Bibliografiske detaljer
| Titel: |
Hybrid Cassava Identification from Morphometric Analysis to Deep Convolutional Neural Networks and Confirmation Strategies. |
| Authors: |
Changtor, Phanupong1, Ratiphaphongthon, Wachiraphong2, Saengthong, Maturada1, Buddhachat, Kittisak1,3, Yimtragool, Nonglak1,3,4 nonglakp@nu.ac.th |
| Source: |
Trends in Sciences. May2025, Vol. 22 Issue 5, p1-20. 20p. |
| Subject Terms: |
*IMAGE recognition (Computer vision), *CONVOLUTIONAL neural networks, *PRINCIPAL components analysis, *CROP management, *DEEP learning |
| Abstract: |
The correct identification of cassava varieties is critical for crop management, such as for developing high-value products or against agricultural pests. In this study, plant characteristic regions used for classification were verified by principal component analysis (PCA) techniques. A deep learning method was applied using well-known pretrained models to identify hybrid cassava through image classification. The models employed--ResNet-18, VGG-16, AlexNet, and GoogLeNet--yielded impressive accuracies in three-fold cross-validation experiments, achieving 100, 99.06, 99.06, and 98.59 % averaged accuracy, respectively. The fine-tuned ResNet-18 model had the highest accuracy and was selected for identifying hybrid cassava. A confusion matrix revealed 3 misidentifications. Cultivar variety (cv) R72 was mistakenly classified as R5 in both the 1st and 2nd folds and as R1 in the 2nd fold. Additionally, we utilized Local Interpretable Model- agnostic Explanations (LIME) to ensure that our models offered insightful explanations for their decision-making processes. The outcomes from Principal Component Analysis (PCA) and Local Interpretable Model-agnostic Explanations (LIME) exhibited close resemblance, particularly within the region encompassing the stem, branch, petiole, and stipule of cassava. These findings were leveraged for the identification of the 3 distinct cultivated cassava varieties. The results demonstrated the efficacy of deep learning as a potent technique for discerning hybrid cassava varieties, presenting promising prospects for its practical deployment in on-site testing and widespread adoption due to its time-saving capabilities. [ABSTRACT FROM AUTHOR] |
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| Database: |
Academic Search Premier |