Identification of Plant Types by Leaf Textures Based on the Backpropagation Neural Network

The number of species of plants or flora in Indonesia is abundant. The wealth of Indonesia's flora species is not to be doubted. Almost every region in Indonesia has one or some distinctive plant(s) which may not exist in other countries. In enhancing the potential diversity of tropical plant resources, good management and utilization of biodiversity is required. Based on such diversity, plant classification becomes a challenge to do. The most common way to recognize between one plant and another is to identify the leaf of each plant. Leaf-based classification is an alternative and the most effective way to do because leaves will exist all the time, while fruits and flowers may only exist at any given time. In this study, the researchers will identify plants based on the textures of the leaves. Leaf feature extraction is done by calculating the area value, perimeter, and additional features of leaf images such as shape roundness and slenderness. The results of the extraction will then be selected for training by using the backpropagation neural network. The result of the training (the formation of the training set) will be calculated to produce the value of recognition accuracy with which the feature value of the dataset of the leaf images is then to be matched. The result of the identification of plant species based on leaf texture characteristics is expected to accelerate the process of plant classification based on the characteristics of the leaves

Design and Development of Ficus Species Database and 2D Leaf Image Identification System

Plants are important part of the ecosystem in the world. Numerous studies had been done on the richness of plants diversity. There are many plant databases currently available online. A search for useful information regarding a particular plant can be performed easily through the databases using text such as the name of a plant. However, this task will be difficult if only image of the plant is available. Thus, to facilitate the search using image of a leaf, a simple two-dimensional (2-D) Ficus Identification Database System (FicIDS) was developed. This system can perform search using both text information and image of the plant. Basically, FicIDS system was focused on Ficus species. Ficus species were chosen due to its variable leaf shapes and its significance in our local herbal industries. Currently, there is a high demand for the natural products derived from this plant, particularly from Ficus deltoidea. But, it is very difficult to identify a Ficus plant correctly since there are more than 100 different Ficus species and more than 20 different varieties of F. deltoidea available in Malaysia. Furthermore, there is no proper documentation of this plant. The FicIDS system was designed and developed to identify Ficus plants based on the leaf image and to store the data about these species. Herbarium specimens of Ficus plant were prepared as evidence for the plants used in this study. Images of herbarium specimen and live plant materials were captured and stored in the database. Additional text information on Ficus plants were also collected from various sources to build up the database. Microsoft Office Access database management system was used to develop the plant database on Windows XP platform. 2-D leaf images identification system was constructed using the MATLAB R2006a program. The shape and size of plant leaves were used as the main features to identify a particular Ficus species. The process of image identification system comprised of four steps, namely image acquisition, image preprocessing and features extraction, computing of descriptors values and normalization, and k-nearest neighbor classification and decision making based on Euclidean distance. Thirteen descriptors values were used in identification of the image, which include aspect ratio, circularity, area convexity, rectangularity, sphericity, eccentricity, and 7 invariant moments. The accuracy of leaf image identification system was evaluated by using 130 leaf images corresponding to 6 Ficus species and 4 varieties of Ficus deltoidea. The evaluation of overall performance of FicIDS system showed that 120 (92.31%) of the tested leaf images were successfully identified by the system. However, the 2-D FicIDS system has some limitations with respect to image identification. The system requires single intact leaf with white background for identification. These limitations may be overcome by using three-dimensional (3-D) leaf image where more features of leaf such as leaf texture or venation can be included to improve the image recognition performance

Plant image retrieval using color, shape and texture features

We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered

Sabanci-Okan system at ImageClef 2011: plant identication task

We describe our participation in the plant identication task of ImageClef 2011. Our approach employs a variety of texture, shape as well as color descriptors. Due to the morphometric properties of plants, mathematical morphology has been advocated as the main methodology for texture characterization, supported by a multitude of contour-based shape and color features. We submitted a single run, where the focus has been almost exclusively on scan and scan-like images, due primarily to lack of time. Moreover, special care has been taken to obtain a fully automatic system, operating only on image data. While our photo results are low, we consider our submission successful, since besides being our rst attempt, our accuracy is the highest when considering the average of the scan and scan-like results, upon which we had concentrated our eorts