Plant leaf disease identification
Image classification on high-performance MCU. MobileNet 0.25 model from STM32 model zoo.
Approach
The solution proposes a model trained on a public dataset providing very good accuracy while running on a STM32
The model can be easily deployed on the STM32H747 discovery kit with the STM32 model zoo Python scripts
The use case presented is based on a plant leaf dataset to identify diseases
Sensor
Data
Data format
39 different classes of plant leaf and background images
RGB color images
Results
Model Fast-downsampling MobileNet 0.25
Input size: 224x224x3
Memory footprint:
137 KB Flash for weights
152 KBRAM for activations
Accuracy:
Float model: 99.82%
Quantized model: 99.62%
Performance on STM32H747 (High-perf) @ 400 MHz
Inference time: 63.2 ms
Frame rate: 16 fps
Confusion matrix
Resources
Model repository ST Edge AI Model Zoo
A collection of reference AI models optimized to run on ST devices with associated deployment scripts. The model zoo is a valuable resource to add edge AI capabilities to embedded applications.
A free STM32Cube expansion package, X-CUBE-AI allows developers to convert pretrained AI algorithms automatically, such as neural network and machine learning models, into optimized C code for STM32.
Compatible with STM32H7 series
The STM32 family of 32-bit microcontrollers based on the Arm Cortex®-M processor is designed to offer new degrees of freedom to MCU users. It offers products combining very high performance, real-time capabilities, digital signal processing, low-power / low-voltage operation, and connectivity, while maintaining full integration and ease of development.
