Ultra-High-Resolution UAV-Based Detection of Alternaria solani Infections in Potato Fields

Ruben Van De Vijver, Koen Mertens, Kurt Heungens, David Nuyttens, Jana Wieme, Wouter H. Maes, Jonathan Van Beek, Ben Somers, Wouter Saeys

Onderzoeksoutput: Bijdrage aan tijdschriftA1: Web of Science-artikelpeer review


Automatic detection of foliar diseases in potato fields, such as early blight caused by Alternaria solani, could allow farmers to reduce the application of plant protection products while minimizing production losses. UAV-based, high resolution, NIR-sensitive cameras offer the advantage of a detailed top-down perspective, with high-contrast images ideally suited for detecting Alternaria solani lesions. A field experiment was conducted with 8 plots housing 256 infected plants which were monitored 6 times over a 16-day period with a UAV. A modified RGB camera, sensitive to NIR, was combined with a superzoom lens to obtain ultra-high-resolution images with a spatial resolution of 0.3 mm/px. More than 15,000 lesions were annotated with points in two full size images corresponding to 1250 cropped tiles of 256 by 256 pixels. A deep learning U-Net model was trained to predict the density of Alternaria solani lesions for every pixel. In this way, density maps were calculated to indicate disease hotspots as a guide for the farmer.
Oorspronkelijke taalEngels
Artikel nummer6232
TijdschriftRemote Sensing
PublicatiestatusGepubliceerd - 9-dec-2022


  • U-Net
  • deep learning
  • drones
  • potato crops
  • precision farming
  • supervised


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