A fully automated method for the recognition of medicinal plants using computer vision and machine learning techniques has been presented. Leaves from 24 different medicinal plant species were collected and photographed using a smart phone in a laboratory setting. A large number of features were extracted from each leaf such as its length, width, perimeter, and area, number of vertices, colour, perimeter and area of hull. Several derived features were then computed from these attributes. The best results were obtained from a random forest classifier using a 10-fold cross-validation technique. With an accuracy of 90.1%, the random forest classifier performed better than other machine learning approaches such as the k-nearest neighbour, naïve Bayes, support vector machines and neural networks.
This research proposed a new mobile application based on Android operating system for identifying Indonesian medicinal plant images based on texture and color features of digital leaf images. In the experiments we used 51 species of Indonesian medicinal plants and each species consists of 48 images, so the total images used in this research are 2,448 images. This research investigates effectiveness of the fusion between the Fuzzy Local Binary Pattern (FLBP) and the Fuzzy Color Histogram (FCH) in order to identify medicinal plants. The FLBP method is used for extracting leaf image texture. The FCH method is used for extracting leaf image color. The fusion of FLBP and FCH is done by using Product Decision Rules (PDR) method. This research used Probabilistic Neural Network (PNN) classifier for classifying medicinal plant species. The experimental results show that the fusion between FLBP and FCH can improve the average accuracy of medicinal plants identification. The accuracy of identification using fusion of FLBP and FCH is 74.51%. This application is very important to help people identifying and finding information about Indonesian medicinal plant.
• The most of existing methods has ignored the poor quality images like images with noise or poor brightness. • Less accuracy.
The proposed technique was tested on a dataset of 55 medicinal plants from Vietnam and a very high accuracy of 98.3% was obtained with a support vector machines (SVM) classifier. The size of each image was 256*256 pixels. Proposed an approach based on fractal dimension features based on leaf shape and vein patterns for the recognition and classification plant leaves. Using a k-nearest neighbour classifier with 20 features, they were able to achieve a high recognition rate of 87.1%. Using a volumetric fractal dimension approach to generate a texture signature for a leaf and the GLCM (Gray level co occurance matrix) algorithm.
• High accuracy is obtained and time consumption for detecting the shape.
• More datasets are included.
• We can find the medical application also.
1. Canny and binary
Processor Type : Pentium -IV
Speed : 2.4 GHZ
Ram : 128 MB RAM
Hard disk : 20 GB HD
Operating System : Windows 7
Software Programming Package : Matlab R2014a
Gao, W. and Lin, W., 2012. Frontal Parietal Control Network Regulates the Anti-Correlated Default and Dorsal Attention Networks. Human Brain Mapping, 33(1), 192–202.
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