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FPGA based effective agriculture productivity prediction system using fuzzy support vector machine

Author

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  • Prabakaran, G.
  • Vaithiyanathan, D.
  • Ganesan, Madhavi

Abstract

This work investigates the functions of hardware-implemented intelligent decision support system using support vector machines. The system aims to forecast future productivity based on the data prepared by field experts followed by productivity influence factors. This feature is perceived by the combination of fuzzy logic and support vector machine. The proposed approach has been thoroughly tested at a ground level, and the designed structural test results have made major improvements compared to the lack of proper approach. This system proposed to compensate for performance decrease, achieved higher productivity with a prediction accuracy of 95%. Furthermore, the proposed intelligent embedded decision support system provided the deficit level of needed input scale to increase productivity and avoid excess consumption of fertilizer in agriculture. A 30-year climate parameter has been taken into account to establish such a system to control the consumption of fertilizers.

Suggested Citation

  • Prabakaran, G. & Vaithiyanathan, D. & Ganesan, Madhavi, 2021. "FPGA based effective agriculture productivity prediction system using fuzzy support vector machine," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 1-16.
    • Handle: RePEc:eee:matcom:v:185:y:2021:i:c:p:1-16
    DOI: 10.1016/j.matcom.2020.12.011
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    2. Bitopi Biswas & Mohammad Tariful Alam Khan & Mohammad Billal Hossain Momen & Mohammad. Rashedur Rahman Tanvir & Abu Mohammad Shahidul Alam & M Robiul Islam Islam, 2024. "Advancements in fuzzy expert systems for site-specific nitrogen fertilisation: Incorporating RGB colour codes and irrigation schedules for precision maize production in Bangladesh," Research in Agricultural Engineering, Czech Academy of Agricultural Sciences, vol. 70(3), pages 155-166.
    3. Ustaoglu, E. & Sisman, S. & Aydınoglu, A.C., 2021. "Determining agricultural suitable land in peri-urban geography using GIS and Multi Criteria Decision Analysis (MCDA) techniques," Ecological Modelling, Elsevier, vol. 455(C).
    4. Ahmed, Moiz Uddin & Hussain, Iqbal, 2022. "Prediction of Wheat Production Using Machine Learning Algorithms in northern areas of Pakistan," Telecommunications Policy, Elsevier, vol. 46(6).

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