MeerAli1472/Crop-Disease-Detection-in-Uganda

This project focuses on diagnosing diseases in cassava plants, a vital staple crop in Uganda, using images from a Kaggle dataset. The goal is to classify plants as healthy or affected by one of four diseases. Key features include data preprocessing (normalization, resizing, and balancing classes), CNN-based model training, and addressing challenges like overfitting. This solution aims to help farmers quickly identify crop issues and improve yield sustainability. **In the 022-explore-dataset we will find** - Our data needs to be loaded and processed - We can normalize our data to help our models - If we have unbalanced classes, our model might favor one class over another - We can correct this by undersampling, throwing away data in the larger classes - **In the 023-multiclass-classification:** - Once more we preprocessed our data to make it ready for deep learning. - We built a CNN with 3 convolutional and max pooling layers, followed by flattening and two dense layers. - We used nn.Sequential to easily build our model's architecture by defining the order of the layers. - Training the model - Testing the model