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Multi-resolution mobile vision system for plant leaf disease diagnosis

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Abstract

The process of detecting plant disease by human naked-eye is difficult and very expensive practice, particularly in developing countries like India. Designing and providing a fast-reliable automated mobile vision based solution for such tasks, is a great realistic contribution to the society. In this paper, a mobile client–server architecture for leaf disease detection and diagnosis using a novel combination of Gabor wavelet transform (GWT) and gray level co-occurrence matrix (GLCM), opens a new dimension in pattern recognition, is proposed. Mobile disease diagnosis system represents a diseased patch in multi-resolution and multi-direction feature vector. Mobile client captures and pre-processes the leaf image, segments diseased patches in it and transmits to the Pathology Server, reducing transmission cost. The Server performs the computational tasks: GWT–GLCM feature extraction and \(k\)-Nearest Neighbor classification. The result is sent back to the users screen via an SMS (short messaging service) with an accuracy rate of 93 %, in best condition. On the other part, paper also focus on design of Human-mobile interface (HMI), which is useful even for the illiterate farmers, to automatically monitor their field at any stage by just a mobile click. Android is currently used to run this system which can be easily extended to other mobile operating systems.

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Notes

  1. Here, 3D means simply three dimensions of color space \((R, G, B)\) in RGB color space model.

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Acknowledgments

The research work was funded by SURA, IIT Roorkee. Also we would like to thank Prof. N. Harsh, HOD (Plant Pathology Division), and Prof. R. Reddy, Forest Research Institute, Dehradun, for their technical supports in diseased plant leaves identification. Special thanks to Abhay Prakash and Mukul Agarwal.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Haridwar, 247667, India

    Shitala Prasad & Sateesh K. Peddoju

  2. Department of Electronics and Communication Engineering, Indian Institute of Technology Roorkee, Haridwar, 247667, India

    Debashis Ghosh

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  1. Shitala Prasad
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  2. Sateesh K. Peddoju
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  3. Debashis Ghosh
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Correspondence to Shitala Prasad.

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Prasad, S., Peddoju, S.K. & Ghosh, D. Multi-resolution mobile vision system for plant leaf disease diagnosis. SIViP 10, 379–388 (2016). https://doi.org/10.1007/s11760-015-0751-y

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