Skip to main content

Advertisement

SpringerLink
Log in

eCrop: A Novel Framework for Automatic Crop Damage Estimation in Smart Agriculture

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Natural disasters impact agriculture. Farmers incur large losses due to crop damage. Climate/weather-driven natural events or disasters are happening often and are causing billions of dollars in losses. Crop insurance provides economic stability to the agricultural industry to make up for losses. A crop insurance claim is an extensive process and it takes time to process claims. In this paper, we propose a proof-of-concept of the novel crop damage estimation method, eCrop which is a part of our proposed agriculture cyber-physical system. eCrop is a grid-based method. We also present a novel crop damage detection method. It is the core of eCrop. It is a Convolutional Siamese Neural Network (CSNN) based model. A meta-learning approach has been taken to train the model. An accuracy of \(92.86\%\) has been achieved. Our eCrop method can be adapted to agricultural insurance claim processing to automatically estimate the crop damage. It is scalable to any size of the cropland and any type of crop.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Climate Change: How Do We Know? https://climate.nasa.gov/evidence/. Accessed on 29 December, 2021

  2. Climate Smart Agriculture. https://www.worldbank.org/en/topic/climate-smart-agriculture. Accessed on 18 January, 2022

  3. Corn Kernel Damage:. https://digitalcommons.unl.edu/cgi/viewcontent.cgi?referer=https://www.google.com/ &httpsredir=1 &article=4447 &context=extensionhist. Accessed on 10 December, 2021

  4. Cyber-Physical Systems Executive Summary. http://iccps.acm.org/2011/_doc/CPS-Executive-Summary.pdf. Accessed on 27 February 2022

  5. Dataset: USDA. https://www.ams.usda.gov/book/corn. Accessed on 10 December, 2021

  6. Disaster Analysis. https://www.nass.usda.gov/Research_and_Science/Disaster-Analysis/. Accessed on 23 December, 2021

  7. Groungwater Nitrate Contamination. https://prd-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/thumbnails/image/wss-nitrogen-map-us-risk-areas.jpg. Accessed on 23 December, 2021

  8. IPCC Sixth Assessment Report, Summary for Policymakers. https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Headline_Statements.pdf. Accessed on 29 December, 2021

  9. Natural Disasters and Crop Insurance. https://www.rma.usda.gov/en/Fact-Sheets/National-Fact-Sheets/Natural-Disasters-and-Crop-Insurance. Accessed on 30 December, 2021

  10. Plant Disease. https://www.kaggle.com/saroz014/plant-diseases. Accessed on 23 December, 2021

  11. Soil Health. https://new.cloudvault.usda.gov/index.php/s/7iknp275KdTKwCA. Accessed on 23 December, 2021

  12. Bell J, Gebremichael E, Molthan A, Schultz L, Meyer F, Shrestha S. Synthetic Aperture Radar and Optical Remote Sensing of Crop Damage Attributed to Severe Weather in the Central United States. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium(IGARSS 2019),2019; pp. 9938–9941 . https://doi.org/10.1109/IGARSS.2019.8899775

  13. Bromley J, Bentz JW, Bottou L, Guyon I, LeCun Y, Moore C, Säckinger E, Shah R. Signature verification using a “siamese” time delay neural network. International Journal of Pattern Recognition and Artificial Intelligence. 1993;7(04):669–88.

  14. Ceballos F, Kramer B, Robles M. The feasibility of picture-based insurance (PBI): Smartphone pictures for affordable crop insurance. Development Engineering. 2019;4: 100042. https://doi.org/10.1016/j.deveng.2019.100042.

    Article  Google Scholar 

  15. Chung S, Takeuchi J, Fujihara M, Oeurng C. Flood damage assessment on rice crop in the Stung Sen River Basin of Cambodia. Paddy and Water Environment. 2019;17:255–63. https://doi.org/10.1007/s10333-019-00718-1.

