INFIELD TEST 1: Insect counting through wearables and artificial intelligence
In the AgriWear Living Lab, core problems related to how work is carried out today in the agricultural sector are tackled. Part of the Living Lab is to couple wearable technologies with advanced knowledge from artificial intelligence. One such solution is our open source low cost Insect Counting System (ICS) that harnesses machine learning technology to save precious time. ICS is responsible for counting 6 types of insect (with the possibility to add more insect types easily without advance knowledge). The Living Lab created an easy to access and use interface for the system with the possibility to integrate this with existing workflows, This bears advantages for enterprises to save time and resources. The process is as simple as taking an image as demonstrated by Dominik Baum (scouting agent). As in the photo below, everything else is streamlined, processed and communicated to the workflow which needs the information the most.
INFIELD TEST 2: Pilot at Vitarom
Scouting is one of the essential processes in crop management to evaluate economic risk from pest infestations and disease and plant growth monitoring (Flaten, 2009). At Vitarom GmbH, the process usually takes 30 minutes to 2 hours to assess pest pressure and crop growth performance for every 10-11 tomato vines in the field. The job does not end there. Scouting agents have to manually transfer these data from paper into a plant registration sheet on the computer at their laboratory to create weekly reports.
In the AgriWear Living Lab, a platform is created with a seamless pipeline to help farmers produce actionable insights based on the agricultural data using wearable technology. For the pilot project, we have developed a scouting app on HoloLens 2 to help farmers and workers collect plant monitoring data such as plant growth data and pest assessment data in a simple way. The agent is only required to input the data once in the field and the reports will be created in real-time for time efficiency and early detection of crop diseases using augmented reality and artificial intelligence.