Name	Name	Last commit message	Last commit date
parent directory ..
exe_v2h	exe_v2h
exe_v2l	exe_v2l
images	images
src	src
gitattributes	gitattributes
readme.md	readme.md

Plant leaf disease classification

Application: Overview

The Plant leaf disease classification application allows to classify between 38 different plant leaf disease/healthy. The developed model is able to recognize 38 different types of plant disease out of 14 different plants. The label list is provided at plant_leaf_disease_class.txt

The application could be used to classify plant leaf whether healthy or not in agricultural sector

It has 4 modes of running.

Mode	RZ/V2L	RZ/V2H
MIPI Camera	`Supported`	`-`
USB Camera	`Supported`	`Supported`
Image	`Supported`	`Supported`
Video	`Supported`	`Supported`

Supported Product

RZ/V2L Evaluation Board Kit (RZ/V2L EVK)
RZ/V2H Evaluation Board Kit (RZ/V2H EVK)

Demo

Following is the demo for RZ/V2L EVK.

Application: Requirements

Hardware Requirements

For	Equipment	Details
RZ/V2L	RZ/V2L EVK	Evaluation Board Kit for RZ/V2L. Includes followings. MIPI Camera Module(Google Coral Camera) Used as a camera input source. MicroUSB to Serial Cable for serial communication.
	AC Adapter	USB Power Delivery adapter for the board power supply.
	MicroHDMI Cable	Used to connect the HDMI Monitor and the board. RZ/V2L EVK has microHDMI port.
RZ/V2H	RZ/V2H EVK	Evaluation Board Kit for RZ/V2H.
	AC Adapter	USB Power Delivery adapter for the board power supply. 100W is required.
	HDMI Cable	Used to connect the HDMI Monitor and the board. RZ/V2H EVK has HDMI port.
	USB Camera	Used as a camera input source.
Common	USB Cable Type-C	Connect AC adapter and the board.
	HDMI Monitor	Used to display the graphics of the board.
	microSD card	Used as the filesystem. Must have over 4GB capacity of blank space. Operating Environment: Transcend UHS-I microSD 300S 16GB
	Linux PC	Used to build application and setup microSD card. Operating Environment: Ubuntu 20.04
	SD card reader	Used for setting up microSD card.
	USB Hub	Used to connect USB Keyboard and USB Mouse to the board.
	USB Keyboard	Used to type strings on the terminal of board.
	USB Mouse	Used to operate the mouse on the screen of board.

Note: All external devices will be attached to the board and does not require any driver installation (Plug n Play Type)

Connect the hardware as shown below.

RZ/V2L EVK	RZ/V2H EVK

Note 1: When using the keyboard connected to RZ/V Evaluation Board, the keyboard layout and language are fixed to English.
Note 2: For RZ/V2H EVK, there are USB 2.0 and USB 3.0 ports.
USB camera needs to be connected to appropriate port based on its requirement.

Application: Build Stage

Note: User can skip to the next stage (deploy) if they do not want to build the application.
All pre-built binaries are provided.

Prerequisites

This section expects the user to have completed Step 5 of Getting Started Guide provided by Renesas.

After completion of the guide, the user is expected of following things.

AI SDK setup is done.
Following docker container is running on the host machine.

Board Docker container

RZ/V2L EVK rzv2l_ai_sdk_container

RZ/V2H EVK rzv2h_ai_sdk_container

Note: Docker environment is required for building the sample application.

Board	Docker container
RZ/V2L EVK	`rzv2l_ai_sdk_container`
RZ/V2H EVK	`rzv2h_ai_sdk_container`

Application File Generation

On your host machine, copy the repository from the GitHub to the desired location.
1. It is recommended to copy/clone the repository on the data folder, which is mounted on the Docker container.
```
cd <path_to_data_folder_on_host>/data
git clone https://github.com/renesas-rz/rzv_ai_sdk.git
```
Note: This command will download the whole repository, which include all other applications.
If you have already downloaded the repository of the same version, you may not need to run this command.
Run (or start) the docker container and open the bash terminal on the container.
E.g., for RZ/V2L, use the rzv2l_ai_sdk_container as the name of container created from rzv2l_ai_sdk_image docker image.

