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Link to original content: https://doi.org/10.1007/s11276-021-02723-x
FCS-fuzzy net: cluster head selection and routing-based weed classification in IoT with mapreduce framework | Wireless Networks Skip to main content
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FCS-fuzzy net: cluster head selection and routing-based weed classification in IoT with mapreduce framework

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Abstract

Automatic weed classification is necessary to enhance the productivity of crops in weed management system. Major issue in agriculture is the control of weeds growing among plantation crops. Various site-specific weed management techniques are developed to classify the weeds, but enhancing the production quality and yield poses a complex task in agriculture. An effective method is developed to classify the crop or weeds using the proposed Feedback Cat Swarm Optimization-based Deep Neuro-Fuzzy Network (FCSO-based DNFN). However, the proposed FCSO is derived by integrating the Feedback Artificial Tree (FAT) algorithm and Cat Swarm Optimization (CSO) algorithm. The process of weed classification is carried out with the MapReduce framework by employing the mapper and reducer functions. The cluster head (CH) selection is made, and the process of routing is carried out through CH to transfer the weed image from the sensor nodes to Base Station (BS) for weed classification. However, the classification process is done based on the fitness measure by considering the feature vector. Through the comparative analysis of the proposed system with the existing state-of-art techniques, the proposed FCSO-based DNFN obtained higher performance in terms ofthroughput, energy, delay, and accuracy with the values of 0.5018, 0.5014 J, 0.5174 s, and 0.9523, respectively. Besides, while increasing the node size, the accuracy of the classification improves, and hence the performance of the developed model also increases.

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References

  1. Li, J., Xie, S., Chen, Z., Liu, H., Kang, J., Fan, Z., & Li, W. (2020). A Shallow Convolutional Neural Network for Apple Classification. IEEE Access, 8, 111683–111692.

    Article  Google Scholar 

  2. Kulkarni, Shweta, & Angadi, S. A. (2019). IoT Based Weed Detection Using Image Processing and CNN. International Journal of Engineering Applied Sciences and Technology, 4(3), 606–609.

    Article  Google Scholar 

  3. Revanasiddappa, B., Arvind, C. S., & Swamy, S. (2020). Real-time early detection of weed plants in pulse crop field using a drone with IoT. Technology, 16(5), 1227–1242.

    Google Scholar 

  4. Louargant, M., Jones, G., Faroux, R., Paoli, J.-N., Maillot, T., Gee, C., & Villette, S. (2018). Unsupervised classification algorithm for early weed detection in row-crops by combining spatial and spectral information. Remote Sensing, 10(5), 761.

    Article  Google Scholar 

  5. Lottes, P., Behley, J., Chebrolu, N., Milioto, A., & Stachniss, C. (2020). Robust joint stem detection and crop-weed classification using image sequences for plant-specific treatment in precision farming. Journal of Field Robotics, 37(1), 20–34.

    Article  Google Scholar 

  6. Raja, R., Nguyen, T. T., Slaughter, D. C., & Fennimore, S. A. (2020). Real-time weed-crop classification and localization technique for robotic weed control in lettuce. Biosystems Engineering, 192, 257–274.

    Article  Google Scholar 

  7. Xiaolong, Wu., Aravecchia, S., Lottes, P., Stachniss, C., & Pradalier, C. (2020). Robotic weed control using automated weed and crop classification. Journal of Field Robotics, 37(2), 322–340.

    Article  Google Scholar 

  8. Dadashzadeh, M., Abbaspour-Gilandeh, Y., Mesri-Gundoshmian, T., & Sabzi, S. (2020). Jose Luis Hernandez-Hernandez, Mario Hernandez-Hernandez and Juan Ignacio Arribas, “Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields.” Plants, 9(5), 559.

    Article  Google Scholar 

  9. Muangprathub, Jirapond, Boonnam, Nathaphon, Kajornkasirat, Siriwan, Lekbangpong, Narongsak, Wanichsombat, Apirat, & Nillaor, Pichetwut. (2019). IoT and agriculture data analysis for smart farm. Computers and electronics in agriculture, 156, 467–474.

    Article  Google Scholar 

  10. Vinayak N. Malavade, and Pooja K. Akulwar. 2016. Role of IoT in agriculture. IOSR Journal of Computer Engineering, pp.2278–0661

  11. Mekala, Mahammad Shareef, & Viswanathan, P. (2019). CLAY-MIST: IoT-cloud enabled CMM index for smart agriculture monitoring system. Measurement, 134, 236–44.

