A novel method for detecting morphologically similar crops and weeds based on the combination of contour masks and filtered Local Binary Pattern operators. Issue 3 (4th March 2020)

• Record Type: Journal Article
• Authors: Le, Vi Nguyen Thanh; Ahderom, Selam; Apopei, Beniamin; Alameh, Kamal
• Is Part Of: GigaScience. Volume 9:Issue 3(2020)
• Publication Date: 2020-03-04
• Subjects: precision agriculture -- morphological operators -- feature extraction -- local binary patterns -- contour masks -- weed detection -- computer vision; Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1093/gigascience/giaa017
• Languages: English
• ISSNs: 2047-217X

Abstract

Abstract: Background: Weeds are a major cause of low agricultural productivity. Some weeds have morphological features similar to crops, making them difficult to discriminate. Results: We propose a novel method using a combination of filtered features extracted by combined Local Binary Pattern operators and features extracted by plant-leaf contour masks to improve the discrimination rate between broadleaf plants. Opening and closing morphological operators were applied to filter noise in plant images. The images at 4 stages of growth were collected using a testbed system. Mask-based local binary pattern features were combined with filtered features and a coefficient k . The classification of crops and weeds was achieved using support vector machine with radial basis function kernel. By investigating optimal parameters, this method reached a classification accuracy of 98.63% with 4 classes in the "bccr-segset" dataset published online in comparison with an accuracy of 91.85% attained by a previously reported method. Conclusions: The proposed method enhances the identification of crops and weeds with similar appearance and demonstrates its capabilities in real-time weed detection.