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Deep residual network-based data streaming approach for soil type application under IoT-based big data environment

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Abstract

The agriculture acquires more portions in Gross Domestic Product in the developed countries. However, analysis of the soil properties is a costly and time-consuming process. In this research, a new technique for classifying soil types on an IoT platform is developed. This method simulates the Internet of Things in order to transmit data. The Multi-verse Firefly algorithm (MVFA), which has been proposed, is used for clustering and routing. I n this case, the Multi-Verse Optimizer and Firefly algorithm are used to generate the proposed MVFA. At base station, the soil type classification is carried out utilizing the soil data. A selection of feature is done using an entropy factor and the type of soil is classified using Deep Residual Network considering spark architecture. The proposed MVFA trains the Deep Residual Network. The data stream is effectively handled with concept drift detection, and retraining of deep residual network is carried out utilizing Mutual information and Loss function-based thresholding. The proposed MVFA-enabled Deep Residual Network bestowed enhanced performance with an accuracy of 96%, sensitivity of 94.1%, specificity of 95.4%, delay of 90.6% and throughput of 0.0084.

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Data availability

The datasets analyzed during the current study are available in the Dukes County Soil Type repository, https://hub.arcgis.com/datasets/Dukescountygis::dukes-county-soil-type?geometry=-71.611%2C41.198%2C-69.759%2C41.559.

References

  1. Ahouandjinou, A.S., Kiki, P.M, & Assogba, K. (2017). Smart environment monitoring system by using sensors ultrasonic detection of farm pests. In 2017 2nd International Conference on Bio-engineering for Smart Technologies (BioSMART), pp. 1–5.

  2. Wedashwara, W., Ahmadi, C., & Arimbawa, I. W. A. (2019). Sequential fuzzy association rule mining algorithm for plants environment classification using internet of things. In AIP Conference Proceedings, 2199(1), 030004.

    Article  Google Scholar 

  3. Vij, A., Vijendra, S., Jain, A., Bajaj, S., Bassi, A., & Sharma, A. (2020). IoT and machine learning approaches for automation of farm irrigation system. Procedia Computer Science, 167, 1250–1257.

    Article  Google Scholar 

  4. Azizi, A., Gilandeh, Y. A., Mesri-Gundoshmian, T., Saleh-Bigdeli, A. A., & Moghaddam, H. A. (2020). Classification of soil aggregates: a novel approach based on deep learning. Soil and Tillage Research, 199, 104586.

    Article  Google Scholar 

  5. Pegalajar, M. C., Ruiz, L. G. B., Sánchez-Marañón, M., & Mansilla, L. (2020). A Munsell colour-based approach for soil classification using fuzzy logic and artificial neural networks. Fuzzy Sets and Systems, 401, 38–54.

    Article  MathSciNet  Google Scholar 

  6. Barman, U., & Choudhury, R. D. (2020). Soil texture classification using multi class support vector machine. Information Processing in Agriculture, 7(2), 318–332.

    Article  Google Scholar 

  7. Kovačević, M., Bajat, B., & Gajić, B. (2010). Soil type classification and estimation of soil properties using support vector machines. Geoderma, 154(3–4), 340–347.

    Article  Google Scholar 

  8. Sitton, J. D., Zeinali, Y., & Story, B. A. (2017). Rapid soil classification using artificial neural networks for use in constructing compressed earth blocks. Construction and Building Materials, 138, 214–221.

    Article  Google Scholar 

  9. Uriarte-Arcia, A.V., López-Yáñez, I., Yáñez-Márquez, C., Gama, J. & Camacho-Nieto, O. (2015). Data stream classification based on the gamma classifier. Mathematical Problems in Engineering.

  10. Ghomeshi, H., Gaber, M. M., & Kovalchuk, Y. (2019). RED-GENE: An evolutionary game theoretic approach to adaptive data stream classification. IEEE Access, 7, 173944–173954.

    Article  Google Scholar 

  11. Azizi, M., Ghasemi, S. A. M., Ejlali, R. G., & Talatahari, S. (2019). Optimal tuning of fuzzy parameters for structural motion control using multiverse optimizer. The Structural Design of Tall and Special Buildings, 28(13), 1652.

    Article  Google Scholar 

  12. Arora, S. & Singh, S. (2013) The firefly optimization algorithm: convergence analysis and parameter selection. International Journal of Computer Applications 69(3).

  13. Dukes County Soil Type. https://hub.arcgis.com/datasets/Dukescountygis::dukes-county-soil-type?geometry=-71.611%2C41.198%2C-69.759%2C41.559. Accessed on April 2021.

  14. Huluka, G., & Miller, R. (2014). Particle size determination by hydrometer method. Southern Cooperative Series Bulletin, 419, 180–184.

    Google Scholar 

  15. Day, P. R. (1965). “Particle fractionation and particle-size analysis”, methods of soil analysis: Part 1 physical and mineralogical properties. Including Statistics of Measurement and Sampling, 9, 545–567.

