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Superlative Uprising of Smart Farming to Discovering the Magnitude and Superiority of the Agri-Data in Hybrid Techniques

  • K. Tharani
  • D. Ponniselvi
Conference paper
  • 34 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 118)

Abstract

This exploration concentrated on shrewd cultivating in agribusiness. The recent innovations increase the quality and quantity of agro-products. Based on the field and the soil moisture, the cultivation brings a profit. The plant can be affected by fungi, bacteria, and viruses. It affects the plants shortly. The maladies at the beginning time on the plants are exceptionally hard to discover. Earth’s perception will be founded on a Decision Support System (DSS). This methodology will apply in a proposed system to improve the soil continuum. Information mining procedures are connected here to improve the surplus and vitality framework. Be that as it may, in a current framework, they were utilizing a SAR procedure for the topographical debacle. Grouping is used to isolate the information for horticulture, and pre-preparing is used to identify the commotion and evacuate the unimportant information. For finding the ideal outcome, the K-means, fuzzy, KNN, and ANFIS are utilized for the finished structure. On account of these sicknesses, horticulture will elevate the ranchers to misfortune and influence the generation. Shrewd cultivating by applying information mining systems will expand profitability and benefit, just as it expands contamination security and the nature of the items.

Keywords

Decision support system (DSS) Fuzzy K-means KNN—K-means nearest neighborhood ANFIS—artificial neural fuzzy inference system 

References

  1. 1.
    Liu S, Guo L, Webb H, Ya X, Chang X (2019) Internet of Things monitoring system of modern eco-agriculture based on cloud computing. IEEE Access (99):1–1Google Scholar
  2. 2.
    Wu T, Luo J, Dong W, Sun Y, Xia L, Zhang X (2019) Geo-object-based soil organic matter mapping using machine learning algorithms with multi-source geo-spatial data. IEEE J Sel Top Appl Earth Observations Remote Sens 12(4)Google Scholar
  3. 3.
    Del Frate F, Schiavon G, Solimini D, Borgeaud M, Hoekman DH, Vissers MAM (2003) Crop classification using multiconfiguration C-band SAR data. IEEE Trans Geosci Remote Sens Adv Search 41(7):1611–1619Google Scholar
  4. 4.
    Miyaoka K, Maki M, Susaki J, Homma K, Noda K, Oki K (2013) Rice-planted area mapping using small sets of multi-temporal SAR data. IEEE Geosci Remote Sens Lett 10(6)Google Scholar
  5. 5.
    Chou J-S, Nguyen T-K (2018) Forward forecast of stock price using sliding-window metaheuristic- optimized machine-learning regression. IEEE Trans Ind Inf 14(7)Google Scholar
  6. 6.
    de Roo RD, Du Y, Ulaby FT, Dobson MC (2001) A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion. IEEE Trans Geosci Remote Sens 39(4)Google Scholar
  7. 7.
    Cristofani E, Becquaert M, Lambot S, Vandewal M, Stiens JH, Deligiannis N (2018) Random subsampling and data preconditioning for ground penetrating radars. IEEE Access 6Google Scholar
  8. 8.
    Du H, Mao F, Li X, Zhou G, Xu X, Han N, Sun S, Gao G, Cui L, Li Y, Zhu D, Liu Y, Chen L, Fan W, Li P, Shi Y, Zhou Y (2018) Mapping global bamboo forest distribution using multisource remote sensing data. IEEE J Sel Top Appl Earth Observations Remote Sens 11(5)Google Scholar
  9. 9.
    Jackson TJ, O’Neill PE (1990) Attenuation of soil microwave emission by corn and soybeans at 1.4 and 5 GHz. IEEE Trans Geosci Remote Sens 28(5)Google Scholar
  10. 10.
    Tomičić I, Schatten M (2016) An agent-based framework for modeling and simulation of resources in self-sustainable human settlements: a case study on water management in an eco-village community in Croatia. Int J Sustain Dev World Ecol 23(6)Google Scholar
  11. 11.
    Qin L, Feng S, Zhu H (2018) Research on the technological architectural design of geological hazard monitoring and rescue-after-disaster system based on cloud computing and Internet of things. Int J Syst Assur Eng Manag 9(3):684–695Google Scholar
  12. 12.
    Ishitsuka N, Saito G, Murakami T, Ogawa S, K Okamoto (2003) Methodology development for area determination of rice planted paddy using RADARSAT Data. J Remote Sens Soc Jpn 23(5):458–472Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • K. Tharani
    • 1
  • D. Ponniselvi
    • 1
  1. 1.Vivekanadha College of Arts and ScienceNamakkalIndia

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