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Optimizing Rice Plant Diseases Recognition in Image Processing and Decision Tree Based Model

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Smart and Innovative Trends in Next Generation Computing Technologies (NGCT 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 828))

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Abstract

The objective of this paper is to design rice plant diseases recognition system and optimize the recognition efficiency of the system for new test datasets. In this research, the images from rice plant field had been captured by Charged Couple Device digital camera in Joint Photographic Experts Group format in day lighting. The total 6 categories of images with 5 categories of disease infected and one category of non-infected images had been captured. These acquired images had been pre-processed and segmented using three-level of the threshold to extract hybrid features which are a combination of color, texture and discrete cosine coefficient. The hybrid features of each image represent unique feature pattern of individual categories. The inverse multi-quadrics radial basis function had been applied on extracted hybrid features to make features localized and non-singular to enhance the uniqueness of the feature patterns. These transformed features had been used to design rice plant diseases recognition system using a decision tree. The uses of radial function drastically optimize the average recognition efficiency of diseased and non-diseased rice plant from 16.67% to 83.34%. This method can be generalized to design a monitoring system for plant diseases to help farmers and government agencies for on-location inspection and assessment of severity of diseases and take precautionary measure to control the spread of diseases.

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Acknowledgement

We are very thankful to Mr. Nalin Lunia (Secretary) and Dr. K.S.Pandya (Principal), Chhattisgarh Agriculture college, Durg, CG (India) for their kind support in this research to collect digital image data sets of rice plant under their esteemed guidance.

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Authors and Affiliations

  1. RCET, Chhattisgarh Swami Vivekanand Technical University, Bhilai, CG, 490024, India

    Toran Verma & Sipi Dubey

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  1. Toran Verma
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  2. Sipi Dubey
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Correspondence to Toran Verma .

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Editors and Affiliations

  1. Indian Institute of Technology Patna, Patna, Bihar, India

    Pushpak Bhattacharyya

  2. University of Petroleum and Energy Studies, Dehradun, India

    Hanumat G. Sastry

  3. University of Petroleum and Energy Studies, Dehradun, India

    Venkatadri Marriboyina

  4. University of Petroleum and Energy Studies, Dehradun, India

    Rashmi Sharma

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Verma, T., Dubey, S. (2018). Optimizing Rice Plant Diseases Recognition in Image Processing and Decision Tree Based Model. In: Bhattacharyya, P., Sastry, H., Marriboyina, V., Sharma, R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-10-8660-1_55

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  • DOI: https://doi.org/10.1007/978-981-10-8660-1_55

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8659-5

  • Online ISBN: 978-981-10-8660-1

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