稻谷爆炸检测:一种基于阈值的OTSU图像处理方法

Tanvir Ahmed, Rashidul Hasan Nabil, MD. Siyamul Islam
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引用次数: 0

摘要

如果水稻感染蔓延,农业产业以及以大米为主要粮食的人们将遭受巨大的生产和经济损失,以及粮食短缺。稻瘟病是影响水稻植株生长发育的最致命病害之一。由于褐斑病和叶风病外观相同,但形状不同,因此很难区分。在这种情况下,利用计算机视觉方法检测稻瘟病。但由于它们与其他斑点相似,颜色通道选择差,以前的方法困难,耗时,并且检测爆炸的能力差。本文提出了一种有效的、自动化的水稻叶胚图像分析方法,该方法可以利用不同的形状来识别水稻叶胚。此外,该过程最大限度地减少了无意义的数据探索,并提供了95.34%的优越准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of Paddy Blast: An Image Processing Approach with Threshold based OTSU
If rice infections spread, the agricultural industry as well as the people who eat rice as their primary food grain suffer greatly from production and financial losses as well as food shortages. One of the deadliest diseases that can affect paddy plants at any stage of development and hinder the growth of rice plants is paddy leaf blast. Because the brown spot and the leaf blast have the same appearance but distinct shapes, it is quite difficult to distinguish between them. In this case, paddy leaf blast is detected using computer vision methods. But because of their resemblance to other spots and poor color channel selection, previous procedures are difficult, time-consuming, and poorly able to detect blasts. In this article, an effective and automated image analysis method has been proposed to identify paddy leaf blasts that can identify leaf blasts by utilizing various shapes. Additionally, the process minimized pointless data exploration and provided superior accuracy of 95.34 percent.
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