RRJBS | Volume 3 | Issue 3| July - September, 2014 34 Research and Reviews: Journal of Botanical Sciences Texture Analysis Using DWT for Grape Plant Species Classification. Ismail S Bagalkote 1 , Anup S Vibhute 1 , and BM More 2 . 1 Department of Electronics and Telecommunication Engg, BMIT, Solapur, Maharashtra, India. 2 Department of Physics, BMIT, Solpur, Maharashtra, India. Research Article Received: 11/05/2014 Revised : 28/05/2014 Accepted: 13/06/2014 *For Correspondence Department of Physics, BMIT, Solpur, Maharashtra, India. Keywords: Grape plant, Clone, Sonaka, Thomson and Manik ABSTRACT By observing leaf, we can easily identify plant but it is difficult to identify its species. In this project we developed an algorithm that gives user the ability to identify plant species based on photographs of the leaf. The core theme of this application is an algorithm that acquires statistical features of the leaves, and then classifies the species based on a novel combination of the computed texture feature analysis and wavelet analysis. While implementing this algorithm we have considered the Grape plant and its four species viz. Clone, Sonaka, Thomson and Manik. The algorithm is first trained against several samples of known plant species and then used to classify unknown query species. By using this algorithm we have achieved 93.33% efficiency. INTRODUCTION This Convergence of multidisciplinary research is more and more considered as the next big thing to answer profound challenges of humanity related to health, biodiversity or sustainable energy. The integration of life sciences and computer sciences has a major role to play towards managing and analyzing cross-disciplinary scientific data at a global scale. More specifically, building accurate knowledge of the identity, geographic distribution and uses of plants is essential if agricultural development is to be successful and biodiversity is to be conserved. Unfortunately, such basic information is often only partially available for professional stakeholders, teachers, scientists and citizens, and often incomplete for ecosystems that possess the highest plant diversity. A noticeable consequence, expressed as the taxonomic gap, is that identifying plant species is usually impossible for the general public, and often a difficult task for professionals, such as farmers or wood exploiters and even for the botanists themselves. The only way to overcome this problem is to speed up the collection and integration of raw observation data, while simultaneously providing to potential users an easy and efficient access to this botanical knowledge. In this context, content-based visual identification of plant's images is considered as one of the most promising solution to help bridging the taxonomic gap [1,2,3] . In the field of comparative biology, novel sources of data are continuously being sought to enable or enhance research varying from studies of evolution to generating tools for taxon identification. Leaves are especially important in this regard, because in many applied fields, such as studies of ecology or paleontology, reproductive organs, which may often provide an easier form of identification, are unavailable or present for only a limited season. Leaves are present during all seasons when plants are in growth. There are also millions of dried specimens available in herbaria around the world, many of which have already been imaged. While these specimens may possess reproductive organs, the main character features are often concealed in images through being internal or due to poor preparation. However, almost all specimens possess well-preserved and relatively easily imaged leaf material [4] . In this I developed an algorithm that gives user the ability to identify plant species based on photographs of the plant’s leaf taken by a camera. At the heart of this application is an algorithm that