Towards In-situ Based Printed Sensor Systems for Real-Time Soil-Root Nutrient Monitoring and Prediction with Polynomial Regression

Authors

Lamar K. Burton, Florida International University Department of Electrical and Computer EngineeringFollow

Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Electrical Engineering

First Advisor's Name

Shekhar Bhansali

First Advisor's Committee Title

Co-Committee Chair

Second Advisor's Name

Krish Jayachandran

Second Advisor's Committee Title

Co-Committee Chair

Third Advisor's Name

Nezih Pala

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Wujie Wen

Fourth Advisor's Committee Title

Committee Member

Fifth Advisor's Name

Arif Sarwat

Fifth Advisor's Committee Title

Committee Member

Keywords

Soil nutrient sensor, printed sensor, printed electronics, plant sensor pod

Date of Defense

3-27-2020

Abstract

This dissertation explores how to increase sensor density in the agricultural framework using low-cost sensors, while also managing major bottlenecks preventing their full commercial adoption for agriculture, accuracy and drift. It also investigated whether low-cost biodegradable printed sensor sheets can result in improved stability, accuracy or drift for use in precision agriculture. In this dissertation, multiple electrode systems were investigated with much of the work focused on printed carbon graphene electrodes (with and without nanoparticles). The sensors were used in two configurations: 1) in varying soil to understand sensor degradation and the effect of environment on sensors, and 2) in plant pod systems to understand growth. It was established that 3) the sensor drift can be controlled and predicted 2) the fabricated low-cost sensors work as well as commercial sensors, and 3) these sensors were then successfully validated in the pod platform. A standardized testing system was developed to investigate soil physicochemical effects on the modified nutrient sensors through a series of controlled experiments. The construct was theoretically modeled and the sensor data was matched to the models. Supervised machine learning algorithms were used to predict sensor responses. Further models produced actionable insight which allowed us to identify a) the minimal amounts of irrigation required and b) optimal time after applying irrigation or rainfall event before achieving accurate sensor readings, both with respect to sensor depth placement within the soil matrix. The pore-scale behavior of solute transport through different depths within the sandy soil matrix was further simulated using COMSOL Multi-physics. This work leads to promising disposable printed systems for precision agriculture.

Identifier

FIDC008953

ORCID

https://orcid.org/0000-0001-9852-2082

Recommended Citation

Burton, Lamar K., "Towards In-situ Based Printed Sensor Systems for Real-Time Soil-Root Nutrient Monitoring and Prediction with Polynomial Regression" (2020). FIU Electronic Theses and Dissertations. 4394.
https://digitalcommons.fiu.edu/etd/4394