Detection of Fruit Ripeness and Defectiveness Using Convolutional Neural Networks
Journal Title: Information Dynamics and Applications - Year 2024, Vol 3, Issue 3
Abstract
The classification of fruit ripeness and detection of defects are critical processes in the agricultural industry to minimize losses during commercialization. This study evaluated the performance of three Convolutional Neural Network (CNN) architectures—Extreme Inception Network (XceptionNet), Wide Residual Network (Wide ResNet), and Inception Version 4 (Inception V4)—in predicting the ripeness and quality of tomatoes. A dataset comprising 2,589 images of beef tomatoes was assembled from Golden Fingers Farms and Ranches Limited, Abuja, Nigeria. The samples were categorized into six classes representing five progressive ripening stages and a defect class, based on the United States Department of Agriculture (USDA) colour chart. To enhance the dataset's size and diversity, image augmentation through geometric transformations was employed, increasing the dataset to 3,000 images. Fivefold cross-validation was conducted to ensure a robust evaluation of the models' performance. The Wide ResNet model demonstrated superior performance, achieving an average accuracy of 97.87%, surpassing the 96.85% and 96.23% achieved by XceptionNet and Inception V4, respectively. These findings underscore the potential of Wide ResNet as an effective tool for accurately detecting ripeness levels and defects in tomatoes. The comparative analysis highlights the effectiveness of deep learning (DL) techniques in addressing challenges in agricultural automation and quality assessment. The proposed methodology offers a scalable solution for implementing automated ripeness and defect detection systems, with significant implications for reducing waste and improving supply chain efficiency.
Authors and Affiliations
Joshua S. Mommoh, James L. Obetta, Samuel N. John, Kennedy Okokpujie, Osemwegie N. Omoruyi, Ayokunle A. Awelewa
Keywords
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- EP ID EP752643
- DOI https://doi.org/10.56578/ida030304
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