Classification of Name Based On Leaf Recognition Using BT and ED Algorithm

Abstract

ABSTRACT:The main purpose of this paper should be to show that the outer frame of a leaf and with the help of Back propagation Network is enough to give a reasonable statement about the species category is identified. Leaves Recognition is a neuronal network based java application/applet to recognize images of leaves using Back propagation Network. The intention is to give the user the ability to administrate a hierarchical list of images, where they can perform some sort of image using edge detection to identify the individual tokens of every image. The Thinning algorithm here is used to process the image recursively and minimizes the found lines to a one-pixel wide one by comparing the actual pixel situation with specific patterns can be identified and then minimizes it. The urgent situation is that due to environmental degradation and lack of awareness, many rare plant species are at the risk of extinction so it is necessary to keep record for plant protection. It focuses on using digital image processing for the purpose of automate classification and recognition of plants based on the images of the leaves. It help to protect the plant and mainly it is used for highly production of rare plant or herbal plant used for medical purpose. Efficacy of the proposed methods is studied by using two neural classifiers. These are neuro-fuzzy controller and a feed-forward back-propagation multi-layered perception to discriminate between 28 classes of leaves. The features have been applied individually as well as in combination to investigate how recognition accuracies can be improved with the help of B&T algorithm.

Autentikasi Daun Herbal Menggunakan Convolutional Neural Network dan Raspberry Pi

“…Akurasi yang diperoleh adalah 90,27%. Sebuah penelitian juga telah mengidentifikasi 32 gambar dari 24 tanaman di pulau Mauritius menggunakan metode Random Forest dengan teknik 10 cross validation dengan akurasi mencapai 90,1% [13], sedangkan penelitian lainnya menggunakan neuro-fuzzy dan feed forward back propagation multi-layer perception untuk klasifikasi 28 jenis daun [14]. Telah dilakukan pula penelitian yang mengidentifikasi 200 daun ayurvedic dari 20 tanaman menggunakan Known Leaf Image Database [15].…”

A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification

“…Neural network was applied in [128] for plant disease classification and identification, based on the color co-occurrence texture features of the leaf. The BPN was used in [129] with the edge features for classification of leaves such as the neem, pine and oak and achieved 90.45% classification accuracy. The authors of the paper [130] used the BPN to classify the night jasmine, arka (blue madar), mango, neem, and shigru (moringa/drumstick) and achieved 85% accuracy.…”

“…However, in [67] geometrical features incorporating a neuro fuzzy classifier with the added advantage of its humanlike reasoning style and linguistic model, achieved an accuracy of 97.5%. Hu, shape, texture 100% [151] Back propagation neural network Shapes, Angles and sinus of leaves 111 leaves with 14 species [125] Texture and wavelet feature Grape varieties 93.3% [126] Colour Affected and Unaffected Vegetables [127] Texture of Colour Co-occurrence Disease Identification [128] Edge Features of leaf Neem, pine oak 90.33% [129] Leaf Margin Jasmine, arka, mango, neem and shigru 85% [130] Morphological features 450 leaves of 16 classes in Ayurveda and agriculture 90% [62] Texture Foxtail, crabgrass, velvet leaf, morning glory 97% [177] Fuzzy based classifiers Statistical features of leaf 97.6% [26] Texture, shape, colour 99.87% [86] K-nearest neighbour Edge, vein, ring projection wavelet feature 87.14% [120] Geometrical features 80% [134] Leaf Margin? texture 75.5% [135] Texton Costa Rican Flavia Dataset 99.1% [40] Texton 87.14% [120] Texture ICL-97.07% Plumber-72.8% Simthsonain-73.08% [37] HoCS, contour, colour, curvature Flavia 99.61% [39] Texture 97.55% [94] Run length sequence 93.17% [152] Contour-amplitude frequency descriptor Swedish-89.6% ICL-91.6% [198] Moving centre classifier Moment invariant 92.6% [137] Bayesian classifier Fourier descriptor 88% [116] Support vector machine HoCS Leafsnap [38] Wavelet features Ornamental Plants 95.83% [114] Fourier and texture Australian Federal dataset-100%, Flavia-99.7%, Foliage-99.8%, Swedish and Middle European datasets-99.2% [93] Kernel level descriptor Flavia-97.5% [110,111] Hu moments Annona Squamosa and Psidiuguajava, 86.6% [139] Lanculariity Flavia-95.048% [84] HOG?…”