Fruit Ripeness Identification Rumbekwan, Julio Ignasius Wangjaya; Aryanto, Joko
Journal of Scientific Research, Education, and Technology (JSRET) Vol. 4 No. 1 (2025): Vol. 4 No. 1 2025
Publisher : Kirana Publisher (KNPub)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58526/jsret.v4i1.645

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
Co-Authors Joko Aryanto