Suitable area identification for mulberry plantation using query-based prescriptive analytics and microclimatic parameters

Abstract

Precision farming plays a vital role in suitable land location identification. Precision farming never considers microclimatic condition parameters for suitable land location identification. Farmers need know suitable crops and land area for cultivation based on current soil condition and microclimatic data. Farmers need yield predictions before cultivation of any crop. In this paper, a suitable area for Mulberry plantation cultivation is identified using current soil, microclimatic conditions and query using prescriptive analytics for higher yield predictions. Proposed Query-based Prescriptive analytics (QPA) for mulberry is performed through descriptive and predictive analysis. QPA recommend farmers for suitable area for cultivation of mulberry plants based on query such as soil, microclimatic and previous crop yield data. Descriptive analysis is performed through hybrid machine learning algorithms such as PCA-enabled GPR (PG) and Bayesian-optimized GPR (BG) for identification of data patterns and trends. Predictive analysis is performed using Decision Tree ID3 (DT) algorithm and Pelican optimized LSTM (PL) for land suitability analysis. QPA based on BG-PL, combination of descriptive and predictive analysis, provides 99% accuracy in suitable land identification and crop yield prediction before cultivation. Proposed BG-DT, PG-PL and PG-DT methods of QPA provide suitable land identification with accuracy of 90%, 85% and 80%, respectively.