Extemporize Agriculture Yield with Predictions Based on Water and Soil Properties using Multivariate Analytics and Machine Learning Algorithm
V. Sudha1, S. Mohan2

1V. Sudha, Research Scholar, Department of CIS, Annamalai University, Tamil Nadu India.
2Dr. S. Mohan, Assistant Professor, Department of Computer Science and Engineering, Annamalai University, Tamil Nadu India..
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1504-1508 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8137088619/2019©BEIESP | DOI: 10.35940/ijeat.F8137.088619
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Big data have rapidly developed in agriculture that increase the extensive attention of farmers to get extraordinary ideas for future crop development based on soil and water is an essential key factor of agriculture to predict data. In future farmers apply the extracts PCA (Principal component analysis) in the agriculture for crops to find best yield. The level of data is analyzed using PCA and PLS (Projection to latent structure) datasets like Crop data, Soil properties and Water properties such as Linear Regression, Multi Linear Regression, Discriminate Analysis, Partial Least Square, Least squares algorithm is used instead of Multivariate Curve Resolution. Predictive analytics can be used to make a modular decision in farming by observation of actual time data on crops, soil and water data received from different agriculture sources would contain multi-dimensions, the entire content is needed for performing analysis. Multivariate data Analysis based on Partial Least Square Programming model that identifies the cropping pattern to getting maximum yield correlated. The motive of the work is to compare various techniques which give the maximum accuracy of crop.
Keywords: PCA; PSL; Support Vector Machine; Multivariate; Correlate; Data Analytics.