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Plant disease detection using CNNs and GANs as an augmentative approach

机译:使用CNN和GAN作为增强方法进行植物病害检测

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Almost 40% of the world's crop yield is lost to diseases and pest infestations. According to a 2012 survey, Maharashtra has the highest rate of farmer suicides and one of the major reasons for this is the failure of crops. This paper presents an image-based classification system for identification of plant diseases. Since existing datasets have diluted focus across several countries and there are none that pertain to India specifically, there is a need for establishing a local dataset to be of use to Indian farmers. It uses Generative Adversarial Networks (GANs) to augment the limited number of local images available. The classification is done by a Convolutional Neural Network (CNN) model deployed in a smart phone app.
机译:由于疾病和病虫害的侵害,全球农作物的产量损失了近40%。根据2012年的一项调查,马哈拉施特拉邦农民自杀率最高,主要原因之一是农作物歉收。本文提出了一种基于图像的植物病害识别系统。由于现有数据集在多个国家/地区的关注集中,并且没有专门针对印度的数据集,因此有必要建立一个可供印度农民使用的本地数据集。它使用生成对抗网络(GAN)来增加有限数量的可用本地图像。通过部署在智能手机应用程序中的卷积神经网络(CNN)模型进行分类。

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