Image Based Papaya (Carica Papaya Linn.) seed germination evaluation by ResNet50
Issued Date
2025-01-01
Resource Type
eISSN
27738752
Scopus ID
2-s2.0-105001645153
Journal Title
ASEAN Journal of Scientific and Technological Reports
Volume
28
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
ASEAN Journal of Scientific and Technological Reports Vol.28 No.1 (2025)
Suggested Citation
Pornpanomchai C. Image Based Papaya (Carica Papaya Linn.) seed germination evaluation by ResNet50. ASEAN Journal of Scientific and Technological Reports Vol.28 No.1 (2025). doi:10.55164/ajstr.v28i1.254804 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/109415
Title
Image Based Papaya (Carica Papaya Linn.) seed germination evaluation by ResNet50
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Author's Affiliation
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Abstract
The researchers developed a papaya seed germination evaluation system (PSGES) using ResNet50, a convolutional neural network, to evaluate papaya seed germination potential from single seed images. Using a comprehensive dataset of 12,600 papaya seed images, they allocated 11,600 images for training (with an 80/20 training-testing split) and 1,000 images for validation. The system achieved impressive performance metrics, with an overall accuracy of 99.58% and an average processing time of 1.4705 seconds per image. The training dataset demonstrated exceptional performance with 0.9958 accuracy, 0.9980 precision, 0.9972 recall, and 0.9976 F1-score. When compared to existing seed evaluation methods in the literature, PSGES showed superior precision at 99.59%, significantly outperforming Rice (ANN) at 92.80%, Beet (NIR) at 89.00%, and Chili (ANN) at 71.71%. The study revealed a papaya seed germination rate of 84.92%, calculated from (10,000 + 20 + 677 + 3) ÷ 12,600 × 100. Notably, ResNet50 demonstrated superior performance compared to six other CNN architectures tested, including AlexNet, GoogLeNet, Inceptionv3, ResNet18, ResNet101, and VGG16, in both training and validation performance metrics.