Plant-disease-detection-using-CNN

• A 5 layer CNN model to detect plant disease using leaf images.

Dataset

• The model is trained on Plant Village Dataset.
• The dataset has 61,486 images of total 39 categories of 16 variety plants and backgrounds.
• The dataset has been augmented using different techniques like image flipping, Gamma correction, noise injection, PCA color augmentation, rotation, and Scaling.

Model

• The model consisted of 9 Convolutional layers followed by ReLU, Batch Normalization and Max Pooling.

  

• 80% of dataset has been used for training, 10% for validation and 10% for testing.

• The model has been trained for 10 epochs.

  

• The accuracy of the model on training set is 93.07%, validation set is 91.53% and test is 90.94%.

• References:

• Project

• Dataset