Detecting heterogeneity parameters and hybrid models for precision farming

Abstract

Precision 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.

Identifying heterogeneity for increasing the prediction accuracy of machine learning models

“…These advantages make it a preferable option over the elastic net model. This conclusion, emphasizing the superior performance of the random forest model, aligns with the findings of several studies conducted by Sharma et al [2], Ibidoja et al [11], Ibidoja et al [18], Mukhtar et al [38] and Yesilkanat [39].…”

“…Moving on, VIF and boxplot analysis are the techniques that will be utilized to identify the significant parameters exhibiting heterogeneity. Ibidoja et al [11], have employed the methods of variance inflation factor (VIF) and boxplot in their study to determine the heterogeneity parameters. Hence, these methods will be considered in this study as well.…”

“…Various factors contribute to heterogeneity, including differences in parameters and variations in units for temperature, relative humidity, wind, and solar radiation, as well as variability in variances. Examining this variability is essential to prevent inaccuracies in findings and conclusions, as it can result in inconsistent estimations and distort the overall findings [11]. Nunes et al [10], have also discovered that if heterogeneity is not accurately measured, then it is impossible to precisely determine its impact.…”

How Many Parameters are Needed to Represent Polar Sea Ice Surface Patterns and Heterogeneity?

“…where R 2 is the coefficient of determination, to detect multicolinearity of these heterogeneity parameters (see Ibidoja et al (2023)). For each metric X i , where i ∈ [1, ..., 22], the VIF was calculated over the regression equation…”