Crop Disease Classification System Using Deep Learning (ResNet Model)

Developed a deep learning-based image classification model using Kaggle notebooks to detect crop leaf conditions. The system is capable of classifying whether a crop leaf is healthy or diseased, and if diseased, it predicts the specific type of disease.

Project details:

Built and trained a deep learning model using ResNet

Used a labeled dataset of crop leaf images from Kaggle

Performed data preprocessing including resizing, normalization, and augmentation

Implemented multi-class classification for different plant diseases

Evaluated model performance using accuracy, loss, and validation metrics

Improved model generalization to reduce overfitting and enhance prediction reliability

Tools & Technologies:

• Python

• Kaggle Notebooks

• TensorFlow / Keras

• Deep Learning

• Convolutional Neural Networks (CNN)

• Image Classification