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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.

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 Turbine sensors are used to continually assess acceleration, temperature and vibration. Turbine impact sensors – for monitoring avian and bat collisions Turbine vibration sensors – Vibration sensors provide data that enables predictive maintenance, allowing operators to manage assets at a distance – Turbine – Because of variable wind speeds and frequent braking, the load is never consistent on the turbine, causing a lot of wear on the moving parts. Bearings are the biggest culprit in gearbox failure. When bearings fail, it usually leads to other components, such as gearwheels, breaking down, causing a domino effect of failure across the entire apparatus. One of the biggest issues with regard to bearing failure is lubrication starvation. Vibration sensors can help an operator stay ahead of lubrication issues by detecting subtle friction changes -Blade – Wear and tear on rotor blades come from high winds, lightning, ice, and extreme weather conditions that result in blade imbalance. Over time, these factors lead to cracking and fractures along the edges and pitch system failure. Wireless vibration sensors make it feasible to remotely monitor such conditions, alerting operators to impending failure and maintenance needs without physically accessing the site. These sensors are combined together into one communication channel. Associated KPI’s are considered in the aggregate.