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@article{170346,
        author = {K. Aryasri Reddy and J. Mahathi and T. Surya Sangeeth Chandra and Ch. Joshi Paul and Dr. G. Aparna},
        title = {Automated Weed Recognition In Sesame Crop Fields Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {314-320},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170346},
        abstract = {Agriculture is one of the origins of human nourishment in this world. Nowadays due to growing population we need the greater productive capability of the agriculture to meet the demands. So, to increase the crop yield, we started using herbicides. By doing so we got success in increasing the productivity but we have forgotten the damage done to the environment. So, the idea is to reduce the usage of herbicides by spraying them only in the areas where weed is present. The goal is to detect weeds in agricultural fields. A deep learning system is used in this project to identify weeds that are growing between crops. The primary characteristics of photos taken in agriculture are analyzed using the DL approach, CNN. This dataset is used to recognize weeds and crops to distinguish between weed and crop, a CNN uses rectified linear units (RELU) on a fully connected surface. Crop identification is done using features from a deep learning network.},
        keywords = {Agriculture productivity, Crop yield enhancement, weed detection technology, Convolutional Neural Network (CNN) in farming, Rectified Linear Unit (ReLU), Agricultural image analysis, Deep learning-based weed control.},
        month = {December},
        }