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UOM

PhD Position in Smart and Digital Agriculture Using Integrated Sensing and AI

University of Manitoba Department of Biosystems Engineering
Self-funded ⏰ Closing Soon 🎓 Computer Science machine learning smart agriculture digital agriculture crop monitoring sensing systems drone technology precision agriculture data modeling

Explore how integrated sensing and AI can improve crop monitoring and management. Develop innovative digital agriculture tools to enhance food security and sustainability in farming systems.

AI-generated overview

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Why This Research Matters

This research advances sustainable farming by providing precise, data-driven tools that optimize resource use and increase crop productivity. By integrating AI with sensing technologies, it addresses global challenges of food security and environmental conservation in agriculture.

Digital Agriculture Proximal & Remote Sensing Multimodal Sensors Knowledge-guided ML

Project Description

Project Overview

This PhD project focuses on Smart and Digital Agriculture, leveraging sensing systems, artificial intelligence, and data analytics to optimize agricultural operations. Research includes developing integrated ground and aerial sensing platforms, and advancing machine learning for robust crop prediction.

What You Will Do

Develop integrated sensing systems using drones, robots, and IoT devices for high-throughput crop monitoring. Advance knowledge-guided machine learning by combining AI with physics-based models to create accurate crop prediction tools.
Design intelligent decision-making platforms for precision management of fertilization, irrigation, and harvesting operations.

Expected Outcomes

Creation of scalable sensing technologies and intelligent platforms that enable precise crop monitoring and management.
Improved prediction accuracy of crop performance using hybrid AI-physics models.
Enhanced sustainability and profitability in agriculture through precise resource management.

Why This Matters

With global population growth and climate change, efficient and sustainable agricultural practices are critical. This research advances technologies that improve food security, minimize environmental impact, and support resilient farming systems, addressing pressing global challenges.

Entry Requirements

Master’s degree in Agricultural and Biological Engineering, Biosystems Engineering, Computer Science, Electrical Engineering, Mechanical Engineering, or related fields. Strong programming skills in Python, R, Matlab, or C++. Experience or knowledge in machine learning, deep learning, and data modeling. Familiarity with agricultural or crop production systems preferred. Skills in multi-modal sensing and mechatronics/robotics are highly valued. Must meet University of Manitoba Faculty of Graduate Studies admission and English language requirements.

How to Apply

Applications should be submitted by May 20, 2026, for best consideration. Online interviews will be conducted for shortlisted candidates.

Eligibility

UK/Home
EU
International

Supervisor Profile

DJ
Dr. Jiating Li
University of Manitoba, Department of Biosystems Engineering
Google Scholar

Dr. Jiating Li leads interdisciplinary research in smart agriculture, combining engineering, data science, and agronomy. His group develops integrated sensing platforms and advanced AI models to improve crop monitoring and decision-making. Known for pioneering knowledge-guided machine learning techniques in agricultural applications, his work bridges academia and industry.

Key Publications

2020 241 citations
Comparison of object detection and patch-based classification deep learning models on mid-to late-season weed detection in UAV imagery
2018 157 citations
Wheat height estimation using LiDAR in comparison to ultrasonic sensor and UAS
2018 128 citations
Elucidating sorghum biomass, nitrogen and chlorophyll contents with spectral and morphological traits derived from unmanned aircraft system
2017 77 citations
Prediction of egg storage time and yolk index based on electronic nose combined with chemometric methods
2019 58 citations
Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Research Contributions

Developed and compared deep learning models for UAV-based late-season weed detection, improving object detection accuracy in agricultural fields.
Enables more precise and automated weed management, potentially reducing herbicide use and increasing crop yields.
Utilized LiDAR and UAV technologies to estimate wheat height accurately compared to ultrasonic sensors.
Supports high-throughput phenotyping and precision agriculture by providing reliable crop height measurements.
Elucidated relationships between sorghum biomass, nitrogen, and chlorophyll contents with spectral and morphological traits derived from UAV data.
Enhances crop monitoring and management for optimized growth and resource use efficiency.
Developed chemometric methods combined with electronic nose technology to predict egg storage time and quality indices.
Provides a novel non-destructive approach for food freshness assessment and quality control.

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