Multiclass Model for Agriculture development using Multivariate Statistical method

TL;DR

This paper introduces an improved multiclass classification model using an enhanced Mahalanobis Taguchi System for agriculture, achieving perfect accuracy in classifying crops like paddy, sugarcane, and groundnut.

Contribution

The paper proposes a novel multiclass model based on IMTS and Mahalanobis distance, tailored for agriculture development, with improved classification performance over traditional methods.

Findings

Achieved 100% accuracy, recall, and precision in crop classification.

Effectively clustered 26 factors into six main groups for model development.

Validated results against expert assessments, confirming model reliability.

Abstract

Mahalanobis taguchi system (MTS) is a multi-variate statistical method extensively used for feature selection and binary classification problems. The calculation of orthogonal array and signal-to-noise ratio in MTS makes the algorithm complicated when more number of factors are involved in the classification problem. Also the decision is based on the accuracy of normal and abnormal observations of the dataset. In this paper, a multiclass model using Improved Mahalanobis Taguchi System (IMTS) is proposed based on normal observations and Mahalanobis distance for agriculture development. Twenty-six input factors relevant to crop cultivation have been identified and clustered into six main factors for the development of the model. The multiclass model is developed with the consideration of the relative importance of the factors. An objective function is defined for the classification of three crops, namely paddy, sugarcane and groundnut. The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate when compared with other traditional classifier models.