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Novel Approach for Plant Disease Detection Based on Textural Feature Analysis

  • Varinderjit Kaur
  • Ashish OberoiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1042)

Abstract

The image processing is the technique which can propose the information stored in the form of pixels. The plant disease detection is the technique which can detect the disease from the leaf. The plant disease detection algorithms have various steps like preprocessing, feature extraction, segmentation, and classification. The KNN classifier technique is applied which can classify input data into certain classes. The performance of KNN classifier is compared with the existing techniques and it is analyzed that KNN classifier has high accuracy, less fault detection as compared to other techniques. This paper presents methods that use digital image processing techniques to detect, quantify, and classify plant diseases from digital images in the visible spectrum. In plant leaf classification leaf is classified based on its different morphological features. Some of the classification techniques used are neural network, genetic algorithm, support vector machine, and principal component analysis. In this paper results are compared between KNN classifier and SVM classifier.

Keywords

GLCM KNN K-means WDDIP-KNN Plant disease detection 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of CSERIMT UniversityMandi GobindgarhIndia

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