Enterprise AI Analysis
Seeds Image – Introduction and Baseline Experiments with the New Labeled Benchmark for Machine Learning Tasks
This paper introduces the "Seeds Image Data Set," a novel and compact benchmark for evaluating deep neural networks in machine learning tasks. Derived from X-ray images of wheat grains (Kama, Rosa, Canadian), this dataset features 276 grayscale instances and includes robust cross-validation sets. Our analysis applies ten pretrained deep CNNs, demonstrating VGG16's superior performance with nearly 94% accuracy. This benchmark is crucial for developing and testing robust AI solutions in limited-data, real-world scenarios, particularly for transfer learning applications.
Key Impact Metrics
Leveraging specialized datasets like Seeds Image is critical for developing robust and efficient AI models in real-world agricultural and industrial contexts.
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Top Test Accuracy (VGG16)
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Total Dataset Samples
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CNN Architectures Tested
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Wheat Species Identified
Deep Analysis & Enterprise Applications
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Seeds Image Dataset Characteristics
Understanding the Seeds Image Dataset
The Seeds Image Dataset is a new, publicly available benchmark derived from X-ray images of wheat grains, specifically Kama, Canadian, and Rosa species. Unlike synthetic datasets, it reflects real-world data challenges, being compact with 276 grayscale images and exhibiting class imbalance (72 Kama, 96 Canadian, 108 Rosa). This design makes it a robust tool for evaluating algorithm performance, particularly in limited-data scenarios and for studying model robustness against real-world imperfections. It provides a crucial benchmark for deep neural networks where dataset compactness and realistic challenges are paramount.
To ensure rigorous evaluation and mitigate the impact of outliers, the dataset includes five distinct cross-validation sets. Each set's test partition is independent, guaranteeing that no elements are shared between test sets across the folds, thus enhancing the reproducibility and reliability of comparative studies.
Applied Machine Learning Approach
This study leveraged transfer learning, a highly effective technique for deep neural networks, especially with limited datasets. Ten state-of-the-art Convolutional Neural Networks (CNNs), including DenseNet, Inception, ResNet, VGG, MobileNet, and Xception architectures, were chosen for their proven capabilities in image recognition. These networks were pre-trained on the vast ImageNet dataset, allowing their robust feature extraction layers to be frozen and adapted to the specific task of wheat species classification.
To handle the grayscale nature of the Seeds Image dataset while utilizing models pre-trained on RGB (color) images, the single-channel grayscale images were triplicated across three channels. This ensures compatibility with the input requirements of the pre-trained CNNs, enabling them to effectively process the unique characteristics of the wheat grain images.
Enterprise Process Flow: Robust Cross-Validation
Randomly Shuffle Dataset
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Initial 80/20 Train/Test Split
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Generate 5 Independent Cross-Validation Sets
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Train Models on Each CV Set
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Average Performance for Robust Evaluation
Overall Model Performance
Top Performer: VGG16
0 Achieved Test AccuracyThe evaluation revealed interesting performance characteristics among the tested CNN architectures. While many networks achieved high training accuracy, their performance on the test sets varied. Notably, VGG16 emerged as the top performer, demonstrating a robust ability to generalize to unseen data with the highest average test accuracy.
Surprisingly, older and simpler architectures like VGG16 often outperformed or matched the performance of more modern, complex networks (e.g., InceptionResNetV2, DenseNets). This suggests that for datasets with a compact, grayscale nature like Seeds Image, less complex architectures may be more efficient or less prone to overfitting, highlighting the importance of selecting appropriate models for specific data characteristics.
| CNN Type | Test Accuracy | Test Loss | F1-Score (Avg) |
|---|---|---|---|
| DenseNet 121 | 0.8912 | 0.3087 | 0.867 |
| DenseNet 201 | 0.9094 | 0.2662 | 0.910 |
| Inceptionresnet V2 | 0.8440 | 0.4029 | 0.847 |
| Inception V3 | 0.8876 | 0.3295 | 0.880 |
| MobileNet V2 | 0.8947 | 0.3130 | 0.900 |
| ResNet50 V2 | 0.9165 | 0.2317 | 0.917 |
| ResNet152 V2 | 0.9058 | 0.2518 | 0.910 |
| VGG16 |
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| VGG19 | 0.8910 | 0.3856 | 0.880 |
| Xception | 0.8803 | 0.3466 | 0.883 |
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