A5062308492- Smart Agriculture and AI
- Remote Sensing in Agriculture
- Plant Pathogens and Fungal Diseases
- Spectroscopy and Chemometric Analyses
- 3D Surveying and Cultural Heritage
- Remote Sensing and LiDAR Applications
- Plant Disease Management Techniques
- Satellite Image Processing and Photogrammetry
- Postharvest Quality and Shelf Life Management
Ghent University
2022-2024
Flanders' Food (Belgium)
2022
The development of UAVs and multispectral cameras has led to remote sensing applications with unprecedented spatial resolution. However, uncertainty remains on the radiometric calibration process for converting raw images surface reflectance. Several methods exist, but advantages disadvantages each are not well understood. We performed an empirical analysis five different calibrating a 10-band camera, MicaSense RedEdge MX Dual Camera System, by comparing spectrometer measurements taken in...
Automatic detection of foliar diseases in potato fields, such as early blight caused by Alternaria solani, could allow farmers to reduce the application plant protection products while minimizing production losses. UAV-based, high resolution, NIR-sensitive cameras offer advantage a detailed top-down perspective, with high-contrast images ideally suited for detecting solani lesions. A field experiment was conducted 8 plots housing 256 infected plants which were monitored 6 times over 16-day...
Alternaria solani is the second most devastating foliar pathogen of potato crops worldwide, causing premature defoliation plants. This disease currently prevented through regular application detrimental crop protection products and guided by early warnings based on weather predictions visual observations farmers. To reduce use products, without additional production losses, it would be beneficial to able automatically detect in fields. In recent years, potential deep learning precision...
Potato cultivation is regularly affected by Alternaria solani, a destructive foliar pathogen causing early blight, premature defoliation of potato plants resulting in yield losses. Currently, treated through preventive application chemical crop protection productions, following warnings based on weather predictions and visual observations. Automatic detection could make the mapping blight more accurate, reducing production losses products. Current research explores potential deep learning...