A Model of Plant Identification System Using GLCM, Lacunarity And Shen Features

Recently, many approaches have been introduced by several researchers to identify plants. Now, applications of texture, shape, color and vein features are common practices. However, there are many possibilities of methods can be developed to improve the performance of such identification systems. Therefore, several experiments had been conducted in this research. As a result, a new novel approach by using combination of Gray-Level Co-occurrence Matrix, lacunarity and Shen features and a Bayesian classifier gives a better result compared to other plant identification systems. For comparison, this research used two kinds of several datasets that were usually used for testing the performance of each plant identification system. The results show that the system gives an accuracy rate of 97.19% when using the Flavia dataset and 95.00% when using the Foliage dataset and outperforms other approaches.Comment: 10 page

LifeCLEF Plant Identification Task 2015

International audienceThe LifeCLEF plant identification challenge aims at evaluating plant identification methods and systems at a very large scale, close to the conditions of a real-world biodiversity monitoring scenario. The 2015 evaluation was actually conducted on a set of more than 100K images illustrating 1000 plant species living in West Europe. The main originality of this dataset is that it was built through a large-scale partic-ipatory sensing plateform initiated in 2011 and which now involves tens of thousands of contributors. This overview presents more precisely the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes

Malaysian medicinal plant leaf shape identification and classification

Malaysian medicinal plants may be abundant natural resources but there has not been much research done on preserving the knowledge of these medicinal plants which enables general public to know the leaf using computing capability.This study proposes a framework to identify and classify tropical medicinal plants in Malaysia based the extracted patterns from the leaf.The extracted patterns from medicinal plant leaf are obtained based on several angle features.Five classifiers, obtained from WEKA and an ensemble classifier, called Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML), are used to compare their performance accuracies over this data.In this experiment, five species of Malaysian medicinal plants are identified and classified in which each species will be represented by using 65 images.This study is important in order to assist local community to utilize the knowledge discovery and application of Malaysian medicinal plants for future generation

The ImageCLEF 2012 Plant Identification Task

International audienceThe ImageCLEF's plant identification task provides a testbed for the system-oriented evaluation of plant identification, more precisely on the 126 tree species identification based on leaf images. Three types of image content are considered: Scan, Scan-like (leaf photographs with a white uniform background), and Photograph (unconstrained leaf with natural background). The main originality of this data is that it was specifically built through a citizen sciences initiative conducted by Tela Botanica, a French social network of amateur and expert botanists. This makes the task closer to the conditions of a real-world application. This overview presents more precisely the resources and assessments of task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results. With a total of eleven groups from eight countries and with a total of 30 runs submitted, involving distinct and original methods, this second year pilot task confirms Image Retrieval community interest for biodiversity and botany, and highlights further challenging studies in plant identification

The ImageCLEF 2013 Plant Identification Task

International audienceThe ImageCLEF's plant identification task provides a testbed for a system-oriented evaluation of plant identification about 250 species trees and herbaceous plants based on detailed views of leaves, flowers, fruits, stems and bark or some entire views of the plants. Two types of image content are considered: SheetAsBackgroud which contains only leaves in a front of a generally white uniform background, and NaturalBackground which contains the 5 kinds of detailed views with unconstrained conditions, directly photographed on the plant. The main originality of this data is that it was specifically built through a citizen sciences initiative conducted by Tela Botanica, a French social network of amateur and expert botanists. This makes the task closer to the conditions of a real-world application. This overview presents more precisely the resources and assessments of task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results. With a total of twelve groups from nine countries and with a total of thirty three runs submitted, involving distinct and original methods, this third year task confirms Image Retrieval community interest for biodiversity and botany, and highlights further challenging studies in plant identification

The integration of freely available medium resolution optical sensors with Synthetic Aperture Radar (SAR) imagery capabilities for American bramble (Rubus cuneifolius) invasion detection and mapping.

Doctoral Degree. University of KwaZulu- Natal, Pietermaritzburg.The emergence of American bramble (Rubus cuneifolius) across South Africa has caused severe ecological and economic damage. To date, most of the efforts to mitigate its effects have been largely unsuccessful due to its prolific growth and widespread distribution. Accurate and timeous detection and mapping of Bramble is therefore critical to the development of effective eradication management plans. Hence, this study sought to determine the potential of freely available, new generation medium spatial resolution satellite imagery for the detection and mapping of American Bramble infestations within the UNESCO world heritage site of the uKhahlamba Drakensberg Park (UDP). The first part of the thesis determined the potential of conventional freely available remote sensing imagery for the detection and mapping of Bramble. Utilizing the Support Vector Machine (SVM) learning algorithm, it was established that Bramble could be detected with limited users (45%) and reasonable producers (80%) accuracies. Much of the confusion occurred between the grassland land cover class and Bramble. The second part of the study focused on fusing the new age optical imagery and Synthetic Aperture Radar (SAR) imagery for Bramble detection and mapping. The synergistic potential of fused imagery was evaluated using multiclass SVM classification algorithm. Feature level image fusion of optical imagery and SAR resulted in an overall classification accuracy of 76%, with increased users and producers’ accuracies for Bramble. These positive results offered an opportunity to explore the polarization variables associated with SAR imagery for improved classification accuracies. The final section of the study dwelt on the use of Vegetation Indices (VIs) derived from new age satellite imagery, in concert with SAR to improve Bramble classification accuracies. Whereas improvement in classification accuracies were minimal, the potential of stand-alone VIs to detect and map Bramble (80%) was noteworthy. Lastly, dual-polarized SAR was fused with new age optical imagery to determine the synergistic potential of dual-polarized SAR to increase Bramble mapping accuracies. Results indicated a marked increase in overall Bramble classification accuracy (85%), suggesting improved potential of dual-polarized SAR and optical imagery in invasive species detection and mapping. Overall, this study provides sufficient evidence of the complimentary and synergistic potential of active and passive remote sensing imagery for invasive alien species detection and mapping. Results of this study are important for supporting contemporary decision making relating to invasive species management and eradication in order to safeguard ecological biodiversity and pristine status of nationally protected areas

Performance analysis of support vector machine for early identification of citrus diseases

Early citrus disease detection is necessary for optimum citrus productivity. But detecting a citrus disease at an early stage requires expert views or laboratory tests. But getting an expert view of all time is impossible for rural farmers. The present study aimed to create a low-cost, intelligent, affordable citrus disease classification system. This study offered a Support Vector Machine (SVM) based smart classification method for categorizing various citrus diseases. Citrus photos were subjected to a variety of image processing techniques to categorize the diseases using SVM and the kernel. Prior to classification, the images were segmented and the hue channel threshold value was used to differentiate the diseased area from the remaining portion of the image. The segmented image’s color and grey domains were used to extract 13 different texture and color features. This study outlined three different SVM kernel types- Linear, Gaussian, and Polynomial, and evaluated their accuracy and confusion matrix performances. The Radial Based Function with a polynomial kernel derived from the SVM outperformed the SVM's linear and Gaussian kernel