Abstract
Identifying weed species plays a key role in agricultural management, helping to target eradication and precision farming. In this study, ResNet, VggNet and DenseNet were used to investigate the robust models capable of accurately discriminating between different weed species. The dataset used in this study consists of high resolution images of different weed species under different environmental conditions. Experimental results demonstrated the effectiveness of the proposed deep learning models in accurately identifying multiple weed species simultaneously. Evaluation metrics including accuracy, precision, recall and confusion matrix demonstrate the effectiveness of the models in discriminating between weed species. Experimental results indicate that among the convolutional neural network architectures used in this study, VggNet has the highest CA at 99.21%. This study discusses the implications of these results in the context of agricultural applications, highlighting the potential for scalable and efficient weed species identification and management through deep learning-based classification systems.
Published Version
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