Author Identifier
Vi Nguyen Thanh Le
ORCID:0000-0003-2665-9332
Document Type
Journal Article
Publication Title
Information Processing in Agriculture
PubMed ID
30395051
Publisher
China Agricultural University
School
Electron Science Research Institute
RAS ID
31234
Abstract
Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the combination of Local Binary Pattern (LBP) operators, for the extraction of crop leaf textural features and Support vector machine (SVM) method, for multiclass plant classification. This paper presents the first investigation of the accuracy of the combined LBP algorithms, trained using a large dataset of canola, radish and barley leaf images captured by a testing facility under simulated field conditions. The dataset has four subclasses, background, canola, corn, and radish, with 24,000 images used for training and 6000 images, for validation. The dataset is referred herein as “bccr-segset” and published online. In each subclass, plant images are collected at four crop growth stages. Experimentally, the algorithm demonstrates plant classification accuracy as high as 91.85%, for the four classes. © 2018 China Agricultural University
DOI
10.1016/j.inpa.2018.08.002
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Included in
Agriculture Commons, Artificial Intelligence and Robotics Commons, Mathematics Commons, Robotics Commons
Comments
Nguyen Thanh Le, V., Apopei, B., & Alameh, K. (2019). Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods. Information Processing in Agriculture, 6(1), 116-131. Available here.