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Classification of Crop Across Heterogeneous Landscape Through Experienced Artificial Bee Colony

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Abstract

This study aims to develop an experienced artificial bee colony algorithm which is proposed for classification of crops using remote sensed data. The study was conducted in Devnur village located in Nanajangudu taluk of Mysore district, where a highly differed irrigated agriculture system is present. The study is designed to overcome the issues of misclassification in crop patterns due to similar crop Phenology. In Artificial Bee Colony algorithm a single parameter like waggle dance is used for training and validating the classes particularly in high resolution images, this may lead to misclassification of classes like crops which has a same spectral features. In order to avoid this misclassification, a group of experienced employed bee is been trained and updated by each employed bee through a waggle dance where each employed bee depends on its own experience and the current terrain in each space. Here these features help in identifying the classes and updating of the weights in order to map the agents and identifying the classes and robust the spatial and spectral features. This resultant algorithm is called Experienced Artificial bee colony (EABC). The proposed strategy gives a structure within which a pixel based EABC gives commonly integral data to one another, so characterization process is refined through waggle dance. The findings of the experiment indicate that the suggested approach exhibits a 7% and 6% enhancement in Level 2, and an 8% and 5% improvement in Level 3 categories, respectively, compared to the Artificial Bee Colony (ABC) algorithm and Support Vector Machine (SVM).

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Data Availability

The dataset used in this article should be purchased from NRSC/NSIL OR data can be provided upon request and approval from NRSC and Higher officals of Instituions.

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Acknowledgements

We gratefully acknowledge NRSC, Hyderabad, for sharing the satellite data utilised in this work. We appreciate the management and employees of GSSSIETW and NCE-H, for their prompt assistance in completing the task.

Funding

The Department of Science and Technology, Government of India, New Delhi, India, has provided funding for this study via the CURIE programme (DST/CURIE-PG/2022/71). We would like to express our gratitude to the Management, Principal, and Staff of GSSSIETW for their timely help in enabling us to finish the task.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Navkis College of Engineering, Hassan, Karnataka, India

    H. K. Ravikiran & G. H. Yogeesh

  2. Department of Electronics and Communication Engineering, GSSS Institute of Engineering and Technology for Women, Mysore, Karnataka, India

    J. Jayanth & R. Dileep

  3. Department of Computer Science and Engineering, Navkis College of Engineering, Hassan, Karnataka, India

    M. S. Sathisha

Authors
  1. H. K. Ravikiran
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  2. J. Jayanth
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  3. M. S. Sathisha
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  4. G. H. Yogeesh
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  5. R. Dileep
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Contributions

The authors of this work have attributed the following roles: JJ and RHK were responsible for visualisation and conceptualization, respectively. JJ and RHK contributed to the methodology, while JJ provided the resources. DR, SMS and YGH were responsible for the original draught of the writing, while JJ and RHK contributed to the review and editing process. The final version of the manuscript was endorsed by all authors subsequent to their thorough review.

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Correspondence to H. K. Ravikiran.

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This article is part of the topical collection “Advances in Computational Intelligence, Paradigms and Applications” guest edited by Young Lee and S. Meenakshi Sundaram.

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Ravikiran, H.K., Jayanth, J., Sathisha, M.S. et al. Classification of Crop Across Heterogeneous Landscape Through Experienced Artificial Bee Colony. SN COMPUT. SCI. 5, 428 (2024). https://doi.org/10.1007/s42979-024-02790-9

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