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Identification and Classification of Water Stressed Crops Using Hyperspectral Data: A Case Study of Paithan Tehsil

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Proceedings of 2nd International Conference on Communication, Computing and Networking

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 46))

Abstract

Globally, agricultural drought is the heterogeneous issue which causes the reduction of food production. The conventional methods have many limitations. Moreover, the use of multispectral remote sensing in drought condition monitoring possesses a limited spectral resolution which is insignificant for an understanding of water stress in the vegetation. In this regard, the study has been examined the agricultural droughts using ground observation, meteorological data and hyperspectral remote sensing (HRS) for assessment of crop water stress. The objective of this research was to: (a) examine the meteorological and hyperspectral data set for drought assessment (b) examine the agricultural stress tool for agricultural crop stress classification. The experimental results were evaluated and validated. The overall accuracy was obtained 86.66% with kappa coefficient 0.80. The research study has investigated the severe drought in the study area due to scanty rainfall during the Kharif season of year 2014. The present work is beneficial for identifying and monitoring the agricultural drought for better planning and management of crops.

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Acknowledgements

The authors would like to acknowledge and thanks to UGC, India for granting UGC SAP (II) DRS Phase-I and Phase-II F. No. 3-42/2009 and 4-15/2015/DRS-II for Laboratory facility to Department of CS and IT, Dr. BAM University, Aurangabad, Maharashtra, India and financial assistance under UGC BSR Fellowship for this work. The author is thankful Vaijapur Tehsil office and the Agricultural office for providing meteorological and sown area data. The author is also thankful to USGS for providing all the satellite images.

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Authors and Affiliations

  1. Geospatial Technology Research Laboratory, Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, 431004, India

    Sandeep V. Gaikwad, Amol D. Vibhute, K. V. Kale, S. C. Mehrotra, Rajesh K. Dhumal, Amarsinh B. Varpe & Rupali R. Surase

Authors
  1. Sandeep V. Gaikwad
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  2. Amol D. Vibhute
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  3. K. V. Kale
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  4. S. C. Mehrotra
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  5. Rajesh K. Dhumal
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  6. Amarsinh B. Varpe
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  7. Rupali R. Surase
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Corresponding author

Correspondence to Sandeep V. Gaikwad .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India

    C. Rama Krishna

  2. Department of Educational Television Centre, National Institute of Technical Teachers Training and Research, Chandigarh, India

    Maitreyee Dutta

  3. Department of Computer Science and Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India

    Rakesh Kumar

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Gaikwad, S.V. et al. (2019). Identification and Classification of Water Stressed Crops Using Hyperspectral Data: A Case Study of Paithan Tehsil. In: Krishna, C., Dutta, M., Kumar, R. (eds) Proceedings of 2nd International Conference on Communication, Computing and Networking. Lecture Notes in Networks and Systems, vol 46. Springer, Singapore. https://doi.org/10.1007/978-981-13-1217-5_89

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  • DOI: https://doi.org/10.1007/978-981-13-1217-5_89

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1216-8

  • Online ISBN: 978-981-13-1217-5

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