Table of Contents

1. Dataset Content
2. Business Requirements
3. Hypothesis and validation
4. Rationale for the model
5. Trial and error
6. Implementation of the Business Requirements
7. ML Business case
8. Dashboard design
9. CRISP DM Process
10. Bugs
11. Deployment
12. Technologies used
13. Credits

App deployed hete ml5-mildew-detection_herokuapp

Dataset Content

• Dataset consist of 4208 photos of cherry leaves both healthy and infected with fungus. Disease that affect wide range of plants however client interested in Cherry Trees mostly. All images taken from Farmy & Foody. Customer is concerned about supplying compromised quality product.
• Dataset located at Kaggle
• This project based on fictious story to apply machine learning algorithms to solve problem which later on could be used in real world scenario.

Business Requirements

Our customer Farmy & Foody contacted us to resolve non trivial issue in agricultural sector. And we are trying to create Machine Learning system that can help. Core problem is cherry leaves that are infected by fungus (Powdery Mildew). At the moment this process takes around 30 minutes per tree. Infected trees treated with fungicide. Due to size of customers (thousands trees all over the country) this process could be time consuming. In order to increase efficiency one of the solutions is Machine Learning model. Our system should make a decision based on an image of cherry leaves and give answer whether it is "Healthy" or "Infected"

• Customer interested in app that can:

1. Visually differentiate healthy leaf from infected by powdery mildew.
2. Predicting base on image, if leaf infected or healthy.
3. Receiving prediction report of leaves.

Hypothesis and validation

1. Hypothesis: Leaves that are infected have marks compare to heathy ones

   • Validation Understand of how Powdery Mildew look like.

2. Hypothesis: Which model better to choose

   • Validation depends on the problem I solve

3. Hypothesis: When it perfomed better

   • Validation colors, filters algorithms.

Hypothesis 1

Leaves that are infected have marks compare to heathy ones

If leaf is infected by Powdery Mildev we would see some classical marks as: pale yellow leaf spots, round lesions on either side which will develope to white powdery spots on the leaves. This understanding we should provide to our system. But how? Firstly, we need transform, split and basically prepare our data for learning for best learning outcome.

Once we know we should prepare our dataset by normalization before training our model. In order to normalize our images we need to calculate meand and standart deviation for our images. Mean is dividing the sum of pixel values by the total number of pixel in dataset. Standart deviation basically tell us how bright or dark image is. Brighter image is more "busy" it is, if standart deviation is lov that means brightness of the picture is similar across picture. We do that with some mathematical calculation.

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We can spot difference between healthy ad infected leaf based on this image montage.

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Lookig at avarage and variability images we can spot more white spotes and lines on the infected leaves

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On the other hand no visual differenceson avarage infected and healthy leaves here

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System is capable of detecting differences in our leaves dataset so our learning outcome would be high. This is important step as we making sure that our model can understand patterns and features so we can make predictions for new data but with same problem.

Hypothesis 2

Change color to grey

hypothesis 3

if we put images upside down

Rationale to map the business requirements to the Data Visualizations and ML tasks

The model has 1 input layer, 3 hidden layers (2 ConvLayer, 1 FullyConnected), 1 output layer.

CRISP-DM Method

CRISP-DM methodology widely used in data mining and AI development. Method provides structured approach to planning project. There are six phases: 1. Business Understanding: here objectives and requirements are defined. Understanding of the problem and setting goals that the project should achieve. 2. Data Understanding: data collected and explored so we have more understanding of data. 3. Data Preparation: Data is cleaned , transformed and prepared for analysis and modeling 4. Modeling: different rechnique applied to the data and results are evaluated for efectiveness for meeting our goals 5. Evaluation: model results are evaluated if they met project objectives. Assessing accuracy, reliability, validity and if model suitable for deployment. 6. Deployment: model deployed and running. Results monitored to ensure that the model continues to meet project objectives

Model

Creating model and settind hyperparameters with hidden layers been achieved with trial and error way. This structure appear in my case best one according to my goals

• Model should predict on new dataset if leaf infected or not with 97% accuracy.

• Model should save company time, resources and money as this process is manual at the moment.

• Model should use as less computational power as possible while still providing correct prediction

Which hyperparameters I choose

• layers
• number of neurons
• kernel size
• activation function (not sigmoid)
• output
• dropout

layers

model creation

Business Requirement 1: Data Visualization

Visually differentiate healthy leaf from infected by powdery mildew

User Story

• As a User I want interactive, easy to navigate and clear dashboard so I can understand data.
• As a User I want to display avarage and variability image so I can differentiate healthy and infected leaves
• As a User I want to display difference between average healthy and infected leaf, so I can visually diffferentiate them
• As a User I want to display images for healthy and infected leaves so I can visually differentiate them

All User stories been implemented in development Dashboard:

• Easy navigating side bar
• Displaying difference of avarage healthy and infected leaves.
• Displaying avarage variability of the healthy and infected leaves.
• Image montage of the leaves

Business Requirement 2: Classification

Predicting base on image, if leaf infected or healthy.

