TEBAKA: Territorial Basic Knowledge Acquisition. An Agritech Project for Italy: Results on Self-Supervised Semantic Segmentation

Emerging technologies such as remote sensing from satellites and drones, internet of things (IoT), deep learning models, etc, could all be utilized to make informed and smart decisions aimed to increase crop production. We provide an overview of TEBAKA, an Italian national project on Smart Farming and discuss its relevance in the overall scenario of similar projects. We emphasize the project originality, in particular the research activity on new data-driven ML models that better extract relevant knowledge from observations. We presented the task of image semantic segmentation of olive trees or rows of grape plants and show an original self-supervised deep learning network that produce the segmentation with high accuracy. Furthermore, we discuss some idea that would be part of the project activities for the next year.