Note that all the build steps/commands listed below are executed on the docker container bash terminal.
Set your clone directory to the environment variable.
```
export PROJECT_PATH=/drp-ai_tvm/data/rzv_ai_sdk
```

Go to the application source code directory.

cd ${PROJECT_PATH}/Q07_plant_disease_classification/src

Create and move to the build directory.
```
mkdir -p build && cd build
```

Build the application by following the commands below.
For RZ/V2L

cmake -DCMAKE_TOOLCHAIN_FILE=./toolchain/runtime.cmake ..
make -j$(nproc)

For RZ/V2H

cmake -DCMAKE_TOOLCHAIN_FILE=./toolchain/runtime.cmake -DV2H=ON ..
make -j$(nproc)

The following application file would be generated in the ${PROJECT_PATH}//Q07_plant_disease_classification/src/build directory
- plant_leaf_disease_classify

Application: Deploy Stage

Prerequisites

This section expects the user to have completed Step 7-1 of Getting Started Guide provided by Renesas.

After completion of the guide, the user is expected of following things.

microSD card setup is done.

File Configuration

For the ease of deployment all the deployable file and folders are provided in following folders.

Board	`EXE_DIR`
RZ/V2L EVK	exe_v2l
RZ/V2H EVK	exe_v2h

Each folder contains following items.

File	Details
plant_dis_onnx	Model object files for deployment
plant_leaf_disease_class.txt	Label list for Object Detection
plant_leaf_disease_classify	application file
sampleimg.jpg	sample image for image mode

Instruction

Copy the following files to the /home/root/tvm directory of the rootfs (SD Card) for the board.

File Details

All files in EXE_DIR directory Including deploy.so file.

plant_leaf_disease_classify application file Generated the file according to Application File Generation
Check if libtvm_runtime.so exists under /usr/lib64 directory of the rootfs (SD card) on the board.

File	Details
All files in `EXE_DIR` directory	Including `deploy.so` file.
`plant_leaf_disease_classify` application file	Generated the file according to Application File Generation

Folder structure in the rootfs (SD Card) would look like:

|-- usr
|   `-- lib64
|       `-- libtvm_runtime.so
`-- home
    `-- root
        `-- tvm
            |-- plant_dis_onnx
            |   |-- deploy.json
            |   |-- deploy.params
            |   `-- deploy.so 
            |-- plant_leaf_disease_class.txt
            |-- plant_leaf_disease_classify
            |-- plantvid.mp4
            `-- sampleimg.jpg

Note: The directory name could be anything instead of tvm. If you copy the whole EXE_DIR folder on the board, you are not required to rename it tvm.

Application: Run Stage

Prerequisites

This section expects the user to have completed Step 7-3 of Getting Started Guide provided by Renesas.

After completion of the guide, the user is expected of following things.

The board setup is done.
The board is booted with microSD card, which contains the application file.

Instruction

On Board terminal, go to the tvm directory of the rootfs.
```
cd /home/root/tvm/
```

Run the application.

For Image Mode

./plant_leaf_disease_classify IMAGE sampleimg.jpg

For USB Camera Mode

./plant_leaf_disease_classify USB

For MIPI Camera Mode [RZ/V2L only]

./plant_leaf_disease_classify MIPI

For VIDEO Mode

./plant_leaf_disease_classify VIDEO plantvid.mp4

Select area for classification.

The user needs to draw the box for which specific area to classify.

After selecting the area (rectangle box drawn via mouse connected to board), press Enter key on keyboard connected to the board, or click "Done" button at the top-left corner for RZ/V2H, to trigger the inference and shows the classification result.
Following window shows up on HDMI screen.

RZ/V2L EVK RZ/V2H EVK

On application window, following information is displayed.
- Camera capture
- Classification result (class name, status and score.)
- Processing time taken for AI inference.
- Frames per Second
- Top 5 Classification Results (Based on the score)
To terminate the application, follow the termination method below.
- For RZ/V2L, application can be terminated by pressing Esc key on the USB keyboard connected to the board or alternatively, user can force close the application using CTRL+c on the board console.
- For RZ/V2H, switch the application window to the terminal by using Super(windows key)+Tab and press ENTER key on the terminal of the board.

