Intelligent Robotic System for Digital Agriculture

GreenBotics project aims to make a significant step in Precision Agriculture by integrating sensing, field robotics, probabilistic machine learning and agriculture-science to develop a new soil-moisture monitoring system for crop plantations (namely Maize). The system is called Maize and Moisture Monitoring System (M3Sys). Based upon anovel multimodal spatio-temporal probabilistic inference framework, M3Sys will incorporate fundamental and applied techniques from machine learning, field robotics, drone andsatellite sensing, probability and sensor-fusion. Concisely, GreenBotics has the goal of increasing the precision and reliability of early anomalies detection and monitoring of Maize plantations by combining robotics, datasensor, ML, and agriculture expertise, both experimental and computational .


  • Build data-driven models based on relevant parameters related to the Maize stages and the external cues (T1)

  • Develop an integrated remote sensing processing framework using higher-order spatio-temporal filtering (T2)

  • Provide a flexible robotic sensor-network to collect field (ground) data (T3)

  • Endow the GreenBotics framework with probabilistic ML capabilities (T4)

  • Develop a probabilistic data-fusion system combining local, remote and contextual information (T5)

Short paper