Flower Identification & Classification Based on Efficient Deep Learning Technique - Rupali Jena, Sujata Chakravarty (International Journal of Modern Agriculture, 2020)
Every day we see a huge number of flower species in our house, parks, roadsides, in farms, on our rooftop but we have no knowledge of that flower species or their origin. Even we have no idea about its name. There are several guidebooks for flowers knowledge but it becomes quite difficult to find the name when have the picture. Even the Internet sometimes is not useful. But it is quite difficult for human brain to memorize all the species they see. Even some flower is similar to look at. Image-based automatic flower species classification is an important problem for the biologists who construct digital flower catalogues. Image-based automatic flower species classification is an important problem for the biologists who construct digital flower catalogues. A dozen of work about flower species recognition has been proposed so far based on traditional image processing routines. Nowadays, researchers apply the deep learning on various image-based object recognition tasks. This Paper is based on the automatic flower recognition techniques that can be used by biologists, taxonomist and naïve peoples also. For constructing efficient feature extraction from CNN model is referred. Finally, a feature selection algorithm done automatically by neural network model. A support vector machine (SVM) is used to classify having Radial Bases Function (RBF) kernel is employed on Gray-scale image as well as in colour images, hence the comparison has been done. Flower dataset having 5 species which have a manageable number of images.