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Image processing based rice plant leaves diseases in Thanjavur, Tamilnadu

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Abstract

India is a highly populated agricultural country which has land area of 60.3% for agriculture purpose. The production of rice plant is decreased up to 20–30% because of various diseases. The most frequent diseases occurred in paddy leaves are leaf blast, leaf blight, false smut, brown spot and leaf streak. This paper mainly considers a method to detect the leaf diseases automatically using image processing techniques. To determine these diseases, the proposed methodology involves image Acquisition, image pre-processing, segmentation and classification of paddy leaf disease. In this proposed system, the features are extracted using hybrid method of discrete wavelet transform, scale invariant feature transform and gray scale co-occurrence matrix approach. Finally, the extracted features are given to various classifiers such as K nearest neighborhood neural network, back propagation neural network, Naïve Bayesian and multiclass SVM to categorize disease and non-disease plants. Many classification techniques are examined to classify the leaf disease. In experimental result, the proposed work is implemented in MATLAB software and performance of this work is measured in terms of accuracy. It is observed that multi class SVM provides the better accuracy of 98.63% when compared to other classifiers.

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Acknowledgement

We thank Tamil Nadu Rice Research Institute (TRRI), Aduthurai, Thanjavur, for their kind support by providing data for the research study.

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Authors and Affiliations

  1. Department of ECE, School of EEE, SRC, SASTRA Deemed University, Kumbakonam, India

    T. Gayathri Devi

  2. Department of E&I, School of EEE, SASTRA Deemed University, Thanjavur, India

    P. Neelamegam

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  1. T. Gayathri Devi
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Correspondence to T. Gayathri Devi.

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Gayathri Devi, T., Neelamegam, P. Image processing based rice plant leaves diseases in Thanjavur, Tamilnadu. Cluster Comput 22 (Suppl 6), 13415–13428 (2019). https://doi.org/10.1007/s10586-018-1949-x

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  • DOI: https://doi.org/10.1007/s10586-018-1949-x

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