Research on UAV-Based Disease Detection
Overview
This collection of research explores advanced UAV-based technologies combined with hyperspectral sensing and machine learning techniques to detect powdery mildew in vineyards. The studies aim to improve early disease detection, enhance vineyard management efficiency, and promote scalable real-world applications of aerial sensing.
Investigating the Potential of UAV‑Based Hyperspectral Sensor in Detecting Powdery Mildew in Grapes
Authors: Michael Acosta, Jonathan Pena, Ardavan Sherafat, Cristobal Gonzalez, et al.
Published: June 2024
Conference: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IX
Organizer: SPIE – The International Society for Optics and Photonics
DOI: 10.1117/12.3014667This paper explores using UAV-mounted hyperspectral sensors to detect powdery mildew on grapevine leaves. It demonstrates the viability of early disease detection to support more timely and effective vineyard management.
Machine-Learning Techniques for the Detection of Powdery Mildew in Vineyards Using Aerial and Ground Imageries
Authors: Michael D. Acosta, Mahakbhai S. Patel, Amar Raheja, Cristobal C. Gonzalez, et al.
Published: May 2025
Conference: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X
Organizer: SPIE – The International Society for Optics and Photonics
DOI: 10.1117/12.3066315Building on previous work, this paper introduces machine learning algorithms to process aerial and ground imagery for powdery mildew detection. The integration of these technologies enhances detection accuracy and scalability in real-world vineyard conditions.
Presentation Materials
View or download the presentation slides from my Master's Thesis Defense in 2024.