Dill Plant Disease Detection

roject Overview: Developed a CNN-based model to detect diseases in dill plants with high accuracy. The system helps farmers identify plant diseases early, allowing for timely intervention and reducing crop loss.

Key Achievements: Achieved over 90% accuracy in disease detection through extensive training and validation on a large dataset. The client praised the model's effectiveness in real-world applications, leading to its adoption in agricultural practices.