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Book ChapterDOI

Automatic Plant Leaf Classification Based on Back Propagation Networks for Medical Applications

01 Jan 2020-pp 981-991
TL;DR: 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.
Citations
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Journal ArticleDOI
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.

6 citations

References
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Journal ArticleDOI
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.
Abstract: This paper proposes an automated system for recognizing plant species based on leaf images. Plant leaf images corresponding to three plant types, are analyzed using two different shape modeling techniques, the first based on the Moments-Invariant (M-I) model and the second on the Centroid- Radii (C-R) model. For the M-I model the first four normalized central moments have been considered and studied in various combinations viz. individually, in joint 2-D and 3-D feature spaces for producing optimum results. For the C-R model an edge detector has been used to identify the boundary of the leaf shape and 36 radii at 10 degree angular separation have been used to build the feature vector. To further improve the accuracy, a hybrid set of features involving both the M-I and C-R models has been generated and explored to find whether the combination feature vector can lead to better performance. Neural networks are used as classifiers for discrimination. The data set consists of 180 images divided into three classes with 60 images each. Accuracies ranging from 90%-100% are obtained which are comparable to the best figures reported in extant literature. Keywords-plant recognition; moment invariants; centroid-radii model; neural network; computer vision.

136 citations

Book ChapterDOI
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.
Abstract: In this paper we present 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. Both the centroid-contour distance curve and the eccentricity of a leaf image are scale, rotation, and translation invariant after proper normalizations. In the frist step, the eccentricity is used to rank leaf images, and the top scored images are further ranked using the centroid-contour distance curve together with the eccentricity in the second step. A thinning-based method is used to locate start point(s) for reducing the matching time. Experimental results show that our approach can achieve good performance with a reasonable computational complexity.

105 citations

Book ChapterDOI
26 Jun 2013
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%.
Abstract: A number of shape features for automatic plant recognition based on digital image processing have been proposed by Pauwels et al. in 2009. A database with 15 classes and 171 leaf samples was considered for the evaluation of these measures using linear discriminant analysis and hierarchical clustering. The results obtained match the human visual shape perception with an overall accuracy of 87%.

101 citations

Journal Article
Wu Qingfeng1, Lin Kunhui1, Zhou Chang-le1, M Li, 吴清锋 
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.
Abstract: Plant recognition is an important and challenging task. Leaf recognition plays an important role in plant recognition and its key issue lies in whether selected features are stable and have good ability to discriminate different kinds of leaves. From the view of plant leaf morphology (such as shape, dent, margin, vein and so on), domain–related visual features of plant leaf are analyzed and extracted first. On such a basis, an approach for recognizing plant leaf using artificial neural network is brought forward. The prototype system has been implemented. Experiment results prove the effectiveness and superiority of this method.

71 citations

Journal Article
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%.
Abstract: In this paper, the image processing techniques has been used in order to classify the plants by applying on the leaves images. To extract the leaves texture features, the Gray-Level Co-occurrence matrix (GLCM) and Principal Component Analysis (PCA) algorithms have been considered. The Algorithms are trained by 390 leaves to classify 13 kinds of plants with 65 new or deformed leaves images. The result indicates that the accuracy for the GLCM method is 78% while the accuracy for the PCA method is 98%.

61 citations