Prediction of Suitability of Soil for Different Crops using Spatial Data Mining
Prathik A1, Anuradha J2, Uma K3

1Prathik. A, Research Associate, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
2Anuradha. J. (Correspondence Author), Associate Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
3Uma. K, Associate Professor, School of Advanced Science and Engineering, Vellore Institute of Technology, Vellore, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2330-2337 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1377109119/2019©BEIESP | DOI: 10.35940/ijeat.A1377.109119
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Abstract: The main Objective of Data mining in agriculture is to improvise the productivity based on the data observed and timelines of cultivation. Spatial Data mining, a key to capture the data by proposing sensors on a particular geographical location and observe various parameters to enhance the productivity based on the statistical analysis of data collected. In general, Data mining is an anticipating measurement and prognosticates the various data sets and mutate into useful data sets which can be applied on various applications. In this paper, data mining is applied in bridging the soil conditions to the applicable crop for cultivation in enhancing the productivity and multiple crops cultivation for enriched productivity based on the data sets acquired. A Statistical analysis resulted from a backend algorithm with the data sets and displayed as dashboard with the forecasted productivity. A Grid based clustering algorithm is adhered at the backend for performing analysis on the collected data sets results crop selectivity & productivity timelines. Geographical analysis forms a grid pattern with multiple data sets as matrix results in multiple crop selectivity based on the soil conditions and analyzed data sets obtained from various sensor parameters on a particular location. Data visualization is performed after the algorithmic process at the backend and data stored in the cloud server. Spatial Survey & Collective data Sets analyzed with the algorithm are used to elevate the Crop Selectivity and productivity on a soil based on the Biological Predicts, defoliant and manure usage timelines yields Improved Monetary generation.
Keywords: Spatial-data mining, Grid based clustering algorithm, Biological Predictions, Geographical Survey.