AI in Agriculture is a topic of crucial importance for the European Union. Several initiatives are taken in that area that is of growing importance in the context of Agriculture 4.0 and the future of farming. This section provides various entry-points on existing projects and pilots related to this topic.
This service consists of AI-driven weed detection, aimed at identifying and managing weeds in agriculture, significantly reducing crop loss and promoting sustainable farming practices.
The presented service offers insect detection and counting, enhancing agricultural management and environmental sustainability through innovative AI technology.
This service uses AI for precise fruit disease detection and fruit counting, enhancing agricultural productivity, optimizing crop management, and promoting good practices in modern agriculture.
Introducing our visualization tool, we leverage the capability to transform UAV footage into detailed 3D models of agricultural environments. This innovation is pivotal in enabling precise measurement and monitoring of crops, marking a significant advance...
This application enhances agricultural safety by facilitating collaboration between human operators and autonomous robots on the same land sections simultaneously. Addressing the increasing need for robotic solutions in agriculture due to labor shortages ...
This application enhances agricultural safety by facilitating collaboration between human operators and autonomous robots on the same land sections simultaneously. Addressing the increasing need for robotic solutions in agriculture due to labor shortages ...
This application enhances agricultural safety by facilitating collaboration between human operators and autonomous robots on the same land sections simultaneously. Addressing the increasing need for robotic solutions in agriculture due to labor shortages ...
The model was designed for tree-crops semantic-segmentation and created using the AI4Copernicus service: “Deep network for pixel-level classification of S2 patches"
The dataset was designed for prediction of vegetation health on Sentinel-2 imagery and utilized in the AI4Copernicus service: “Long Short-Term Memory Neural Network for NDVI prediction" in which an LSTM neural network was trained in order to create an AI ...
The dataset was designed for semantic-segmentation-based deep learning models and utilized in the AI4Copernicus service: “Deep network for pixel-level classification of S2 patches" in which a U-net neural network was used in order to create an AI model fo...
