WEED CLASSIFICATION WITH CNN

This study leverages Convolutional Neural Networks (CNNs) to automate weed classification in agricultural fields,reducing reliance on manual identification and excessive herbicide use. A CNN-based model was trained on adiverse dataset encompassing varying lighting conditions, soil types, and plant growth stages to ensure robustness.Techniques such as data augmentation, transfer learning, and hyperparameter tuning optimized model performance,achieving superior accuracy compared to traditional image processing and machine learning approaches. The modeldemonstrated adaptability to new datasets with minimal retraining and computational efficiency, making it suitablefor real-time applications like autonomous sprayers and robotic weeders. Future work aims to enhance multi-classclassification of weed species and optimize deployment in resource-limited farming environments.