Enterprise AI Analysis
A Mobile Application for Flower Recognition System Based on Convolutional Neural Networks
This study developed a mobile application for flower recognition using CNNs. Three models (MobileNet, DenseNet121, Xception) were trained with transfer learning and fine-tuning. DenseNet-121 with SGD and Global Average Pooling achieved 95.84% accuracy, outperforming other models and traditional methods. This demonstrates the viability of CNNs for mobile flower classification.
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Deep Analysis & Enterprise Applications
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DenseNet-121
Best Performing CNN Model
| Model | Strengths | Weaknesses |
|---|---|---|
| MobileNet |
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| DenseNet-121 |
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| Xception |
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Optimization Process Flow
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SGD
Best Optimizer for DenseNet-121 & Xception
Impact of Freezing Ratios
The study found that 0% freezing produced better results for all models, indicating that retraining the entire model leads to superior performance. This is crucial when the dataset differs significantly from pre-training data like ImageNet. This suggests that for distinct enterprise datasets, a full retraining approach may yield better accuracy.
95.84%
Mobile App Accuracy (DenseNet-121)
Real-time Flower Recognition on Mobile
The developed mobile application allows users to take real-time images using their phone camera and identify the type of flower. The best-performing DenseNet-121 model, converted using TensorFlow-Lite, provides quick and easy access to botanical information for non-specialists. This enhances user engagement and accessibility to expert-level knowledge.
| Device | CPU | GPU | Avg. Exec. Time (ms) |
|---|---|---|---|
| Casper Via S | 4x 2.0 GHz ARM Cortex-A53 | PowerVR GE8320 | 279.90 |
| Xiaomi Redmi Note 9 Pro | 2.3 GHz ARM Cortex-A76 | Adreno 618 | 153.56 |
| Xiaomi Redmi 9 | 2x 2.0 GHz ARM Cortex-A75 | Mali-G52 MC2 | 68.91 |
| Samsung Galaxy A70 | 2x 2.0 GHz ARM Cortex-A76 | Adreno 612 | 73.47 |
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Your AI Implementation Timeline
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Phase 1: Discovery & Strategy
Initial consultations to understand your business needs, data infrastructure, and strategic objectives. Deliverable: AI Strategy Blueprint.
Phase 2: Data Preparation & Model Training
Collecting, cleaning, and preparing enterprise data. Training and fine-tuning custom AI models to meet specific performance requirements.
Phase 3: Integration & Deployment
Seamless integration of trained AI models into existing systems and workflows. Deployment to target environments (e.g., cloud, edge devices).
Phase 4: Monitoring & Optimization
Continuous monitoring of AI system performance, regular updates, and iterative optimization to ensure sustained high accuracy and efficiency.
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