Plant AI: Recognition of Plant Diseases by Leaf Image Classification using Python n Machine Learning
Welcome to our project on plant disease detection using deep learning!
We aim to develop a model that identifies 38 diseases across 14 plant species using leaf image classification with deep convolutional networks. This approach helps improve crop yield and reduce pesticide use, making it feasible for small and medium-sized farms.
Steps Covered:
1. Data Preparation: Using the "New Plant Diseases Dataset" from Kaggle.
2. Dataset and Dataloader: Transform images, create datasets, and load them as PyTorch tensors.
3. Model Building: Develop CNN models from scratch and with transfer learning (e.g., ResNet34).
4. GPU Processing: Move data to GPU for faster processing.
5. Training and Evaluation: Train the model with Adam optimizer and evaluate its performance.
6. Prediction: Test the model on unseen data.
7. Saving the Model: Save model weights for future use.
We achieved a test accuracy of 98.42%! Let's dive in and explore how we did it.
Dataset:
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