A study of Feature Extraction of leaf shape and Texture Quality Surface

Improving Plant Identification through Leaf Disorder Recognition and Classification Techniques

Abstract

The aim of this paper is to the field of leaf Disorder recognition for plant identification hasexperienced an increased need for fast and efficient classification algorithms to aid in keeping track of themost precious plants on earth. This requirement resulted in a number of techniques revolutionizing theautomatic classification area. The increasing number of techniques has led to a dilemma in deciding whichof these methods have the best qualities and potential to efficiently classify. In the botanical industrywhere the information distortion can produce inaccurate diagnosis, this problem is particularlyimportant. Thus, the urgent necessity of the botanical field is automated tools that help identify factories.The main process of the machine's training includes building a leaf database, image enhancement,segmentation (leaf extraction) features, extraction and classification. Leaf disorder recognition (CAPLDR)consists of four stages. This research aims primarily at proposing techniques for improving everyplant identification operation through leaf disease. A system for improving the leaf image was proposed,called 'Enhanced wavelet-based demoizing with built-in edge enhancement and automatic contrastadjustment algorithm. This 197 method combines the wavelets, CLAHE (contrast adjustment), cornerenhances and a relaxed middle filter (noise removal), with a single procedure to increase the visualquality of the leaf image.