Thesis subject

MSc thesis topic: Potato disease identification with UAV remote sensing

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.

The capabilities of remote sensing technologies for detecting stress responses in vegetation have been widely demonstrated with different modalities providing information on different physiological responses, i.e. hyperspectral imagery is sensitive to changes in leaf biochemical properties, thermography to alterations in transpiration, and LiDAR to structural changes. Combining information from different modalities has therefore the potential to enhance the ability to quantify the stress level and diagnose its cause.

The project will investigate disease detection capabilities of multimodal UAV remote sensing. The focus will be on Erwinia bacterial infection in seed potatoes that causes stunted growth, chlorosis and wilting symptoms, stem

rot or even plant destruction, consequently leading to economic losses to the grower through lowering of the quantity and quality of the potato yield. A range of UAV-borne data (hyperspectral, thermal, LiDAR, RGB) is available for exploration as part of the thesis.

Relevance to research/projects at GRS or other groups

This topic is aligned with the AGROS project that aims to develop tools that can support sustainable agricultural production.

Objectives

  • Review literature on the topic of UAV disease detection
  • Design data analysis workflow Investigate plant-level responses to infection onset
  • Evaluate how well diseased potatoes can be identified UAV 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.
  • Rodriguez, J. et al. 2021 Assessment of potato late blight from UAV-based multispectral imagery. Comput. Electron. Agric., 184, 106061.
  • Francesconi, S. et al. 2021. UAV-Based Thermal, RGB Imagi

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