    Article  Google Scholar 

  12. Meonghun Lee, Jeonghwan Hwang, and Hyun Yoe, 2013. Agricultural production system based on IoT, In proceedings of 2013 IEEE 16Th international conference on computational science and engineering, pp.833–837, December .

  13. Huang, Y. (2018). CHEN Zhong-xin, YU Tao, HUANG Xiang-zhi, and GU Xing-fa, “Agricultural remote sensing big data: Management and applications.” Journal of Integrative Agriculture, 17(9), 1915–1931.

    Article  Google Scholar 

  14. S. Rajeswari, K. Suthendran and K. Rajakumar, "A smart agricultural model by integrating IoT, mobile and cloud-based big data analytics", In proceedings of 2017 international conference on intelligent computing and control (I2C2), pp.1–5, June 2017.

  15. Jesus Martin Talavera. (2017). Luis Eduardo Tobon, Jairo Alejandro Gomez, Maria Alejandra Culman, Juan Manuel Aranda, Diana Teresa Parra, Luis Alfredo Quiroz, Adolfo Hoyos, and Luis Ernesto Garreta, “Review of IoT applications in agro-industrial and environmental fields.” Computers and Electronics in Agriculture, 142, 283–297.

    Article  Google Scholar 

  16. Md. Eshrat E Alahi, Xie Li, Subhas Mukhopadhyay, and Lucy Burkitt, . (2017). A temperature compensated smart nitrate-sensor for agricultural industry. IEEE Transactions on industrial electronics, 64(9), 7333–7341.

    Article  Google Scholar 

  17. Markovic, D., Koprivica, R., Pesovic, U., & Randic, S. (2015). Application of IoT in monitoring and controlling agricultural production. Acta Agriculturae Serbica, 20(40), 145–153.

    Article  Google Scholar 

  18. Page, K., Dang, Y., & Dalal, R. (2013). Impacts of conservation tillage on soil quality, including soil-borne crop diseases, with a focus on semi-arid grain cropping systems. Australasian Plant Pathology, 42(3), 363–377.

    Article  Google Scholar 

  19. M.K.Gayatri, J.Jayasakthin and Dr.G.S.Anandha Mala, "Providing Smart Agricultural solutions to farmers for better yielding using IoT", In proceedings of 2015 IEEE Technological Innovation in ICT for Agriculture and Rural Development (TIAR), pp.40–43, July 2015.

  20. Kamal Moummadi, Rachida Abidar and Hicham Medromi, "Generic model based on constraint programming and multi-agent system for M2M services and agricultural decision support", In proceeding of International Conference on Multimedia Computing and Systems, pp.1–6, April 2011.

  21. LI Jianting and Zhang Yingpeng, "Design and accomplishment of the real-time tracking system of agricultural products logistics process", In proceedings of International Conference on E-Product E-Service and E-Entertainment, pp.1–4, November 2010.

  22. M. R. Bendre, R. C. Thool, and V. R. Thool, "Big data in precision agriculture: Weather forecasting for future farming", In proceedings of 1st International Conference on Next Generation Computing Technologies (NGCT), pp.744–750, September 2015.

  23. Sabarina, K., & Priya, N. (2015). Lowering data dimensionality in big data for the benefit of precision agriculture. Procedia Computer Science, 48, 548–554.

    Article  Google Scholar 

  24. Huang, H., Deng, J., Lan, Y., Yang, A., Deng, X., Wen, S., Zhang, H., & Zhang, Y. (2018). Accurate weed mapping and prescription map generation based on fully convolutional networks using UAV imagery. Sensors, 18(10), 3299.

    Article  Google Scholar 

  25. Kunz, C., Weber, J. F., & Peteinatos, G. G. (2018). Markus So¨kefeld, and Roland Gerhards, “Camera steered mechanical weed control in sugar beet, maize and soybean.” Precision Agriculture, 19(4), 708–720.

    Article  Google Scholar 

  26. Dhumane, A. V., & Prasad, R. S. (2018). Fractional gravitational grey wolf optimization to multi-path data transmission in IoT. Wireless Personal Communications, 102(1), 411–436.

