Image based leaf segmentation and counting in rosette plants

作     者：J.Praveen Kumar S.Domnic 

作者机构：National Institute of TechnologyTiruchirappalliIndia 

出 版 物：《Information Processing in Agriculture》 (农业信息处理（英文）)

年 卷 期：2019年第6卷第2期

页      面：233-246页

核心收录：

学科分类：0710[理学-生物学] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 0907[农学-林学] 0908[农学-水产] 0905[农学-畜牧学] 0707[理学-海洋科学] 08[工学] 0906[农学-兽医学] 0829[工学-林业工程] 0901[农学-作物学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research did not receive any specific grant from funding agencies in the public  commercial  or not-for-profit sectors 

主　　题：Plant image analysis Plant phenotyping Leaf region extraction Leaf count 

摘      要：This paper proposes an efficient method to extract the leaf region and count the number of leaves in digital plant *** plant image analysis plays a significant role in viable and productive *** is used to record the plant growth,plant yield,chlorophyll fluorescence,plant width and tallness,leaf area,*** and *** growth is a major character to be analyzed among these plant characters and it directly depends on the number of leaves in the *** this paper,a new method is presented for leaf region extraction from plant images and counting the number of *** proposed method has three *** first step involves a new statistical based technique for image *** second step involves in the extraction of leaf region in plant image using a graph based *** third step involves in counting the number of leaves in the plant image by applying Circular Hough Transform(CHT).The proposed work has been experimented on benchmark datasets of Leaf Segmentation Challenge(LSC).The proposed method achieves the segmentation accuracy of 95.4%and it also achieves the counting accuracy of
0.7(DiC)and 2.3(|DiC|)for datasets(A1,A2 and A3),which are better than the state-of-the-art methods.