Camera types used on agrobots: – RGB cameras – extensively used in agricultural machine vision applications for fruit/plant detection, yield prediction, segmentation tasks, disease detection, ripeness detection, weed detection and insects detection – Hyperspectral, thermal, or ultrasonic cameras – typically provide better results than conventional RGB color images – Stereoscopic cameras – provides 3D plant structure information (dimension of crops (depth information, crop height, leaf shape, leaf area etc.)) Critical factor is the camera’s resolution, which should be high enough to capture the details of the scene especially in the cases of insect and disease detection.
GPS for autonomous navigation in open-field cultivation environments Robotic data collection platform equipped with RADAR, liDAR, ultrasonic radar sensors, spectrometers, fluorometers
Some of the key types of sensors used in urban air quality monitoring include: Particulate Matter (PM) Sensors: These sensors measure concentrations of particulate matter Nitrogen Dioxide (NO2) Sensors: NO2 is a common urban pollutant, often produced by vehicle exhaust and industrial processes. Sulfur Dioxide (SO2) Sensors: Commonly produced by industrial processes, SO2 levels are often monitored using ultraviolet fluorescence or electrochemical sensors. Ozone (O3) Sensors: Ozone at ground level is a harmful pollutant, and its concentration is typically monitored using ultraviolet (UV) photometry or electrochemical cells. Carbon Monoxide (CO) Sensors: CO is a colorless, odorless gas resulting from incomplete combustion. It’s usually monitored in urban environments using electrochemical sensors. Volatile Organic Compounds (VOCs) Sensors: VOCs are emitted from a variety of sources, including vehicle exhaust, industrial processes, and consumer products. Meteorological Sensors: These sensors measure environmental conditions like temperature, humidity, wind speed, and wind direction, which are important for understanding and interpreting air quality data.
Wind Sensors (Anemometers): measure wind speed and direction, key factors in sandstorm formation. By understanding wind patterns, it becomes easier to predict the movement and intensity of sandstorms.
