This document summarizes a tomato disease detection project that uses a random forest classifier algorithm. The project uses a dataset of 7,000 flattened tomato leaf images categorized as healthy, late blight, mold, or septoria. Each image is represented as a row of 784 pixel values. The random forest model outputs a prediction of the leaf condition. The model achieved 75% accuracy in distinguishing the four categories. Some problems encountered were faulty images and downscaling the original images. Further improvements could include adding more images and diseases and optimizing the image preprocessing.

1. TomatoGenius: RFC
A tomato disease detector by Mihit Puvvula

2. Motivation
This project was made as a continuation of a series of TomatoGenius
Projects. I add knowledge of key concepts related to Random Forest
to a belt with different AI algorithms.

3. Contents
• Dataset
• Input and Output
• Algorithm
• How did my AI service perform?
• Demo
• Problems Encountered
• Further Improvements
• Q and A

4. Dataset
• A CSV File containing 7000 flattened images
• Has 4 categories: Healthy, Late Blight, Mold, and Septoria Leaf
Spot
• Each flattened row contains a grayscale value of each pixel of the
28x28 image making 784 elements per row.

5. Input and Output
Input
• A row of 784 csv’s representing each pixel of my image
Output
• 4 labels describing the tomato leaf’s condition:
Healthy, Late Blight, Septoria Virus, Curl Virus

6. Algorithm
The Random Forest Classifier
Algorithm was used to generate
a training model.
Decision
Tree 1
Decision
Tree 2
Decision
Tree 3
Prediction: Late Blight
A decision tree is an entity that decides
which output to pass on based on rules
generated by the trained model.
Late Blight: 80%
Septoria: 15%
Unknown: 5%
Late Blight: Dark
Brown Patch
Healthy: Green with
no Spots
Septoria: Dark
Brown Spots
Mold: Yellow Fading
to Brown
Late Blight: Distinct
Brown Patch

7. Demo
Let’s take a look at the Python Integrated
Demo!

8. How did my AI Service Perform?
• Had an accuracy of 75% percent
• Very good for an AI with 4 different categories
Labels Healthy Late Blight Mold Septoria
Healthy 249 12 2 14
Late Blight 14 204 8 59
Mold 7 26 64 49
Septoria 24 40 23 247
TrueValues
Predicted

9. Faulty Data
Septoria MoldLate Blight
Try to categorize these three images:
Septoria: Has brown spots that evolve into
patches of dark brown with occasional spots
of yellow
Mold: Has spots of the leaf rotting yellow
with faint brown patches
Late Blight: The Coronavirus of crops. Leaves
rot into chunks of brown.
Can you classify these images?

10. Problems Encountered
• Faulty Images in some categories
• Downscaling to from 200x200 to 28x28

11. Further Improvements
• As always, I can add more pictures to provide more info
• Add more diseases
• Add certain presets like certain color combinations to get an even
more accurate prediction
• Instead of downscaling to 28x28, I can downscale by a lower factor
to 100x100