利用CNN进行图尔西叶病检测

Sheetal Patil, S. Patil, Akhil Bhall, Amay Rajvaidya, Himanshu Sehrawat, Avinash M. Pawar, Drishti Agarwal
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引用次数: 0

摘要

自covid - 19大流行开始以来,已经生产了各种各样的药物,目前正在用于治疗该疾病。除了所有化学药物外,Tulsi是一种草药疗法,对治疗这种疾病特别有效。几千年来,Tulsi一直被用来治疗疾病和感染,尤其是在印度。因为我们将杜尔西用于医疗目的,所以为了充分利用其草药特性,监测其健康状况至关重要。植物病害危害植物的健康和生长。植物的疾病检测至关重要,这样就可以在疾病蔓延到整个植物之前进行治疗。为了检测杜鹃花叶片的疾病,我们提出了一种基于卷积神经网络的模型。图像处理和CNN被广泛应用。所制备的模型提取图像的关键特征,并将其分类为不同的紊乱。该模型的准确率为75%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tulsi Leaf Disease Detection using CNN
Since the start of the covid 19 pandemic, a wide range of medications have been produced and are currently being utilized to treat the disease. Tulsi, in addition to all of the chemical-based medications, is an herbal therapy that is particularly effective in the treatment of this ailment. Tulsi has been used to heal ailments and infections for millennia, particularly in India. Because we use tulsi for medicinal purposes, it's vital to monitor its health in order to reap the full benefits of its herbal properties. Plant diseases harm the health and growth of the plant. Disease detection in plants is crucial so that it can be treated before it spreads throughout the plant. To detect illnesses in tulsi leaves, we propose employing a model based on convolution neural networks. Image processing and CNN are widely employed. The prepared model extracts the image's key features and categorizes it into different disorders. The model has a 75 percent accuracy rate.
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