Plant leaf segmentation through connected pixel approach

R. Dayanand, D. Noola
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Abstract

Agricultural plays a significant role in human survival and it has become much more essential due to population increase and food demand, and hence the crop yield has to be produced according to the demand. However, one of the reason that quality and quantity of the crop gets compromised is the disease and in past various methodology has been proposed, however they lack on the various model metrics or the segmentation is achieved for the particular leaf,. In this paper, we have proposed a methodology named as SCPA (Segmentation through Connected Pixel Approach). The main objective of this paper is to achieve high accuracy segmentation. SCPA is the two step approach first we find the ROI(Region of Interest) of the particular leaf and in the second approach we find the instance based ROI i.e. for the whole plant, here both the step are performed simultaneously through incorporating one another. Moreover, SCPA is optimized iterative-based method and it is achieved through the approach of connected pixel approach. Connected pixels are the one where the edge of one pixel is connected to the other. When performed on the LSC dataset we achieve the accuracy of 95.10%. This methodology is compared with the various state of art model and existing system by considering the model metric such as SBD, the results shows that SCPA model performs better than the other exiting method also the pictorial comparison of segmented leaf are shown and it shows our model identify it well when compared to others.
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植物叶片的连通像素分割方法
农业在人类生存中扮演着重要的角色,由于人口的增长和对食物的需求,农业变得更加重要,因此作物产量必须根据需求来生产。然而,作物的质量和数量受到损害的原因之一是疾病,过去已经提出了各种方法,但是它们缺乏各种模型度量或对特定叶子的分割。在本文中,我们提出了一种名为SCPA (Segmentation through Connected Pixel Approach)的方法。本文的主要目标是实现高精度的分割。SCPA是两步方法,第一步我们找到特定叶子的ROI(感兴趣区域),第二步我们找到基于实例的ROI,即对于整个植物,这两个步骤通过合并同时执行。此外,SCPA是一种基于迭代的优化方法,并通过连通像素法实现。连通像素是指一个像素的边缘连接到另一个像素的边缘。当在LSC数据集上执行时,我们实现了95.10%的准确率。通过考虑SBD等模型度量,将该方法与各种先进的模型和现有系统进行了比较,结果表明SCPA模型比其他现有方法性能更好,并给出了分割叶片的图像比较,表明我们的模型与其他方法相比具有更好的识别能力。
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