Traditional crop scouting methods (e.g., visual scouting, field sampling and laboratory analysis) are not rapid especially when conditions affecting the crop are too complicated to be identified using these techniques. Advanced sensor technologies can help in such scenarios.
A sensor is “a device that responds to a physical stimulus such as heat, light, sound, pressure, magnetism, or a particular motion, and transmits a resulting impulse for measurement or operating a control” (Merriam-Webster 2017). Typically, sensor data can be used to detect early signs of biotic (biological in nature) or abiotic (water, solar radiation, temperature, air quality) crop stress. When combined with various sampling methods (i.e., ground or aerial), sensor-extracted data can be useful to farmers for monitoring plant growth and health. It can also help in potential crop yield estimation.
The emergence of unmanned aerial systems (UAS) has enhanced possibilities of acquiring high-resolution multi-spectral (i.e., few spectral bands) images of agricultural fields at a temporal resolution controlled by the user. It can lead to easier and faster monitoring of large farms and agricultural decision making. UAS have evolved into an important technology in precision agriculture with multiple companies and agricultural service providers exploring how to integrate it into production management decision making. Sensors are an integral part of UAS technology for its meaningful and efficient use in agriculture.
Sensors that capture and record natural EM radiation coming from the sun and reflected from the objects are termed as passive sensors. Most optical sensors used in agricultural remote sensing often are of passive type. Active sensors are integrated with specific energy source to illuminate the object and record the response. For example, the light detection and ranging (LiDAR) and ultrasonic sensors, respectively, use infrared light and short sound pulses as energy sources to capture ToF data. For example, a LiDAR sensor (from SICK Inc., Germany) emits infrared light (905 nm) in the sensor FOV and measures the response as reflected light intensity with distance.
A range of active and passive sensors can be integrated with small UAS. Georeferenced and processed data from such sensors can be used for decision support in crop management. Overall, the integration of a typical sensor with a small UAS depends on the specific agricultural application and the UAS platform payload lift capabilities. The section on Sensor Types summarizes some of the key sensors that can be integrated with small UAS for agricultural crop sensing.