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WeedGan: a novel generative adversarial network for cotton weed identification

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Abstract

Recently, precision weed management has emerged as a promising solution for reducing the use of herbicides which is hazardous to crops and human health. Thus, accurate identification of weed in the early stage is the urge of current agricultural practices. Despite recent progress, developing an efficient weed identification system for real field scenarios is a serious challenge. To overcome this, a number of deep learning-based methods have been introduced in the literature. However, these methods require large volume of annotated data images which is rarely available. To address this gap, in this paper, a novel WeedGan method has been introduced for generating realistic synthetic images. The proposed WeedGan adopted concept of federated learning to reduce the computational load by introducing two discriminators. Additionally, a new loss function is defined for the efficient training of the generator. Extensive experiments have been performed to validate the performance of the WeedGan. First, the quality of images generated by the WeedGan was validated on cotton weed dataset in terms of FID score, and discriminator accuracy. The results are compared against four other state-of-the-art GAN models namely, DC-GAN, W-GAN, Info-Gan, and VIT-GAN. Further, classifiers performance of the generated dataset was evaluated using seven state-of-the-art transfer learning-based methods on the original, basic augmented, and WeedGan augmented datasets. The experimental results demonstrate that the proposed WeedGan has outperformed all the considered methods by achieving FID score of 282.76. Moreover, the classification performance of WeedGan augmented dataset was recorded highest with 97.82% on testing and 99.87% training accuracy with DenseNet121.

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Data availability

The data that support the findings of this study are openly available in at, https://www.kaggle.com/datasets/yuzhenlu/cottonweedid15

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  1. Malaviya National Institute of Technology (MNIT), Jaipur, Jaipur, India

    Vivek Sharma, Ashish Kumar Tripathi, Abhishek Parmar, Ashutosh Soni & Rahul Amarwal

  2. Jaypee Institute of Information Technology, Noida, India

    Himanshu Mittal

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Sharma, V., Tripathi, A.K., Mittal, H. et al. WeedGan: a novel generative adversarial network for cotton weed identification. Vis Comput 39, 6503–6519 (2023). https://doi.org/10.1007/s00371-022-02742-5

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