Crop diseases pose a significant threat to food security in Pakistan. This project aims to develop an AI-powered crop disease detection system that is accurate, efficient, and accessible to farmers. The system will be trained on a diverse dataset of labeled leaf images, enabling it to identify and classify diseases with high accuracy.

Special thanks to Mr. Samyan Qayyum Wahla for guidance and support, and the Department of Computer Science at the University of Engineering and Technology, Lahore.

Crop diseases lead to substantial losses in the Pakistani agricultural sector. This project introduces an AI-powered solution for timely and accurate disease detection.

Traditional methods of disease detection are time-consuming and often involve expensive laboratory testing. The shortage of trained plant pathologists further hinders timely and accurate disease detection.

The AI-powered system aims to provide quick and accurate diagnoses of crop diseases, even in remote areas. Accessible via smartphones, the system assists farmers in identifying diseases early.

The system uses deep learning with a Convolutional Neural Network (CNN). The dataset is sourced from Kaggle and undergoes pre-processing, including resizing, normalization, and augmentation. The trained model is evaluated on a test set to assess its performance.

The dataset includes images of various crop species and disease types, ensuring representativeness. Link to dataset

Images undergo resizing, normalization, and augmentation to enhance the dataset's diversity and prepare it for training.

A CNN is employed for training, using the Keras Sequential API.

The trained model is evaluated on a test set using metrics such as accuracy, precision, and recall.

The project anticipates the development of an accurate, efficient, and user-friendly crop disease detection system accessible to farmers across Pakistan.

The system consists of data input, pre-processing, CNN, user interface, and output components. It aims for a seamless flow of data and operations.

The system shows promising results, demonstrating high accuracy in identifying and classifying crop diseases. User interface design for smartphones is well-received.

The AI-powered Crop Disease Detection System holds significant potential in improving crop management practices in Pakistan. Challenges include image quality dependency and dataset bias. Continued efforts are needed for practical implementation.