Journal of Horticulture
Open Access
ISSN: 2376-0354
ISSN: 2376-0354
Department of Agriculture, North Carolina Agricultural and Technical State University, Greensboro NC, United States
Research Article
Using Realistic Images for Plant Classification and Effect on Classification Difficulty
Author(s): Yaqeen Salatneh Ashqer*, Marwan Bikdash and Chyi-Lyi (Kathleen) Liang
We proposed a hybrid computer-vision framework that distinguishes different plant species (such as radish and weeds) that combines machine learning methods (such as Support Vector Machine (SVM) and Random Forest (RF) classifiers) with application-specific features and image processing methods such as our own plant leaf isolation algorithm. The designed features include geometrical features that are sensitive to plant shape, as well as momentinvariant and texture features. The accuracy obtained using the combination of the designed features, and the isolation algorithm was 81.1% using SVM and 88.4% using Random Forest. We used 10-fold cross-validation to illustrate the importance of designing and selecting good features. We compared our designs with generic deep neural networks. We also compared our features with other features, such as SURF features classification, and our methods wer.. View More»