Detecting heterogeneity parameters and hybrid models for precision farming

Abstract

AbstractPrecision farming (PF) plays a crucial role in the field of agriculture to solve the challenges of food shortages in society. Heterogeneity, multicollinearity, and outliers are problems in PF because they can cause bias and lead to incorrect inferences. However, traditional methods typically assume it to be a homogenous model, and in machine learning, data scientists ignore heterogeneity. In this study, the aim is to identify the heterogeneity parameters and develop hybrid models before and after heterogeneity. Data on seaweed is collected using sensor smart farming technology attached to v-Groove Hybrid Solar Drier (v-GHSD). There are 29 drying parameters, and each parameter has 1914 observations. We considered the highest order up to the second order interaction, and the parameters increased to 435 parameters from 29 parameters. In high-dimensional data, the number of observations is less than the number of parameters. The authors proposed a method using the variance inflation factor to identify the heterogeneity parameters. Seven predictive models such as ridge, random forest, support vector machine, bagging, boosting, LASSO and elastic net are used to select the 15, 25, 35 and 45 significant drying parameters for the moisture content removal of the seaweed, and hybrid models are developed using robust statistical methods. For before heterogeneity, the hybrid model random forest M Hampel with 19 outliers is the best, because it performs better when compared to other models. For after heterogeneity, the hybrid model boosting M Hampel with 19 outliers is the best, because it performs better when compared to other models. These results are vital to seaweed precision farming. The study of heterogeneity will not only help us to comprehend the dynamics of the large number of the drying parameters, but also gives a way to leverage the data for efficient predictive modelling.