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An adaptive plant leaf mobile informatics using RSSC

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Abstract

An automated plant biometric system is now an important step in preserving nature’s biodiversity. This paper presents a novel Relative Sub-image Sparse Coefficient (RSSC) algorithm for mobile devices (MDs) representing plant leaves into a mathematically compact vector for its classification. The RSSC feature vector includes local Statistical Entropy Texture (SET) information inter-related to all the sub-images within a leaf. RSSC space is merged with Gray Level Co-occurrence Matrix (GLCM) feature to refine the outputs using best-Nearest Neighbor (best-NN), designed for MDs. The experiments were performed on three different types of leaf datasets: (i) Flavia, (ii) ICL and (iii) Diseased leaf datasets. The results proves our method more accurate and better compared to other existing plant identification systems. The proposed approach is also tolerant under shape distortion caused while capturing. The mobile machine learning system for leaf image informatics is deployed on Android devices which helps botanists, agriculturists and medical biologists to recognize ubiquitously the herbs and plant species anywhere-anytime.

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Acknowledgments

Authors would like to thank MHRD for financially supporting S. Prasad throughout his PhD work at IIT Roorkee.

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Authors and Affiliations

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

    Shitala Prasad & Sateesh Kumar Peddoju

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

    Debashis Ghosh

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  1. Shitala Prasad
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  2. Sateesh Kumar 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. An adaptive plant leaf mobile informatics using RSSC. Multimed Tools Appl 76, 21339–21363 (2017). https://doi.org/10.1007/s11042-016-4040-8

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  • DOI: https://doi.org/10.1007/s11042-016-4040-8

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