Kolivand Hoshang, Fern Bong Mei, Rahim Mohd Shafry Mohd, Sulong Ghazali, Baker Thar, Tully David
Department of Computer Science, Liverpool John Moores University, Liverpool, United Kingdom.
Universiti Tunku Abdul Rahman, Jalan Sungai Long, Bandar Sungai Long, Cheras, Kajang, Selangor, Malaysia.
PLoS One. 2018 Feb 8;13(2):e0191447. doi: 10.1371/journal.pone.0191447. eCollection 2018.
In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost.
在本文中，我们提出了一种利用叶片的表型部分（叶裂片、叶尖和叶基检测）来识别叶型和鉴定植物物种的新方法。该领域的大多数研究都集中在诸如形状、颜色、叶脉和纹理等常见特征上，这些特征消耗大量的计算处理资源且效率不高，尤其是在具有高度复杂叶片结构的槭树数据库中。本文重点关注叶片的表型部分，这提高了准确性。基于每个边界点的质心轮廓距离来检测局部最大值和局部最小值，利用南北区域来识别叶尖和叶基。使用数字形态学来测量叶片形状和叶缘。提出质心轮廓梯度来提取叶尖和叶基的曲率。我们分析了32张热带植物叶片图像，并使用两个不同的数据集（弗拉维亚数据集和槭树数据集）进行评估。分别获得的最佳准确率为94.76%和82.6%。实验结果表明了所提技术的有效性，该技术无需考虑那些计算成本高的常用特征。
