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ACS-210The Crop Circle ACS-210 Sensor provides classic vegetative index data as well as basic reflectance information from plant canopies.

Information produced by the sensor can be utilized to quantify the impact of nutrients, water, disease or other growing conditions on plants or crops.

Unlike other radiometric light sensors, the Crop Circle ACS-210 is not limited by ambient lighting conditions— measurements can be made day or night due to its unique, patented (pending) light source technology.

For on-the-go applications, the Crop Circle ACS-210 sensor can be mounted to virtually any type of vehicle to remotely sense and/or map plant or crop canopy biomass while driving through a field. The compact size and low weight design allows Crop Circle to be easily adapted to pole-mounted and handheld applications.

Two sensor models are available providing yellow/NIR or red/NIR sensing capabilities.

Measurements Made Easy

Vegetative index data produced by the sensor can be easily captured using a laptop PC, PDA or other data acquisition device using a standard RS-232 interface.

To facilitate reading sensor data, a special serial data reader program has been included in the Crop Circle Evaluation Package. The S-Reader software is a custom application designed specifically to parse the output data stream produced by the Crop Circle ACS-210.

The software is menu driven and is extremely user friendly. S-Reader operates on an IBM PC compatible computer. Data is stored in a comma-delimited format for convenient access by third-party data analysis programs or spreadsheet software.

Key Sensor Features
» Easy-to-use
» Make measurements day or night
» Measurements not influenced by fluorescent or other AC light sources
» Wide measurement range—1 to 8 ft.
» Rugged—dust and water resistant
» Low noise performance
» Fast data output rate
» Low power operation
» Light weight—weighs less than 1lb.
» Networkable

handheld

Research Applications
» Crop response to nutrient and fertilizer studies
» Herbicide effect/ performance studies
» Noninvasive plant biomass quantification
» Trend/detect plant health changes
» Early disease detection
» Leaf senescence studies
» Turf and agricultural landscape mapping
» Hybrid selection
» Develop nutrient recommendations/models based on comparative local standards
» Foragable biomass prediction