基于JSEG算法的咖啡树图像分割与病害检测

Jeferson de Souza Dias, J. H. Saito
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引用次数: 1

摘要

巴西是世界上最大的咖啡生产国,因此要保持咖啡豆的高品质和纯度面临许多挑战。因此,利用计算机科学的资源来研究咖啡植物,并帮助农学家检测诸如锈病等疾病是很重要的。在本工作中,描述了使用图像分割算法JSEG的实验,该算法能够在多尺度下分割图像。利用咖啡树图像数据库RoCoLe (Robusta coffee Leaf Images),采用JSEG算法对这些图像进行4个尺度的分割。在每个尺度中选取典型的片段,利用归一化颜色直方图的相似性对其进行分组。用这种方法比较了几种尺度分割。结果表明,1、2两种等级的区段比3、4等级的区段颜色更均匀,适合作为锈病检测的训练样本。
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Coffee plant image segmentation and disease detection using JSEG algorithm
Brazil is the largest coffee producer in the world, and then there are many challenges to maintain the high quality and purity of the beans. Thus, it is important to study coffee plants, and help agronomists to detect diseases, such as rust, with resources of computer science. In this work, it is described experiments using image segmentation algorithm JSEG, which is capable to segment images in multi-scale. Using a coffee tree image database RoCoLe (Robusta Coffee Leaf Images), the JSEG algorithm is used to segment these images in four scales. It is selected typical segments in each scale and they are grouped using similarity of normalized color histograms. In this way the several scales segmentations are compared. It is concluded that the segments in scales 1 and 2, in which the colors are more homogeneous then in scales 3 and 4, are adequate to use as training samples for the detection of rust diseases.
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