In this work package, we explore possible futures for ‘smart’ arable mixed cropping systems. The goal is to think about what the future of arable agriculture could plausibly look like and how to get there. We do this in collaboration with relevant stakeholders. The visions we will develop are based on current trends and developments [ … ]
Arable
Workpackage 2A – Recognition and control of diseases and pests
The aim is to make crop protection more sustainable through the use of (digital) knowledge and techniques. Bayer and WPR want to develop and validate applications on the Farm of the Future (FotT) Field Lab (www.farmofthefuture.nl) and find out how techniques can be incorporated into new cultivation systems. The main three topics are: 1. Incorporating [ … ]
Workpackage 2B – Disease detection in seed potatoes
The main aim is to develop a robust system for the detection of bacterial and virus diseases in seed potatoes. We work on better algorithms to make the detection possible in different potato varieties. Besides focusing on specific for different PVY viruses (primary/secondary) we will also work on bacterial infections (3 types of Erwinia). Kverneland [ … ]
Workpackage 2C – Weed control with vision and deep learning
The control of potato volunteers in succeeding crops is a major problem in arable farming. These remaining plants propagate nematodes in the soil and can transmit diseases like late blight. Control requires a lot of manpower and is therefore costly. The project first focuses on optimal detection of these plants by training a deep learning [ … ]
Workpackage 2D – Recognition and classification of natural areas near parcels
The aim is to automatically detect and classify vegetations that contribute to nature-inclusive agriculture. The detection is based on images collected with drone cameras. Orphiction, WPR and WLR want to develop detection algorithms with artificial intelligence tools in order to assess vegetations in agricultural areas (pastures and arable) on opportunities for birds. The main three [ … ]
Workpackage 2E – Locating and protecting animals and nests in parcels
The aim is automatic detection of bird nests in pasture. The detection is based on image material collected with drone cameras. Orphiction and WR want to develop detection algorithms with artificial intelligence tools in order to detect and map the nests of important meadow birds in meadows. The main three topics are: 1. Collecting and [ … ]
Workpackage 3 – Design and test of small scale infrastructure at farm level for IT and power supply
The implementation of mixed farming concepts at farm level require a next level infrastructure for the managements of the operations and will include information technology and power supply. In these concepts monitoring activities will be done by sensing equipment, producing big quantities of data. Most data will be geo referenced. These data will be stored, [ … ]