Assessment of Geostatistical Models for the Major Soil Nutrients for Tumkur District of Karnataka, India
Leena H.U1, Premasudha B.G2, P. K. Basavaraja3, H. Mohamed Saqeebulla4, G.V. Gangamrutha5
1Leena H.U.*, Full-time Research Scholar, Department of Master of Computer Application, Siddaganga Institute of Technology, Tumakuru, India and affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India.
2Premasudha B.G., Professor ( Department of MCA, Siddganga Institute of Technology, Tumakuru, Karnataka.
3P.K. Basavarja, Professor & Scheme Head, H. Mohamed Saqeebulla, Senior Research Fellow and G.V. Gangamrutha, Research Associate AICRP on STCR, Department of Soil Science & Agricultural Chemistry, UAS, GKVK, Bengaluru, India.
4H. Mohamed Saqeebulla,working as Research Fellow in AICRP on STCR, Dept. of Soil Science & Agril. Chemistry UAS, GKVK, Bangalore, India.
5G. V. Gangamrutha, working as Senior Research Fellow in the scheme AICRP on STCR, Department of Soil Science and Agricultural Chemistry, University of Agricultural Sciences, G.K.V.K., Bengaluru, Karnataka.

Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9382-9387 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9606118419/2019©BEIESP | DOI: 10.35940/ijrte.D9606.118419

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Abstract: Digitization of agriculture has tremendously increased in the adoption of various advanced techniques in the Indian agricultural sector. One of the core agriculture objectives is preserving soil fertility. To achieve this efficient soil fertility management alongside an effective spatial distribution of soil nutrient properties is required. The main objective of this study is to evaluate and propose the best interpolation technique on estimating the soil nutrients status to provide site-specific fertilizer recommendations through the Soil Test Crop Response target yield approach. In this study, we have focused on three major soil nutrients viz., nitrogen (N), phosphorus (P2O5) and potassium (K2O) for evaluation. The benchmarking study has considered four most successive interpolation techniques like Ordinary Kriging (OK), Radial Basis Function (RBF), Inverse Distance Weighted (IDW), and Global Polynomial Function (GPI). The evaluation and analytical results proved Ordinary Kriging is better by securing the highest accuracy against other interpolation techniques concerning RMSE and ME for interpreting the soil nutrients N, P2O5, and K2O. The interpreted values are also cross-validated with actual soil test samples with an accuracy of more than 85% for each nutrient. Nevertheless, these results are dependent on the number of actual soil test samples and the accuracy of the designed network with overall accuracy between the interpreted and the actual data.
Keywords: Soil Nutrients, Ordinary Kriging, Radial Basis Function, Inverse Distance Weighted, Global Polynomial Function.
Scope of the Article: Software Defined Networking and Network Function Virtualization.