    Google Scholar 

  16. Rowell, D. L. (2014). Soil science: Methods and applications. 1st Edition, Routledge, London. pages. 368

    Book  Google Scholar 

  17. Domingos, P., & Hulten, G. (2000) Mining high-speed data streams. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 71–80.

  18. Hulten, G., Spencer, L., & Domingos, P. (2001). Mining time-changing data streams. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 97–106.

  19. Liang, C., Zhang, Y., Shi, P., & Hu, Z. (2012). Learning very fast decision tree from uncertain data streams with positive and unlabeled samples. Information Sciences, 213, 50–67.

    Article  MathSciNet  Google Scholar 

  20. Rutkowski, L., Pietruczuk, L., Duda, P., & Jaworski, M. (2012). Decision trees for mining data streams based on the McDiarmid’s bound. IEEE Transactions on Knowledge and Data Engineering, 25(6), 1272–1279.

    Article  Google Scholar 

  21. Widmer, G., & Kubat, M. (1996). Learning in the presence of concept drift and hidden contexts. Machine learning, 23(1), 69–101.

    Article  Google Scholar 

  22. Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM computing surveys (CSUR), 46(4), 1–37.

    Article  MATH  Google Scholar 

  23. Seybold, C. A., Grossman, R. B., & Reinsch, T. G. (2005). Predicting cation exchange capacity for soil survey using linear models. Soil Science Society of America Journal, 69(3), 856–863.

    Article  Google Scholar 

  24. Nauman, T. (2015) Spatializing the soil-ecological factorial: Data driven integrated land management tools.

  25. Chen, Z., Chen, Y., Wu, L., Cheng, S., & Lin, P. (2019). Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions. Energy Conversion and Management, 198, 111793.

    Article  Google Scholar 

  26. Balachandra, M., Prema, K. V., & Makkithaya, K. (2014). Multiconstrained and multipath QoS aware routing protocol for MANETs. Wireless networks, 20(8), 2395–2408.

    Article  Google Scholar 

  27. Ahmed, G., Zou, J., Fareed, M. M. S., & Zeeshan, M. (2016). Sleep-awake energy efficient distributed clustering algorithm for wireless sensor networks. Computers & Electrical Engineering, 56, 385–398.

    Article  Google Scholar 

  28. Mukherjee, S., & Sharma, N. (2012). Intrusion detection using naive Bayes classifier with feature reduction. Procedia Technology, 4, 119–128.

    Article  Google Scholar 

  29. Christoph R., Moritz H., & Frank-Michael S. (2020). Reactive soft prototype computing for concept drift streams. Retrieved July 10, 2020 from arXiv:2007.05432v1.

  30. Learned-Miller, E.G. (2013). Entropy and mutual information. Retrieved September 2013.

  31. Salehi, M., Boukerche, A., Darehshoorzadeh, A., & Mammeri, A. (2016). Towards a novel trust-based opportunistic routing protocol for wireless networks. Wireless Networks, 22(3), 927–943.

    Article  Google Scholar 

  32. Farmland Soil Classification. Retrieved April, 2021 from https://hub.arcgis.com/datasets/29a69c96239f42ba9ecfd95201f96395_0?geometry=-71.161%2C41.321%2C-70.235%2C41.501.

  33. Malige, G., Kiran, M. C., & Sammulal, P. (2019). Enhanced crow search optimization algorithm and hybrid NN-CNN classifiers for classification of land cover images. Multimedia Research, 2(3), 12–22.

    Google Scholar 

  34. Arun Raj, V., Neeraja K., Lavanya, M., & Kavin Manjari, R. (2022). IoT based crop rotation and soil nutrition analysis. Available online 18 May, 2022 (In Press).

  35. Aafreen, S.H., Soma P., Pravin Kumar, P., Shabika Fathima M., & Yashwanth Krishnan, B. (2021) Design of a comprehensive sensor based soil and crop analysis system model using machine learning algorithms. In Proceedings of 4th International Conference on Recent Developments in Control. Noida: Automation & Power Engineering (RDCAPE).

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Authors and Affiliations

  1. Department of CSE, Bapatla Engineering College, Bapatla, Andhra Pradesh, 522102, India

    D. Kishore Babu

  2. Department of CSE, Narasimha Reddy Engineering College, Hyderabad, Telangana, 500100, India

    C. Ravindra Raman

  3. Department of CSE, Raghu Engineering College, Visakhapatnam, Andhra Pradesh, India

    D. Venkata Divakara Rao

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  1. D. Kishore Babu
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  3. D. Venkata Divakara Rao
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Babu, D.K., Ravindra Raman, C. & Venkata Divakara Rao, D. Deep residual network-based data streaming approach for soil type application under IoT-based big data environment. Wireless Netw 29, 1751–1769 (2023). https://doi.org/10.1007/s11276-022-03195-3

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