• Prateek Pandey
• Jitendra Agarwal

Plants exist everyplace. There are regarding 310000 – 420000 renowned plant species within the world, however, several are still unknown nonetheless. Many rare plant species are at the margin of extinction and many are died out. All food people eat comes directly or indirectly from the plant. Plants have a variety of medicinal and healthy food properties. Plants play an important role in environmental protection. Plants are an integral part of our ecosystem. To effectively use plants, one should learn basic plant identification and classification skills. During this analysis, we tend to propose plant classification supported leaf identification. Each leaf of plant carries a significant amount of information which can be helpful to identify and classify the type of plant species. To carry out the research we have first downloaded dataset namely Flavia. This analysis targeted digital image process for plant classification based on leaf images. The system consists of 4 main modules namely image acquisition, image preprocessing, feature extraction and plant species classification. In image preprocessing the quality of the image is improved. In this research, color-based features and GLCM texture-based features are extracted from the leaf image. Lastly, Discriminant Analysis (DA), Decision Tree (DT), K- Nearest Neighbor (KNN) and Support Vector machine (SVM) classifier is used to classify the species to a particular plant kingdom. It is the type of Supervised Learning. The output displays leaf classified into its particular class. We got 97.5% accuracy.

Discriminant Analysis (DA), Decision Tree (DT), K- Nearest Neighbor (KNN) and Support Vector machine (SVM)

"modeling plant leaf using statistical features through various classifier", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 9, page no.7-12, September-2018, Available :http://www.jetir.org/papers/JETIR1809002.pdf

"modeling plant leaf using statistical features through various classifier", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 9, page no. pp7-12, September-2018, Available at : http://www.jetir.org/papers/JETIR1809002.pdf