    Article  Google Scholar 

  16. Dey S, Dutta A, Toledo JI, Ghosh SK, Llados J, Pal U. SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification 2017

  17. Di L, Yu E, Shrestha R, Lin L. DVDI: A New Remotely Sensed Index for Measuring Vegetation Damage Caused by Natural Disasters. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium(IGARSS 2018), 2018; pp. 9067–9069 https://doi.org/10.1109/IGARSS.2018.8518022

  18. Egala BS, Pradhan AK, Badarla V, Mohanty SP. Fortified-Chain: A Blockchain-Based Framework for Security and Privacy-Assured Internet of Medical Things With Effective Access Control. IEEE Internet of Things Journal. 2021;8(14):11717–31. https://doi.org/10.1109/JIOT.2021.3058946.

    Article  Google Scholar 

  19. Ellen Gray. Global Climate Change Impact on Crops Expected Within 10 Years, NASA Study Finds (2021). https://climate.nasa.gov/news/3124/global-climate-change-impact-on-crops-expected-within-10-years-nasa-study-finds/. Accessed on 01 January, 2022

  20. Emily Sohn. Climate change and the rise and fall of civilizations (2014). https://climate.nasa.gov/news/1010/climate-change-and-the-rise-and-fall-of-civilizations/. Accessed on 01 January, 2022

  21. Hadsell R, Chopra S, LeCun Y. Dimensionality reduction by learning an invariant mapping. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, 2006; pp. 1735–1742

  22. Hsuan WC, Hao LS, Kuo YC. Recognization of rice damage area on UAV ortho-images. In: Proceedings of IEEE International Conference on Applied System Invention (ICASI), 2018;pp. 1092–1094 . https://doi.org/10.1109/ICASI.2018.8394470

  23. Joshi S, Mohanty SP, Kougianos E. Everything You Wanted to Know About PUFs. IEEE Potentials. 2017;36(6):38–46. https://doi.org/10.1109/MPOT.2015.2490261.

    Article  Google Scholar 

  24. Kloos S, Yuan Y, Castelli M, Menzel A. Agricultural Drought Detection with MODIS Based Vegetation Health Indices in Southeast Germany. Remote Sensing 13(19) 2021 https://doi.org/10.3390/rs13193907. https://www.mdpi.com/2072-4292/13/19/3907

  25. Kougianos E, Mohanty SP, Coelho G, Albalawi U, Sundaravadivel P. Design of a high-performance system for secure image communication in the internet of things. IEEE Access. 2016;4:1222–42. https://doi.org/10.1109/ACCESS.2016.2542800.

    Article  Google Scholar 

  26. Kuželka K, Surový P. Automatic detection and quantification of wild game crop damage using an unmanned aerial vehicle (UAV) equipped with an optical sensor payload: a case study in wheat. European Journal of Remote Sensing. 2018;51(1):241–50. https://doi.org/10.1080/22797254.2017.1419442.

    Article  Google Scholar 

  27. Kwak Y, Shrestha BB, Yorozuya A, Sawano H. Rapid Damage Assessment of Rice Crop After Large-Scale Flood in the Cambodian Floodplain Using Temporal Spatial Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2015;8(7):3700–9. https://doi.org/10.1109/JSTARS.2015.2440439.

    Article  Google Scholar 

  28. Mitra A, Vangipuram SLT, Bapatla AK, Bathalapalli VKVV, Mohanty SP, Kougianos E, Ray C. Everything You wanted to Know about Smart Agriculture. arXiv Computer Science arXiv:2201.04754 2022; p. 45 pages Jan . https://doi.org/10.48550/arXiv.2201.04754

  29. Ochandio Fernández A, Olguín Pinatti CA, Masot Peris R, Laguarda-Miró N. Freeze-Damage Detection in Lemons Using Electrochemical Impedance Spectroscopy. Sensors 19(18) 2019. https://doi.org/10.3390/s19184051. https://www.mdpi.com/1424-8220/19/18/4051