User Story

• As a user I want model that oredicts with 97% of accuracy if leaf is healthy or infected.

User story is implemented in Dashboard:

• User can upload own set of images with the help of created widget to receive prediction. (Max size of the file is 200MB)

Business Requirement 3: Report

Receiving prediction report of leaves.

User Story

• As a User I want to receive report of the predictions on new data.

User story in implemented in Dashboard:

• Every new dataset of images new report would be generated with prediction.

ML Business Case

• Machine Learning model should be able to predict on given dataset of cherry leves if they are infected or healthy. Based on this information we consider this problem as supervised model, with 2-class, single-label, classification
• Outcome of the model to provide better and faster way of predicting whether leaf is healthy of infected
• Model accuracy of 97% or above
• The model output is defined as a flag, indicating if the leaf contains any feature that can show that the tree is infected. Owner of the plantation will take a picture of some leaves of the tree and upload them to the App.
• Heuritics: Current method of detection is labour based. Every tree takes around 30 minutes to inspect and taking samples. This way of inspection have high human error and time consuming.
• Dataset provided by Farmy & Foody tha contains 4208 images of cherry leaves. Could be download at Kaggle

Dashboard Design (Streamlit App User Interface)

Page 1: Quick Project Summary

• Quick project summary
  • General information
    • Powdery Mildew is a fungal disease that affects a wide range of plants. Powdery mildew diseases are caused by many different species of ascomycete fungi. Infected plants display white powdery spots on the leaves and stems. The lower leaves are the most affected
    • Typical symptoms are : white patches of fungal growth develop on the lower surface of the leaf, leaf edges curl upwards, exposing the white, powdery fungal growth, purple to reddish blotches may also develop on leaves
  • Project Dataset
    • Provided by customer Farmy & Foody which contains 4208 photos of cherry leaves. Include both healthy and infected
  • Business requirements
    • Customer interested to visually differentiate between healthy and infected leaf
    • Customer interested if peaf is infected by Powdery Mildew
    • Customer interested in prediction report of the dataset

Page 2: leaves Visualizer

It will answer business requirement #1

• Checkbox 1 - Difference between average and variability image
• Checkbox 2 - Differences between average parasitised and average uninfected leaves
• Checkbox 3 - Image Montage

Page 3: Powdery mildew Detector

Business requirements #2

• The client is interested in predicting if a cherry tree is healthy or contains powdery mildew. Business requirements #3
• The client wants to receive report of the prediction.
• Download a set of infected or healthy leaves images from Kaggle
• User Interface wit ha file iploader widget. The user can upload multiple cherry leaves images. It will display the image, a barplot of the visual representation of the prediction and the prediction statement, indicating if the leaf is infected or not with powdery mildew and the probability associated with this statement.
• Report that contain table with the image name and prediction results.
• Download button to download the report.

Page 4: Project Hypothesis and Validation

• Bloack for each project hypothesis, describe the conclusion and how you validated.

Page 5: ML Performance Metrics

• Label Frequencies for Train, Validation and Test Sets
• Model History - Accuracy and Losses
• Model evaluation resul

Technologies used

• Heroku To deploy this project
• Jupiter Notebook
• Gitpod
• Kaggle
• Python
• Numpy
• Pandas
• Plotly
• Tensorflow
• Matplotlib

Deployment

1. Create runtime.txt Python version that supported by Heroku-22
2. Create requirements.txt file that lists all dependencies that program needs to run
3. Log In to your Heroku Account
4. Choose "Create New App"
5. Add Python buildpack in settings tab
6. Choose Github deployment method and connect project repository
7. Choose branch you want to deploy and click Deploy Branch
8. Select Automatic Deploys or Manual Deploy
9. Once deployed you will see link where you can access your app.

Forking

• Find reposirory you are interested in.
• Find button "Fork" near top right corner and click on it
• Choose where to fork it
• You successfully forked a repo

Cloning repo

• Find reposirory you are interested in.
• Than find button "Clone" and click on it
• Choose "Local" and find option that suits you: HTTPS, Download ZIP, Open with GitHub Desktop
• If you choose HTTPS link than copy a link
• Open you terminal with preinstalled Bash
• Decide where you want store new repo
• Type git clone https://github.com/BogdanFSD/ML5-Mildew-Detection and hit Enter

Credits

• Leaves dataset was downloaded from Kaggle and created by Code Institute

• Fungus description taken from RHS and [Wikipedia](https://en.wikipedia.org/wiki/Powdery_mildew#:~:text=Powdery%20mildew%20is%20one%20of,ground%20part%20of%20the%20plant. -)

• CRISP-DM steps taken from Think Insights

• Template for this project belongs to CodeInstitute

• Jupiter notebooks and pages for Dashboard are from CodeInstitute Walkthrough

Acknowledgements

I want to say Thank you to Code Institue and my mentor Rohit Sharma