Plant Disease Detection

This projects consists of two parts.

• Cloud ML Sererver
• Android App

Project Overview

Overall System Architecture:

Android App

Android Screenshots:

Cloud Server

There are two different servers available. Either one of the server running needs to be running.

• Django based Server
• Flask based server

Flask Server

install flask via pip and all the dependencies.

pip install flask

Flask Server running model

Running flask model->

1. Start and run google instance.
2. Activate project env "source activate tf_cpu"
3. cd to Flask Server folder.
4. Run "python3 flask\ model.py"
5. Goto instance ip:5000 (e.g http://35.244.41.56:5000/)
6. Keep the tab open all the time.
7. New images are sent to firebase.
8. As new images are sent the page will auto reload and the prediction will be sent to firebase as output.

Running Environment

#Docker

Assuming an Ubuntu Docker image

$ docker run -it /bin/bash

Kaggle ->

Dropmark Links-> https://rashedoz.dropmark.com/592180

GCP Steps->

Cmd:

source activate tf_gpu
jupyter-notebook --no-browser --port=8123

(tf_gpu) yaan_uchiha29@r-gpu:~$

Tensorboard:

	tensorboard --logdir='logs/'

SSH:

ssh -i [PATH_TO_PRIVATE_KEY] [USERNAME]@[EXTERNAL_IP_ADDRESS]

//create key-pair
ssh-keygen -C yaan_uchiha29

Notebook:

Running Notebook: jupyter-notebook --no-browser --port=8123

Jupyter notebook different python executable solve: python3 -m ipykernel install --user

install Nvidia's CUDA software:

update apt-get

sudo apt-get update

work as root

sudo su

#!/bin/bash echo "Checking for CUDA and installing."

Check for CUDA and try to install.

if ! dpkg-query -W cuda; then curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb sudo apt-get update sudo apt-get install cuda-9-0 sudo nvidia-smi -pm 1

Test that your GPU is successfully installed:

check that GPU is working

Nvidia-smi

Install cudnn and tensorflow gpu using anaconda: conda create --name tf_gpu tensorflow-gpu

If cudnn not installed then:

Install your Deep Neural Network (cuDNN) binaries that you uploaded earlier (check your version): sudo dpkg -i libcudnn7_7.1.4.18-1+cuda9.0_amd64.deb //download cuDNN runtime library

Set sticky path defaults echo 'export CUDA_HOME=/usr/local/cuda' >> ~/.bashrc echo 'export PATH=$PATH:$CUDA_HOME/bin' >> ~/.bashrc echo 'export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc source ~/.bashrc

License