  30. Pallagani V, Khandelwal V, Chandra B, Udutalapally V, Das D, Mohanty SP. dCrop: A deep-learning based framework for accurate prediction of diseases of crops in smart agriculture. In: Proceedings of IEEE International Symposium on Smart Electronic Systems (iSES),2019; pp. 29–33

  31. Puig Garcia E, Gonzalez F, Hamilton G, Grundy P. Assessment of crop insect damage using unmanned aerial systems: A machine learning approach. In: Proceedings of 21st International Congress on Modelling and Simulation, MODSIM2015, 2015; pp. 1420–1426

  32. Rutten A, Casaer J, Vogels MFA, Addink EA, Vanden Borre J, Leirs H. Assessing agricultural damage by wild boar using drones. Wildlife Society Bulletin. 2018;42(4):568–76. https://doi.org/10.1002/wsb.916.

    Article  Google Scholar 

  33. Sawant S, Mohite J, Sakkan M, Pappula S. Near Real Time Crop Loss Estimation using Remote Sensing Observations. In: Proceedings of the 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 2019; pp. 1–5 . https://doi.org/10.1109/Agro-Geoinformatics.2019.8820217

  34. Sosa L, Justel A, Molina I. Detection of Crop Hail Damage with a Machine Learning Algorithm Using Time Series of Remote Sensing Data. Agronomy 11(10) 2021. https://doi.org/10.3390/agronomy11102078. https://www.mdpi.com/2073-4395/11/10/2078

  35. Tripathy PK, Tripathy AK, Agarwal A, Mohanty SP. MyGreen: An IoT-Enabled Smart Greenhouse for Sustainable Agriculture. IEEE Consumer Electronics Magazine. 2021;10(4):57–62.

    Article  Google Scholar 

  36. Udutalapally V, Mohanty SP, Pallagani V, Khandelwal V. sCrop: A novel device for sustainable automatic disease prediction, crop selection, and irrigation in internet-of-agro-things for smart agriculture. arXiv preprint arXiv:2005.06342 2020; p. 23 pages May

  37. USDA N.A.S.S. Farms and Land in Farms 2019 Summaryg (February, 2020). https://www.nass.usda.gov/Publications/Todays_Reports/reports/fnlo0220.pdf. Accrssed on 10 January, 2022

  38. Wuebbles D, Fahey D, Hibbard K, Dokken D, Stewart B,(eds.), T.M. USGCRP, 2017:Climate Science Special Report: Fourth National Climate Assessment. U.S. Global Change Research Program I, 470 2017. https://doi.org/10.7930/J0J964J6

  39. Yang W, Yang C, Hao Z, Xie C, Li M. Diagnosis of Plant Cold Damage Based on Hyperspectral Imaging and Convolutional Neural Network. IEEE Access. 2019;7:118239–48. https://doi.org/10.1109/ACCESS.2019.2936892.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA

    Alakananda Mitra & Saraju P. Mohanty

  2. Texas Academy of Mathematics and Science, Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA

    Anshuman Singhal

  3. Department of Electrical Engineering, University of North Texas, Denton, TX, USA

    Elias Kougianos

  4. Nebraska Water Center, Department of Civil and Environmental Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA

    Chittaranjan Ray

Authors
  1. Alakananda Mitra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anshuman Singhal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saraju P. Mohanty
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elias Kougianos
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chittaranjan Ray
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saraju P. Mohanty.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest and there was no human or animal testing or participation involved in this research. All data were obtained from public domain sources.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mitra, A., Singhal, A., Mohanty, S.P. et al. eCrop: A Novel Framework for Automatic Crop Damage Estimation in Smart Agriculture. SN COMPUT. SCI. 3, 319 (2022). https://doi.org/10.1007/s42979-022-01216-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-022-01216-8

Keywords

Search

Navigation