Image Processing Based on Leaf Diseases

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC).

Abstract

Detecting the visually salient regions in image is a fundamental problem. Salient object regions are soft decomposition of foreground and background image elements. To detect salient regions in an image in terms of saliency map. To create saliency map by using linear combination of colors in high dimensional color space. To improve the performance of saliency estimation, utilize the relative location and color contrast between super pixels. To resolve the salience estimation from trimap by using learning-based algorithm. To create three bench mark datasets, it is efficient in comparison with previous state of art saliency estimation methods. The identification of diseases on plant is an important key to prevent heavy loss of yield and the quantity of agricultural products. The symptoms can be seen on the parts of the plants such as leaf, stems, lesions and fruits. The leaf show the symptoms by changing color, showing the spots on it. This identification of the disease is done by manual observation and pathogen detection which can consume more time and may prove costly. The aim of the project is to identify and classify the disease accurately from the leaf images. The steps required in the process are Preprocessing, Training and Identification. For identification of disease features of leaf such as major axis, minor axis etc. Are extracted from leaf and given to classifier for classification.

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Keywords

Leaf Diseases, Preprocessing, Pixels, Training, Identification, Classification.