Plant leaf Disease detection Project Source Code
The Source Code is Downloadable immediately after the successful payment
In this project, I will discuss how to create a convolutional neural network that will predict whether a plant is suffering from a disease or not.
Different layers and other hyperparameters are used for building, training, validating, and testing the CNN classification model.
TensorFlow and Keras are used to implement this project.
Video Demonstration – Model Building:
Deployment – Streamlit Application
- There is a need to increase food production by an estimated of 70% by 2050 to feed an expected population size that is predicted to be over 9 billion people.
- Currently, infectious diseases reduce the potential yield by an average of 40%.
- Many farmers in the developing world experience yield losses as high as 100%.
Steps to Implement the Plant Leaf Disease Prediction Project
- Mount the google drive on Google Collab Notebook and import the data set
- Import the required libraries
- Visualizing the images and Resize images
- Convert the images into a NumPy array and normalize them.
- Visualize the class count and Check for class imbalance
- Splitting the dataset into the train, validate, and test sets
- Performing one-hot encoding on the target variable
- Creating the model architecture, compiling the model, and then fitting it using the training data
- Plot the accuracy and loss against each epoch
- Make predictions on testing data
- Visualizing the original and predicted labels for the test images
- Deploy the project using Streamlit
Steps to Execute the Project
To generate Model
- Extract the contents of the zip folder.
Create a Projects folder in your Google Drive. Within that create a folder named Plant-Leaf-Disease-Prediction
Upload Plant_Leaf_Disease_Prediction.ipynb and Dataset folder into Plant-Leaf-Disease-Prediction on your Google account
Open Plant_Leaf_Disease_Prediction.ipynb in Google Colab.
Run Plant_Leaf_Disease_Prediction.ipynb to generate the model. The Model plant_disease_model.h5 will be saved in Model Folder on your Google account.
To Run Streamlit Application.
Download the plant_disease_model.h5 from your google account.
Place the plant_disease_model.h5 and main_app.py into a folder in your machine.
Install Anaconda Python Package
Open the anaconda prompt in the current working directory.
The following commands to generate the requirements.txt file
pip install pipreqs
Run the following command to install the required libraries.
pip install -r requirements.txt
Finally, run the following command to run the Streamlit application.
streamlit run main_app.py
Setup and modification are paid services based on requirements.
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