Robotic Weeding

Vision and objectives Novel weeding technologies that can reduce manual effort by % in organically grown sugar beets and vegetables and herbicide usage by % in high value crops.

Inter-row x x xx x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x Weeding Target Areas Close-to-crop Intra-row

Organization WP1 System definition and integration (DIAS + KVL) WP2 Seed geo-referencing (KVL) WP3 Computer vision for identification of crop seedlings (DIAS) WP4 Non-chemical weeding tools (KVL) WP5 Micro-spray system (DIAS) Duration: Budget: 6.4 Mio. DKK

RTK Seeder / Seed Mapping H.W. Griepentrog, KVL

Precision Seeder with RTK-GPS Monopill Optischer Sensor H.W. Griepentrog, KVL

Identification of weed and crop seedlings Training the software (ASM-Toolkit) CHEAL White Goosefoot Chenopodium Album) (ASM = Active Shape Model)

Image capture with API

Weeding Principles

Tillage Hoe, tine (finger/goosefoot), rotary tine/hoe Thermal Gas, radiation (UV-light), steam, laser Electric Electro-static Cutting Mowing Physical Weeding Principles Intra-row H.W. Griepentrog, KVL

Target area: Close-to-crop Seed position (measured, mapped) True plant position (measured, mapped) Close-to-crop area H.W. Griepentrog, KVL

Micro-spraying Biological & Agricultural Engineering, University of California, Davis

The goal To demonstrate an integration of crop- seed mapping, computer vision for identification crop and weed seedlings and highly accurate devices for mechanical and chemical targeting of weed seedlings