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license: apache-2.0 |
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--- |
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# LARS-MobileNet-V4 |
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This repository contains the implementation of the lightweight convolutional neural network architecture described in the paper "Advancing Real-Time Crop Disease Detection on Edge Computing Devices using Lightweight Convolutional Neural Networks." |
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https://github.com/lars-uav/LARS-MobileNet-V4 |
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## Overview |
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This project introduces LARS-MobileNetV4, an optimized version of MobileNetV4 specifically designed for real-time crop disease detection on resource-constrained edge devices such as Raspberry Pi. Our implementation achieves 97.84% accuracy on the Paddy Doctor dataset while maintaining fast inference times (88.91ms on Raspberry Pi 5), making it suitable for deployment in agricultural field settings. |
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## Key Features |
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- **Optimized MobileNetV4 Architecture**: Enhanced with Squeeze-and-Excitation (SE) blocks and Efficient Channel Attention (ECA) mechanisms |
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- **Resource-Efficient Design**: Significantly reduced model size (10.2MB) compared to ResNet34 (85.3MB) |
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- **Real-time Performance**: Average inference time of 39ms on CPU and 88.91ms on Raspberry Pi 5 |
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- **High Accuracy**: 97.84% detection accuracy across 12 common rice diseases |
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- **Custom Loss Function**: Combination of Focal Loss and Label Smoothing for better handling of class imbalance |
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- **Comprehensive Data Augmentation**: Robust augmentation pipeline to improve model generalization |
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- **Deployment-Ready**: Optimized for TFLite deployment on edge devices |
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## Model Architecture |
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LARS-MobileNetV4 builds upon the recently introduced MobileNetV4 architecture with several key optimizations: |
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1. **Universal Inverted Bottleneck (UIB)**: Merges features of Inverted Bottlenecks, ConvNext, and Feed Forward Networks to enhance flexibility in spatial and channel mixing |
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2. **Mobile Multi-Query Attention (MQA)**: An accelerator-optimized attention mechanism that reduces memory bandwidth bottlenecks |
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3. **Squeeze-and-Excitation Blocks**: Added to adaptively recalibrate channel-wise feature responses |
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4. **Efficient Channel Attention**: Captures cross-channel interactions with minimal computational overhead |
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5. **Neural Architecture Search (NAS)**: Tailored architecture for specific hardware |
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## Performance Comparison |
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| Model | Parameters (M) | Accuracy (%) | Model Size (MB) | Inference Time on CPU (ms) | Inference Time on Raspberry Pi 5 (ms) | |
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| --------------------- | -------------- | ------------ | --------------- | -------------------------- | ------------------------------------- | |
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| ResNet34 | 21.79 | 97.50 | 85.3 | 148.93 | 264.50 | |
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| MobileNet-V2 | 3.5 | 92.42 | 9.2 | 40.00 | 73.09 | |
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| MobileNet-V3 | 2.5 | 95.62 | 10.3 | N/A | N/A | |
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| MobileNet-V4 | 3.8 | 97.17 | 10.2 | 39.20 | 88.91 | |
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| **LARS-MobileNet-V4** | **3.8** | **97.84** | **10.2** | **39.20** | **88.91** | |
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## Training Strategies |
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Our implementation includes several optimization techniques: |
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| Model Variation | Train Accuracy (%) | Test Accuracy (%) | |
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| -------------------------------------------------------------------------------- | ------------------ | ----------------- | |
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| MobileNet-V4 Baseline | 99.93 | 97.17 | |
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| MobileNet-V4 (Augmentations) | 99.60 | 97.21 | |
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| MobileNet-V4 (FocalLabelSmoothingLoss) | 99.71 | 97.79 | |
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| MobileNet-V4 (Augmentations, FocalLabelSmoothingLoss, Squeeze-Excitation Blocks) | 99.68 | **97.84** | |
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### Custom Loss Function |
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We implement a combination of Focal Loss and Label Smoothing: |
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1. **Label Smoothing**: Redistributes confidence across classes |
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$$y_{smooth} = (1 - Ξ΅)y + Ξ΅/C$$ |
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where Ξ΅ is the smoothing factor and C is the total number of classes. |
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2. **Focal Loss**: Focuses on harder examples |
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$$L_{focal}(pt) = -Ξ±(1 - pt)^Ξ³ log(pt)$$ |
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where pt is the predicted probability for the true class. |
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3. **Combined Loss (FLS)**: |
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$$L_{FLS} = -Ξ±(1 - pt)^Ξ³ log(p_{smooth})$$ |
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## Requirements |
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``` |
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torch |
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torchvision |
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timm |
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numpy |
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pandas |
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Pillow |
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scikit-learn |
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tqdm |
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wandb |
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``` |
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### Data Preparation |
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Organize your data as follows: |
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``` |
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βββ train_images/ |
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β βββ disease_class_1/ |
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β β βββ image1.jpg |
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β β βββ image2.jpg |
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β β βββ ... |
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β βββ disease_class_2/ |
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β βββ ... |
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βββ test_images/ |
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βββ train.csv |
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``` |
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The train.csv file should contain: |
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- `image_id`: Filename of the image |
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- `label`: Disease class name |
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### Configuration |
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Key hyperparameters can be modified at the top of the script: |
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```python |
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LEARNING_RATE = 0.0001 |
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ARCHITECTURE = "MobileNetV4" |
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EPOCHS = 50 |
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BATCH_SIZE = 64 |
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OPTIMISER = "Adam" |
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LOSS_FUNCTION = "FocalLabelSmoothingComboLoss" |
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NUM_CLASSES = 13 # 12 disease classes + 1 normal class |
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PRETRAINED = True |
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``` |
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## Citation |
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If you use this code in your research, please cite our paper: |
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``` |
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@article{Nanda, T.R., Shukla, A., Srinivasa, T.R., Bhargava, J., Chauhan, S. (2025). |
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Advancing Real-Time Crop Disease Detection on Edge Computing Devices Using Lightweight Convolutional Neural Networks. |
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In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2025. |
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Lecture Notes in Networks and Systems, vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-032-00071-2_33 |
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} |
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``` |
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## Acknowledgements |
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- We use the Paddy Doctor dataset for training and evaluation [Petchiammal et al., 2022] |
