The Challenge:
It is critical for farmers and agriculture researchers to identify problems that affect crop productivity in order to ensure maximum yields and quality for their crops. Standard monitoring techniques consist of in-field work to identify criteria such as moisture deficiencies, pests and other types of infestations, or weather-related crop damage. However, these monitoring techniques are time consuming and can delay remediation efforts, ultimately affecting overall results. Without timely inspections, it is difficult for farmers to implement solutions such as pesticide or fertilizer application in order to improve land productivity and control costs.

The Solution:
Detecting damage and monitoring crop health can now be accomplished through remote sensing using multispectral imaging technology to acquire high-resolution images. The AISA Airborne Hyperspectral System provides a real-time solution for monitoring crop vigour and disease treatment, resulting in high-value crops. AISA is an accomplished detection and mapping tool that farmers, cultivation managers and agricultural agencies can use to monitor the condition of vegetation, classify crops, identify potential land yield and check soil conditions for potential problems such as moisture deficiencies. In the image above, the green areas depict the improved biomass of crops after a fungicide toxicant was applied.

The Tools Used:
AISA hyperspectral sensor head (400 to 970 nm and/or 1000 to 2500 nm)
GPS/INS Unit
Data acquisition Unit
Laptop computer

The Difference It Made:
Thanks to AISA farmers can now improve their yields, reduce operation costs and help minimize the impact to the environment by optimizing pesticide and herbicide use. Although remote sensing doesn't entirely replace traditional in-field monitoring, it allows growers to give immediate attention to areas in need and later monitor the treatment's success. The fast turnaround times achieved with hyperspectral imaging brings crop management to a whole new level.

Unsupervised Classification	Supervised Classification	Relative Biomass
Photos courtesy of Spectrum Mapping, LLC