Application: Configuration

AI Model

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 64, 256, 256]           1,792
       BatchNorm2d-2         [-1, 64, 256, 256]             128
              ReLU-3         [-1, 64, 256, 256]               0
            Conv2d-4        [-1, 128, 256, 256]          73,856
       BatchNorm2d-5        [-1, 128, 256, 256]             256
              ReLU-6        [-1, 128, 256, 256]               0
         MaxPool2d-7          [-1, 128, 64, 64]               0
            Conv2d-8          [-1, 128, 64, 64]         147,584
       BatchNorm2d-9          [-1, 128, 64, 64]             256
             ReLU-10          [-1, 128, 64, 64]               0
           Conv2d-11          [-1, 128, 64, 64]         147,584
      BatchNorm2d-12          [-1, 128, 64, 64]             256
             ReLU-13          [-1, 128, 64, 64]               0
           Conv2d-14          [-1, 256, 64, 64]         295,168
      BatchNorm2d-15          [-1, 256, 64, 64]             512
             ReLU-16          [-1, 256, 64, 64]               0
        MaxPool2d-17          [-1, 256, 16, 16]               0
           Conv2d-18          [-1, 512, 16, 16]       1,180,160
      BatchNorm2d-19          [-1, 512, 16, 16]           1,024
             ReLU-20          [-1, 512, 16, 16]               0
        MaxPool2d-21            [-1, 512, 4, 4]               0
           Conv2d-22            [-1, 512, 4, 4]       2,359,808
      BatchNorm2d-23            [-1, 512, 4, 4]           1,024
             ReLU-24            [-1, 512, 4, 4]               0
           Conv2d-25            [-1, 512, 4, 4]       2,359,808
      BatchNorm2d-26            [-1, 512, 4, 4]           1,024
             ReLU-27            [-1, 512, 4, 4]               0
        MaxPool2d-28            [-1, 512, 1, 1]               0
          Flatten-29                  [-1, 512]               0
           Linear-30                   [-1, 38]          19,494
================================================================
Total params: 6,589,734
Trainable params: 6,589,734
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.75
Forward/backward pass size (MB): 343.95
Params size (MB): 25.14
Estimated Total Size (MB): 369.83
----------------------------------------------------------------
None

Dataset

The class labels are mentioned in the plant_leaf_disease_class.txt This dataset consists of about 87K rgb images of healthy and diseased crop leaves which is categorized into 38 different classes. The total dataset is divided into 80/20 ratio of training and validation set preserving the directory structure. A new directory containing 33 test images is created later for prediction purpose.

Dataset Link

AI inference time

Board	AI inference time
RZ/V2L EVK	Approximately 70 ms
RZ/V2H EVK	Approximately 2 ms

Processing

Processing	RZ/V2L EVK	RZ/V2H EVK
Pre-processing	Processed by CPU	Processed by CPU
Inference	Processed by DRP-AI and CPU	Processed by DRP-AI and CPU
Post-processing	Processed by CPU	Processed by CPU

Image buffer size

Board	Camera capture buffer size	HDMI output buffer size
RZ/V2L EVK	VGA (640x480) in YUYV format	HD (1280x720) in BGRA format
RZ/V2H EVK	VGA (640x480) in YUYV format	FHD (1920x1080) in BGRA format

Reference

For RZ/V2H EVK, this application supports USB camera only with 640x480 resolution.
To use FHD, please use MIPI camera.
Please refer to following URL for how to change camera input to MIPI camera.
https://renesas-rz.github.io/rzv_ai_sdk/latest/about-applications.

License

Apache License 2.0
For third party OSS library, please see the source code file itself.

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Q07_plant_disease_classification

Q07_plant_disease_classification

readme.md

Plant leaf disease classification

Application: Overview

Supported Product

Demo

Application: Requirements

Hardware Requirements

Application: Build Stage

Prerequisites

Application File Generation

Application: Deploy Stage

Prerequisites

File Configuration

Instruction

Application: Run Stage

Prerequisites

Instruction

Application: Configuration

AI Model

Dataset

AI inference time

Processing

Image buffer size

Reference

License

Files

Q07_plant_disease_classification

Directory actions

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Q07_plant_disease_classification

Folders and files

parent directory

readme.md

Plant leaf disease classification

Application: Overview

Supported Product

Demo

Application: Requirements

Hardware Requirements

Application: Build Stage

Prerequisites

Application File Generation

Application: Deploy Stage

Prerequisites

File Configuration

Instruction

Application: Run Stage

Prerequisites

Instruction

Application: Configuration

AI Model

Dataset

AI inference time

Processing

Image buffer size

Reference

License