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A novel algorithm for semi-automatic segmentation of plant leaf disease symptoms using digital image processing

机译:使用数字图像处理的植物叶病症状半自动分割的新算法

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摘要

A new computer algorithm is proposed to differentiate signs and symptoms of plant disease from asymptomatic tissues in plant leaves. The simple algorithm manipulates the histograms of the H (from HSV color space) and a (from the L*a*b* color space) color channels. All steps in the algorithmic process are automatic, with the exception of the final step in which the user decides which channel (H or a) provides the better differentiation. An in-depth analysis of the problem of disease symptom differentiation is also presented, in which issues such as lesion delimitation, illumination, leaf venation interference, leaf ruggedness, among others, are thoroughly discussed. The proposed algorithm was tested under a wide variety of conditions, which included 19 plant species, 82 diseases, and images gathered under controlled and uncontrolled environmental conditions. The algorithm proved useful for a wide variety of plant diseases and conditions, although some situations may require alternative solutions.
机译:提出了一种新的计算机算法来区分植物病的症状和体征与植物叶片无症状的组织。简单的算法可操纵H(来自HSV颜色空间)和a(来自L * a * b *颜色空间)颜色通道的直方图。算法过程中的所有步骤都是自动的,但最后一步除外,在最后一步中,用户决定哪个通道(H或a)提供更好的区分。还提出了对疾病症状分化问题的深入分析,其中对病灶定界,光照,叶片通气干扰,叶片坚固性等问题进行了全面讨论。该算法在多种条件下进行了测试,包括19种植物,82种病害以及在受控和非受控环境下采集的图像。尽管某些情况可能需要替代解决方案,但该算法已被证明对多种植物疾病和状况都非常有用。

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