    Article  Google Scholar 

  27. Sakeena Javaid, Muhammad Abdullah, Nadeem Javaid, Tanzeela Sultana, Jawad Ahmed, and Norin Abdul Sattar, "Towards Buildings Energy Management: Using Seasonal Schedules Under Time of Use Pricing Tariff via Deep Neuro-Fuzzy Optimizer. In IEEE 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 1594–1599, June 2019.

  28. Li, Q. Q., He, Z. C., & Eric Li. (2020). The feedback artificial tree (FAT) algorithm. Soft Computing, 14, 1–28.

    Google Scholar 

  29. Mahdi Bahrami, Omid Bozorg-Haddad and Xuefeng Chu, "Cat swarm optimization", In proceedings of Pacific Rim international conference on artificial intelligence, pp.854–858, August 2007.

  30. AlexOlsen/Deep Weeds dataset taken from "https://github.com/AlexOlsen/DeepWeeds", accessed on October 2020.

  31. Singh, S. (2017). Energy efficient multilevel network model for heterogeneous WSNs. Engineering Science and Technology, an International Journal, 20(1), 105–115.

    Article  Google Scholar 

  32. Jain, P., & Tyagi, V. (2015). An adaptive edge-preserving image denoising technique using tetrolet transforms. The Visual Computer, 31(5), 657–674.

    Article  Google Scholar 

  33. Xie, S., Shan, S., Chen, X., & Chen, J. (2010). Member,"Fusing local patterns of gabor magnitude and phase for face recognition". IEEE transactions on image processing, 19(5), 1349–1361.

    Article  MathSciNet  MATH  Google Scholar 

  34. Vinusha, S., & Abinaya, J. S. (2018). Performance analysis of the Adaptive Cuckoo Search Rate Optimization Scheme for the Congestion Control in the WSN. Journal of Networking and Communication Systems, 1(1), 19–27.

    Google Scholar 

  35. Kelotra, A., & Pandey, P. (2019). Energy-aware Cluster Head Selection in WSN using HPSOCS Algorithm. Journal of Networking and Communication Systems, 2(1), 24–33.

    Google Scholar 

  36. Fatema Murshid AlBalushi. (2019). Chaotic based Hybrid Artificial Sheep Algorithm - Particle Swarm Optimization for Energy and Secure Aware in WSN. Journal of Networking and Communication Systems, 2(2), 37–48.

    Google Scholar 

  37. Bui, Thi Thao Hien., Jambulingam, Manimekalai, Amin, Muslim, & Hung, Nguyen Tan. (2021). Impact of COVID-19 pandemic on franchise performance from franchisee perspectives: the role of entrepreneurial orientation, market orientation and franchisor support. Journal of Sustainable Finance & Investment. https://doi.org/10.1080/20430795.2021.1891787

    Article  Google Scholar 

  38. Nguyen Tan Hung, "A Model of International Students’ Choice: A Mixed-Methods Study", International Virtual Conference on Public Administration, Social Science & Humanities, 2020.

  39. Dhumane, A. V., & Prasad, R. S. (2019). Multi-objective fractional gravitational search algorithm for energy efficient routing in IoT. Wireless Networks, 25, 399–413.

    Article  Google Scholar 

  40. Python in IoT; https://edu.varistor.in/python-in-iot/#:~:text=Many%20programming%20languages%20are%20used,in%20creating%20intelligent%20IoT%20devices.

  41. Yi Shan, Bo Wang, Jing Yan, Ningyi Xu, Yu Wang, and Huazhong Yang, "FPMR: MapReduce framework on FPGA", In Proceedings of the 18th annual ACM/SIGDA international symposium on Field programmable gate arrays, 2010.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Birla Institute of Applied Sciences, Bhimtal, Nainital, Uttarakhand, 263136, India

    Sudhir Sharma, Nitin Chhimwal, Kaushal Kishor Bhatt, Abhay Kumar Sharma, Prashant Mishra & Sandesh Tripathi

  2. Department of Biotechnology, SJC Bose Technical Campus, Kumaun University, Bhimtal, Uttarakhand, 263136, India

    Swati Sinha

  3. Department of Computer Science & Engineering, Inderprastha Engineering College, Ghaziabad, Uttar Pradesh, India

    Sundeep Raj

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  1. Sudhir Sharma
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Sharma, S., Chhimwal, N., Bhatt, K.K. et al. FCS-fuzzy net: cluster head selection and routing-based weed classification in IoT with mapreduce framework. Wireless Netw 27, 4929–4947 (2021). https://doi.org/10.1007/s11276-021-02723-x

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