Project Purpose
Build a robust image classifier using Convolutional Neural Networks (CNNs) to efficiently differentiate between plant seedlings and weeds, improving crop yield management.
Goals
- Develop a CNN model capable of accurately classifying plant seedlings from images.
- Minimize human involvement in seedling identification for faster and more efficient agricultural practices.
- Improve crop yields by enabling targeted weed control and resource allocation.
Challenges
- Image variability: Ensuring the model can handle variations in lighting, image quality, and plant growth stages.
- Class imbalance: There might be more images of certain plant species compared to others.
- Overfitting: Preventing the model from memorizing training data and performing poorly on unseen images.
Achievements
- Developed a CNN model with over 95% accuracy in classifying plant seedlings from images.
- Implemented data augmentation techniques to address image variability and class imbalance.
- Designed a scalable model to handle large datasets with diverse plant species and environmental conditions.
