Thesis subject

MSc thesis topic: Disease detection in wheat with UAV multimodal data

Early and accurate detection of disease emergence in crops is key for the reduction of both qualitative and quantitative losses in yield. UAV-based monitoring can enable rapid and detailed assessments of crop health status and guide farmers on appropriate management interventions.

Disease detection with UAVs has so far largely focused on optical domain. However, a combination of different remote sensing technologies has high potential for improving detection capabilities by providing complementary information on different physiological responses of plants.

This project will investigate disease detection capabilities of UAV hyperspectral and thermal imaging. The focus will be on Fusarium head blight infection, which is one of the most destructive diseases leading to 10–70% of yield loss. Additionally, it causes contamination of the grains with mycotoxins that can lead to animal and human health problems. Timely and accurate detection of Fusarium head blight would contribute towards improved food and feed safety, and sustainable agricultural practices through reduction of fungicide use.

Relevance to research/projects

This topic is aligned with the ToxinImage project that aims to predict on-site mycotoxin contamination in cereal grains.

Objectives

  • Review literature on the topic of UAV disease detection
  • Design image analysis workflow
  • Investigate wheat’s spectral and thermal response to infection
  • Evaluate how well wheat head blight can be identified with multimodal remote sensing approaches

Literature

  • Maes, W. H. et al. 2019. Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture. Trends in Plant Science, 24, 152-164.
  • Barbedo, J. G. A. 2019. A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses. Drones, 3, 40.
  • Francesconi, S. et al. 2021. UAV-Based Thermal, RGB Imaging and Gene Expression Analysis Allowed Detection of Fusarium Head Blight and Gave New Insights Into the Physiological Responses to the Disease in Durum Wheat. Frontiers in Plant Science, 12.
  • Xiao, Y. et al. 2021. Wheat Fusarium Head Blight Detection Using UAV-Based Spectral and Texture Features in Optimal Window Size. Remote Sensing, 13, 2437.

Requirements

  • Completion of Advanced Earth Observation (GRS-32306)
  • Option to join fieldwork activities (late June-early July 2022)

Theme(s): Sensing & measuring; Integrated Land Monitoring