Comparative Evaluation of CNN Models for Precision Agriculture in Deep Learning-Based Weed Detection - Mohanappriya, K.; Vennila, C.; Joshua, R.Roshan; Kumar, A.T.Barani Vijaya (Journal of Neonatal Surgery, 2025)
Precision agriculture depends on automated weed detection to increase crop output and decrease pesticide use. Deep learning-based methods are useful for identifying weeds in the field. This study compares three sets of convolutional neural networks—Densenet, Resnet, and VGGNet—for the purpose of weed detection. A real-time data set that was recorded by a camera in the field is used to perform these models based on accuracy, precision, and recall. All preprocessing methods for performance metrics are completed. According to these results, Densenet outperforms the other two models in terms of accuracy. These observations aid in choosing the best model for agricultural applications in real time