# Integrated microwave cavity perturbation sensor for nondestructive estimation of moisture content of grains: A case study on wheat and chickpea

## TL;DR

A low-cost microwave sensor is developed to non-destructively estimate moisture in wheat and chickpea grains with high accuracy.

## Contribution

A custom circuit and machine learning models enable precise, cost-effective moisture estimation without expensive equipment.

## Key findings

- The BAG model achieved high correlation (R=0.995 for wheat, R=0.989 for chickpea) in predicting moisture content.
- k-NN models classified grain types with 100% accuracy.
- The system offers a rapid and cost-effective alternative to traditional methods.

## Abstract

This study presents a low-cost, integrated microwave cavity perturbation sensor operating in TM010 mode at 2.45 GHz for the non-destructive estimation of moisture content (MC) in wheat and chickpea grains. Unlike conventional methods that rely on expensive vector network analyzers (VNAs), this system uses a custom-designed circuit to derive a density-independent moisture content function, M(Ψ). Various machine learning models (ML) were trained to predict MC and classify grain types based on these dialectical metrics. The results demonstrate that bagging (BAG), k-nearest neighbors (k-NN), and reduced-error pruning trees (REPTree) models significantly outperform deep learning models. The BAG model achieved the highest predictive performance, yielding correlation coefficients (R) of 0.995 for wheat and 0.989 for chickpea, with root mean square error (RMSE) of 0.207% and 0.302%, respectively. Furthermore, k-NN variants achieved 100% accuracy in classifying the grain types. The proposed system offers a precise, rapid, and cost-effective alternative for real-time grain quality monitoring.