雅卡索引
分割
人工智能
模式识别(心理学)
计算机科学
过程(计算)
图像分割
领域(数学)
相似性(几何)
数学
图像(数学)
操作系统
纯数学
作者
Srinivas Talasila,Kirti Rawal,Gaurav Sethi
出处
期刊:International journal of intelligent unmanned systems
[Emerald (MCB UP)]
日期:2021-11-23
卷期号:11 (1): 132-150
被引量:10
标识
DOI:10.1108/ijius-08-2021-0100
摘要
Purpose Extraction of leaf region from the plant leaf images is a prerequisite process for species recognition, disease detection and classification and so on, which are required for crop management. Several approaches were developed to implement the process of leaf region segmentation from the background. However, most of the methods were applied to the images taken under laboratory setups or plain background, but the application of leaf segmentation methods is vital to be used on real-time cultivation field images that contain complex backgrounds. So far, the efficient method that automatically segments leaf region from the complex background exclusively for black gram plant leaf images has not been developed. Design/methodology/approach Extracting leaf regions from the complex background is cumbersome, and the proposed PLRSNet (Plant Leaf Region Segmentation Net) is one of the solutions to this problem. In this paper, a customized deep network is designed and applied to extract leaf regions from the images taken from cultivation fields. Findings The proposed PLRSNet compared with the state-of-the-art methods and the experimental results evident that proposed PLRSNet yields 96.9% of Similarity Index/Dice, 94.2% of Jaccard/IoU, 98.55% of Correct Detection Ratio, Total Segmentation Error of 0.059 and Average Surface Distance of 3.037, representing a significant improvement over existing methods particularly taking into account of cultivation field images. Originality/value In this work, a customized deep learning network is designed for segmenting plant leaf region under complex background and named it as a PLRSNet.
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