{"id":6243,"date":"2025-04-03T09:29:32","date_gmt":"2025-04-03T07:29:32","guid":{"rendered":"https:\/\/www.kis.si\/about-the-institute\/infrastructure\/laboratory-for-remote-sensing-in-plant-protection\/"},"modified":"2026-09-18T12:28:15","modified_gmt":"2026-09-18T10:28:15","slug":"laboratory-for-remote-sensing-in-plant-protection","status":"publish","type":"page","link":"https:\/\/www.kis.si\/en\/about-the-institute\/infrastructure\/laboratory-for-remote-sensing-in-plant-protection\/","title":{"rendered":"Laboratory for Remote Sensing in Plant Protection"},"content":{"rendered":"\n

As part of our professional and research work in the field of plant protection, we develop <\/span>remote sensing methods<\/b> for the <\/span>early detection of plant diseases or pests<\/b> and the determination of <\/span>plant health status<\/b>, high-throughput <\/span>phenotyping<\/b>, chemometric analysis of plants and plant material, and <\/span>analysis and modeling <\/b>of the spread of individual pests using GIS tools.<\/span><\/p>\n\n

By analyzing <\/span>aerial photographs<\/b> (taken from planes and drones) and <\/span>satellite images<\/b>, we can much more effectively distinguish between plant species, determine their health status, and identify the presence of plant diseases and pests, as well as other stress factors (drought stress, nutritional disorders, etc.).<\/span><\/p>\n\n

We use <\/span>various sensors<\/b> (RGB, multi- and hyperspectral, LiDAR) <\/span>on different platforms<\/b> (in the lab and greenhouse, drones, ultralight aircraft, satellites) to detect diseases or pests before visible symptoms develop\u00a0 and take timely and spatially accurate action.<\/span><\/p>\n\n

We are <\/span>operators of a fleet of unmanned aerial vehicles<\/b> in the specific category. In our work, we use multiple <\/span>hyperspectral cameras<\/b> for laboratory and aerial imaging and high-resolution <\/span>multispectral satellite imagery<\/b> (WorldView 2 and 3, Sentinel 2 and others).<\/span><\/p>\n\n

In addition to classical statistical methods (e.g., linear mixed models and multivariate statistical methods), we also perform analyses of large amounts of data using <\/span>machine learning methods<\/b> (e.g., deep neural networks). We are also developing our own software for <\/span>analyzing hyperspectral images<\/b> in time series<\/b> (SiaPy).<\/span><\/p>\n\n

Research work<\/b><\/h2>\n\n