ArticleGigaScience2020
A novel method for detecting morphologically similar crops and weeds based on the combination of contour masks and filtered Local Binary Pattern operators.
Article in GigaScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- ConvGeM-next: a deep learning framework for plant disease detection.Frontiers in plant science · 2026Article
- Detection of commercial crop weeds using machine learning algorithms.Scientific reports · 2025Article
- YOLOv8-DBW: An Improved YOLOv8-Based Algorithm for Maize Leaf Diseases and Pests Detection.Sensors (Basel, Switzerland) · 2025Article
- Evaluation and Optimization of Prediction Models for Crop Yield in Plant Factory.Plants (Basel, Switzerland) · 2025Article
- A spontaneous keypoints connection algorithm for leafy plants skeletonization and phenotypes extraction.Frontiers in plant science · 2025Article
- General retrieval network model for multi-class plant leaf diseases based on hashing.PeerJ. Computer science · 2024Article
- Towards deep learning based smart farming for intelligent weeds management in crops.Frontiers in plant science · 2023Article
- Feature Mapping for Rice Leaf Defect Detection Based on a Custom Convolutional Architecture.Foods (Basel, Switzerland) · 2022Article
- A robust deep learning approach for tomato plant leaf disease localization and classification.Scientific reports · 2022Article
- Weed Classification from Natural Corn Field-Multi-Plant Images Based on Shallow and Deep Learning.Sensors (Basel, Switzerland) · 2022Article
- Artificial Intelligence-Based Drone System for Multiclass Plant Disease Detection Using an Improved Efficient Convolutional Neural Network.Frontiers in plant science · 2022Article
- DCNet: DenseNet-77-based CornerNet model for the tomato plant leaf disease detection and classification.Frontiers in plant science · 2022Article
- Redroot Pigweed (Frontiers in plant science · 2021Article
- Performances of the LBP Based Algorithm over CNN Models for Detecting Crops and Weeds with Similar Morphologies.Sensors (Basel, Switzerland) · 2020Article
- A novel method for detecting morphologically similar crops and weeds based on the combination of contour masks and filtered Local Binary Pattern operators.GigaScience · 2020Article
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4 authors.
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Abstract
backgroundWeeds are a major cause of low agricultural productivity. Some weeds have morphological features similar to crops, making them difficult to discriminate.
resultsWe 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.
conclusionsThe proposed method enhances the identification of crops and weeds with similar appearance and demonstrates its capabilities in real-time weed detection.
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