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MSU

Graduate Research Assistantship in Biosystems Engineering for UAV Image Analysis and Machine Learning

Mississippi State University Department of Agricultural and Biological Engineering
Self-funded 🎓 Computer Science machine learning sensor fusion precision agriculture uav image analysis soil spectroscopy data mining agricultural engineering

Explore machine learning and UAV image analysis in agricultural engineering for a USDA-ARS funded project. Develop programming skills and contribute to advancing precision farming techniques while collaborating in a multidisciplinary environment.

AI-generated overview

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Why This Research Matters

This research facilitates the development of innovative nondestructive techniques for soil and crop monitoring, essential for sustainable agricultural practices. It supports improved crop management under climate stressors such as drought, enhancing food security and environmental stewardship.

Data mining Soil/Plant spectroscopy Precision agriculture Sensors and controls

Project Description

Project Overview

This Graduate Research Assistantship offers a MS-level position within the Department of Agricultural and Biological Engineering at Mississippi State University. Supported by USDA-ARS funding, the research concentrates on applying UAV image analysis, programming, and machine learning for improving agricultural systems and soil property assessments.

What You Will Do

The student will engage in UAV image data processing and machine learning model development using programming languages such as Python, R, and MATLAB. Collaboration across disciplines is encouraged, and the student will contribute to peer-reviewed journals and conferences.

Expected Outcomes

The research aims to create robust predictive models for soil and crop properties, improving precision agriculture monitoring and decision-making. Successful completion will result in publications and knowledge advancement in biosystems engineering.

Why This Matters

With challenges in sustainable agriculture and soil health assessment, this work supports developing innovative tools for nondestructive soil and plant monitoring to aid better crop management under varying environmental stresses, including drought.

Entry Requirements

Bachelor's degree in Agricultural and Biological Engineering, Biosystems Engineering, Computer Science, or related field with GPA 3.0 or higher. Must be a US Citizen.

How to Apply

Interested applicants are encouraged to contact Dr. Nuwan Wijewardane directly via email at nuwanw@abe.msstate.edu to schedule interviews.

Eligibility

UK/Home
EU
International

Supervisor Profile

DN
Dr. Nuwan Wijewardane
Mississippi State University, Department of Agricultural and Biological Engineering
1786 Citations
22 h-index
Google Scholar

Dr. Nuwan Wijewardane is an Assistant Professor at Mississippi State University's Department of Agricultural and Biological Engineering. His research focuses on data mining, soil and plant spectroscopy, precision agriculture, and sensor development. With over 1700 citations and an h-index of 22, he contributes widely to advancing soil property prediction and drought resilience in crops through interdisciplinary approaches.

Key Publications

2018 156 citations
Predicting physical and chemical properties of US soils with a mid‐infrared reflectance spectral library
2023 155 citations
Resilience of soybean cultivars to drought stress during flowering and early-seed setting stages
2022 150 citations
High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion
2016 150 citations
Moisture insensitive prediction of soil properties from VNIR reflectance spectra based on external parameter orthogonalization
2016 130 citations
Prediction of soil carbon in the conterminous United States: visible and near infrared reflectance spectroscopy analysis of the rapid carbon assessment project

Research Contributions

Developed methods to predict soil physical and chemical properties using mid-infrared reflectance spectral libraries.
This advances soil science by providing rapid, nondestructive soil assessment tools that support better agricultural practices.
Studied the resilience of soybean cultivars to drought stress during critical growth stages.
Helps improve crop breeding and management strategies to enhance drought tolerance and agricultural productivity.
Applied high throughput imaging and sensor fusion techniques to analyze leaf chlorophyll content in crops like sorghum.
Enables rapid phenotyping for crop health monitoring, facilitating precision agriculture and improved crop management.
Used visible and near-infrared reflectance spectroscopy for moisture insensitive prediction of soil properties.
Improves the accuracy and reliability of soil property estimation under varying moisture conditions, benefiting soil management.

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