Mathematical Modelling for Crop Selection Using Fuzzy Logic
Phagun Mehta
*
Department of Mathematics and Statistics, College of Basic Sciences and Humanities, Chaudhary Charan Singh Haryana Agricultural University, Hisar-125004, Haryana, India and Department of Electrical, Electronics and Communication Engineering (EECE), North Campus University, Delhi, India.
Manju Singh Tonk
Department of Mathematics and Statistics, College of Basic Sciences and Humanities, Chaudhary Charan Singh Haryana Agricultural University, Hisar-125004, Haryana, India and Department of Electrical, Electronics and Communication Engineering (EECE), North Campus University, Delhi, India.
Kautiliya Chaudhary
Department of Mathematics and Statistics, College of Basic Sciences and Humanities, Chaudhary Charan Singh Haryana Agricultural University, Hisar-125004, Haryana, India Department of Electrical, Electronics and Communication Engineering (EECE), North Campus University, Delhi, India.
Vikas Siwach
Department of Mathematics and Statistics, College of Basic Sciences and Humanities, Chaudhary Charan Singh Haryana Agricultural University, Hisar-125004, Haryana, India and Department of Electrical, Electronics and Communication Engineering (EECE), North Campus University, Delhi, India.
Anu Tonk
Department of Mathematics and Statistics, College of Basic Sciences and Humanities, Chaudhary Charan Singh Haryana Agricultural University, Hisar-125004, Haryana, India and Department of Electrical, Electronics and Communication Engineering (EECE), North Campus University, Delhi, India.
*Author to whom correspondence should be addressed.
Abstract
In today's rapidly advancing world, research in agriculture is rapidly shifting towards mathematical modeling using soft computing techniques. Modeling techniques applied in agriculture can provide valuable insights into research priorities and the fundamental interactions of the entire soil-plant-atmosphere system. By using a model to estimate the significance and impact of specific parameters, a researcher can identify the most influential factors, leading to more informed decisions.
The primary objective of this paper is to present a decision-making tool constructed with a fuzzy logic model, designed to enhance precision and reduce ambiguity in crop selection based on available soil nutrients for better crop yields. The model is applied to five samples selected from different land areas, providing a robust and representative data set. The proposed fuzzy logic model provides a powerful tool for addressing the challenge of crop selection under conditions of uncertain and incomplete information, enabling agricultural experts to make informed decisions and optimize yields.
Keywords: Fuzzy logic, membership functions, decision support system, fuzzy rule base, MATLAB