Book ChapterDOI
Automatic Plant Leaf Classification Based on Back Propagation Networks for Medical Applications
Karattupalayam Chidambaram Saranya,Apoorv Goyal +1 more
- pp 981-991
TLDR
In this article, a review of an intelligent recognition system is presented to classify different types of leaves (40 classes of leaves) using back propagation neural network and the system presents a very good accuracy.Abstract:
Recognition of medicinal leaves has been a skill that is passed down ages. Being a skill of great importance, it can help the community if the use of the skill can be generalized. In this paper, a review of an intelligent recognition system is presented to classify different types of leaves (40 classes of leaves) using back propagation neural network and the system presents a very good accuracy. At the end, the portability and ease of use of the system are demonstrated as a GUI making the system user-friendly and rendering it ready to use.read more
Citations
More filters
Journal ArticleDOI
Using soft computing and leaf dimensions to determine sex in immature Pistacia vera genotypes
TL;DR: Results indicated RBF could reliably be used to differentiate between male and female pistachio genotypes and soft computing models are useful tools for predicting sex in pistachios based on leaf dimensions.
References
More filters
Journal ArticleDOI
Plant Leaf Recognition using Shape based Features and Neural Network classifiers
Jyotismita Chaki,Ranjan Parekh +1 more
TL;DR: An automated system for recognizing plant species based on leaf images using the Moments-Invariant model and the Centroid- Radii model is proposed, which is comparable to the best figures reported in extant literature.
Book ChapterDOI
Leaf Image Retrieval with Shape Features
TL;DR: An efficient two-step approach of using a shape characterization function called centroid-contour distance curve and the object eccentricity (or elongation) for leaf image retrieval and Experimental results show that this approach can achieve good performance with a reasonable computational complexity.
Book ChapterDOI
Evaluation of Features for Leaf Discrimination
TL;DR: A database with 15 classes and 171 leaf samples was considered and the results obtained match the human visual shape perception with an overall accuracy of 87%.
Journal Article
Feature extraction and automatic recognition of plant leaf using artificial neural network
TL;DR: Experimental results prove the effectiveness and superiority of the approach for recognizing plant leaf using artificial neural network, and the prototype system has been implemented.
Journal Article
Leaf recognition for plant classification using GLCM and PCA methods
TL;DR: The Gray-Level Co-occurrence matrix (GLCM) and Principal Component Analysis (PCA) algorithms have been considered to extract the leaves texture features and the result indicates that the accuracy for the GLCM method is 78% while the accuracy of the PCA method is 98%.