ML--Plant-Disease-Detection

An image classification deep learning model

To Start..

Requirments

Data Source

Our Data

Data Description

• Input: Image
• Output: Class
  • Apple___Apple_scab
  • Apple___Black_rot
  • Apple___Cedar_apple_rust
  • Apple___healthy
  • Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot
  • Corn_(maize)__Common_rust
  • Corn_(maize)___Northern_Leaf_Blight
  • Corn_(maize)___healthy
  • Grape___Black_rot
  • Grape___Esca_(Black_Measles)
  • Grape___Leaf_blight_(Isariopsis_Leaf_Spot)
  • Grape___healthy
  • Potato___Early_blight
  • Potato___Late_blight
  • Potato___healthy
  • Tomato___Bacterial_spot
  • Tomato___Early_blight
  • Tomato___Late_blight
  • Tomato___Leaf_Mold
  • Tomato___Septoria_leaf_spot
  • Tomato___Spider_mites Two-spotted_spider_mite
  • Tomato___Target_Spot
  • Tomato___Tomato_Yellow_Leaf_Curl_Virus
  • Tomato___Tomato_mosaic_virus
  • Tomato___healthy

Target

High Accuracy

Resource/Situational Constraints

• limited resources
• Long training time

Process followed

• Use free GPU supplied by google colab or kaggle.
• Use a small dataset with 25 classes related to only five types of plants.

ML Code

1. Splitting Data
   Take only five plants to work with. I've splitted train folder into train and val sets with val ratio 0.2 after shuffeling.
   
2. Exploring the Data
3. Data Preprocessing
   • Rescale
   • Resize
   • Data is already augmented
4. Pretrained Model Choosing
   • VGG16 (Accuracy = 84%)
   • MobileNet (Accuracy = 99.4%)
5. Testing