LARS-MobileNet-V4

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."

https://github.com/lars-uav/LARS-MobileNet-V4

Overview

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.

Key Features

• Optimized MobileNetV4 Architecture: Enhanced with Squeeze-and-Excitation (SE) blocks and Efficient Channel Attention (ECA) mechanisms
• Resource-Efficient Design: Significantly reduced model size (10.2MB) compared to ResNet34 (85.3MB)
• Real-time Performance: Average inference time of 39ms on CPU and 88.91ms on Raspberry Pi 5
• High Accuracy: 97.84% detection accuracy across 12 common rice diseases
• Custom Loss Function: Combination of Focal Loss and Label Smoothing for better handling of class imbalance
• Comprehensive Data Augmentation: Robust augmentation pipeline to improve model generalization
• Deployment-Ready: Optimized for TFLite deployment on edge devices

Model Architecture

LARS-MobileNetV4 builds upon the recently introduced MobileNetV4 architecture with several key optimizations:

1. Universal Inverted Bottleneck (UIB): Merges features of Inverted Bottlenecks, ConvNext, and Feed Forward Networks to enhance flexibility in spatial and channel mixing
2. Mobile Multi-Query Attention (MQA): An accelerator-optimized attention mechanism that reduces memory bandwidth bottlenecks
3. Squeeze-and-Excitation Blocks: Added to adaptively recalibrate channel-wise feature responses
4. Efficient Channel Attention: Captures cross-channel interactions with minimal computational overhead
5. Neural Architecture Search (NAS): Tailored architecture for specific hardware

Performance Comparison

Model	Parameters (M)	Accuracy (%)	Model Size (MB)	Inference Time on CPU (ms)	Inference Time on Raspberry Pi 5 (ms)
ResNet34	21.79	97.50	85.3	148.93	264.50
MobileNet-V2	3.5	92.42	9.2	40.00	73.09
MobileNet-V3	2.5	95.62	10.3	N/A	N/A
MobileNet-V4	3.8	97.17	10.2	39.20	88.91
LARS-MobileNet-V4	3.8	97.84	10.2	39.20	88.91

Training Strategies

Our implementation includes several optimization techniques:

Model Variation	Train Accuracy (%)	Test Accuracy (%)
MobileNet-V4 Baseline	99.93	97.17
MobileNet-V4 (Augmentations)	99.60	97.21
MobileNet-V4 (FocalLabelSmoothingLoss)	99.71	97.79
MobileNet-V4 (Augmentations, FocalLabelSmoothingLoss, Squeeze-Excitation Blocks)	99.68	97.84

Custom Loss Function

We implement a combination of Focal Loss and Label Smoothing:

1. Label Smoothing: Redistributes confidence across classes

   $$y_{smooth} = (1 - ε)y + ε/C$$

   where ε is the smoothing factor and C is the total number of classes.

2. Focal Loss: Focuses on harder examples

   $$L_{focal}(pt) = -α(1 - pt)^γ log(pt)$$

   where pt is the predicted probability for the true class.

3. Combined Loss (FLS):

   $$L_{FLS} = -α(1 - pt)^γ log(p_{smooth})$$

Requirements

torch
torchvision
timm
numpy
pandas
Pillow
scikit-learn
tqdm
wandb

Data Preparation

Organize your data as follows:

├── train_images/
│   ├── disease_class_1/
│   │   ├── image1.jpg
│   │   ├── image2.jpg
│   │   └── ...
│   ├── disease_class_2/
│   └── ...
├── test_images/
└── train.csv

The train.csv file should contain:

• image_id: Filename of the image
• label: Disease class name

Configuration

Key hyperparameters can be modified at the top of the script:

LEARNING_RATE = 0.0001
ARCHITECTURE = "MobileNetV4"
EPOCHS = 50
BATCH_SIZE = 64
OPTIMISER = "Adam"
LOSS_FUNCTION = "FocalLabelSmoothingComboLoss"
NUM_CLASSES = 13  # 12 disease classes + 1 normal class
PRETRAINED = True

Citation

If you use this code in your research, please cite our paper:

@article{Nanda, T.R., Shukla, A., Srinivasa, T.R., Bhargava, J., Chauhan, S. (2025).
Advancing Real-Time Crop Disease Detection on Edge Computing Devices Using Lightweight Convolutional Neural Networks.
In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2025.
Lecture Notes in Networks and Systems, vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-032-00071-2_33
}

Acknowledgements

• We use the Paddy Doctor dataset for training and evaluation [Petchiammal et al., 2022]