A Systematic Literature Survey on Generative Adversarial Network Based Crop Disease Identification

Aruna Mittal, Hridesh Gupta
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Abstract

"However, a deep learning network requires a large amount of data, and because certain plant lesion data is difficult to acquire and has a similar structure, deep learning has lately showed potential in the identification of plant lesions.", The data set has to be increased by generating full plant lesion leaf pictures. To address this issue, this article offers a survey on technique for generating full and rare picture of plant lesion leaf that may be enhance the accuracy of classification network. Some of the benefits of our research in this article are a systematic survey on GAN based plant disease identification where many authors gave the theory and practical implementation on that. My approach has been shown to successfully extend plant lesion research and improve the classification network’s identification accuracy in the future.
基于生成对抗网络的作物病害识别系统文献综述
“然而,深度学习网络需要大量的数据,并且由于某些植物病变数据难以获取且具有相似的结构,因此深度学习最近在植物病变识别方面显示出潜力”,必须通过生成完整的植物病变叶片图片来增加数据集。针对这一问题,本文对植物病变叶片全貌和罕见图像的生成技术进行了综述,以期提高分类网络的准确性。本文研究的一些好处是对基于氮化镓的植物病害鉴定进行了系统的调查,许多作者给出了理论和实际实施。我的方法已被证明可以成功地扩展植物病变研究,并在未来提高分类网络的识别精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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