An Efficient Weed Detection using Adaboost and CNN Hybrid Model

The detection of weeds is an essential component of precision agriculture because it provides farmers with the tools necessary to efficiently manage and control weed infestation in agricultural areas. Manual work is laborious and error-prone in traditional weed-detecting methods. Computer vision and machine learning have shown promise in automating weed detection in recent years. This study introduces a hybrid model that combines AdaBoost and CNN to detect weeds. The hybrid model uses AdaBoost and CNN to boost weed detection accuracy. AdaBoost is used as a feature selector to identify input images with discriminative properties. CNNs, or convolutional neural networks, are popular deep learning models that can extract complex spatial features from images. This CNN receives the selected features. The CNN learns and improves features, enabling precise weed classification. A large dataset from Kaggle with crop field images of various weed species and backdrops is used to evaluate the hybrid model. The dataset is hand-annotated to provide ground truth labels for training and testing. The dataset trains the hybrid model, which is optimized for precision, recall, and F1 score. Experimental comparisons show that the proposed AdaBoost and CNN hybrid model outperforms individual models and other weed detection methods in accuracy and computing economy.