49 research outputs found
A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network
Full text linkIn this paper, we employ Probabilistic Neural Network (PNN) with image and
data processing techniques to implement a general purpose automated leaf
recognition algorithm. 12 leaf features are extracted and orthogonalized into 5
principal variables which consist the input vector of the PNN. The PNN is
trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater
than 90%. Compared with other approaches, our algorithm is an accurate
artificial intelligence approach which is fast in execution and easy in
implementation.Comment: 6 pages, 3 figures, 2 table
Plant Recognition using Hog and Artificial Neural Network
Get PDFThis paper presents a plant leaf recognition system being implemented through Artificial Neural Networks. The system proposed is designed using MATLAB Software which takes a leaf image from the user and classifies, recognizes the plant species and shows all the relevant details about the plant.it also incorporates a webpage from various plant databases. The leaf features are extracted by using a HOG (Histograms of Oriented Gradients) vector and the ANN(Artificial Neural Network) is used in training through Backpropagation. We have extracted the HOG features from the flavia datasheet of leaves and trained them in the Neural Network. The results were nearly perfect and the accuracy of the program implemented is very high compared with other models
Mobile Leaf Classification Application Utilizing a Convolutional Neural Network
Get PDFPlant classification is an important task in biological research. However, plant classification is a complex task that very few biologists are qualified experts to conduct. Therefore, an application to assist in this task would be extremely useful for biology students, researchers, and enthusiasts.A significant amount of research has been done for the task of classifying plants based upon images of their leaves; however, all of that research has utilized images of single leaves on a white background for classification to allow easy extraction of shape features. This is not realistic for field work since a natural picture of a leaf will have a complex background.This thesis applies a convolutional neural network to the problem in order to allow classification of images with natural backgrounds. A mobile application is built that can run this neural network on images taken by the device’s camera. This tool can be used to assist in complex plant classification tasks anywhere as long as they have a mobile device with them
Automatic Plant Detection Using HOG and LBP Features With SVM
Get PDFPlants play a vital role in the cycle of nature. Plants are the only organisms which produce food by converting light energy from the sun. They also help in maintaining oxygen balance on earth by emitting oxygen and taking carbon dioxide. They have plenty of use in medicine and industry. But plant species are vast in number. To identify this large number of existing plant species in the world is a tedious and time-consuming task for a human. Hence, an automatic plant identification tool is very useful even for experienced botanists to identify the vast number of plants. In this paper, we proposed a technique to identify the plant leaf images. For training and testing, we used a publicly available dataset called Flavia leaf dataset. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are used to extract features and multiclass Support Vector Machine (SVM) is applied to classify the leaf images. We observed that the accuracy of HOG+SVM with HOG feature extraction using cells size of 2 x 2, 4 x 4 and 8 x 8 are 77.5%, 81.25% and 85.31 respectively. The accuracy of LBP+ SVM is 40.6% and the combination of HOG and LBP based features with SVM achieved 91.25% accuracy. The experimental results indicate the effectiveness of HOG+LBP with SVM over HOG+SVM and LBP+SVM techniques.
Classification of Plants Using Images of their Leaves
Get PDFPlant recognition is a matter of interest for scientists as well as laymen. Computer aided technologies can make the process of plant recognition much easier; botanists use morphological features of plants to recognize them. These features can also be used as a basis for an automated classification tool. For example, images of leaves of different plants can be studied to determine effective algorithms that could be used in classifying different plants. In this thesis, those salient features of plant leaves are studied that may be used as a basis for plant classification and recognition. These features are independent of leaf maturity and image translation, rotation and scaling and are studied to develop an approach that produces the best classification algorithm. First, the developed algorithms are used to classify a training set of images; then, a testing set of images is used for verifying the classification algorithms
