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Agriculture

The images on this page contain data collected with a Spectra-View® system. They pertain to agriculture but are not limited to it. Ground cover classification, predicting crop yields, spotting differences in fields due to climatic conditions, soil conditions, vegetation analysis, stress and growth rates, pest impacts, and irrigation needs are only some of the possible agricultural applications. One of the newest applications for high resolution multi-spectral imaging is precision farming whereby crop consultants can analyze the data and identify fields that are infected with disease or insects, in need of fertilization or irrigation, or under chemical stress.

Irrigation Needs

The false color image to the left is an example of a center pivots potato field. Note the sprayer skips. Also detectable within the image are weeds. The green identifies unirrigated ground. This type of data can help in predicting crop yield and trouble spots for improving crop yield.

Identify Disease & Insect Infestation

The two images to the right are true-color and false-color images of a citrus field in Florida. In this example one can quickly assess differences in vegetation vigor. Detecting plant stress and growth rate is also possible, as is identification of trees infected with disease or insects.

The image to the left is an example of an auto-classified false-color image. This is the process where pixels within the image are grouped together with like pixels with the same spectral characteristics and are identified as a category. Using tightly geo-referenced Spectra-View® data for image classification allows for more accurately positioning a given feature on the ground. This process is an invaluable tool for unlimited applications.

PINK=Citrus Trees
BLUE=Bare Ground
GREEN=Bare Ground (different soil type)

Identifying Chemical Saturation

To the left is an image of a sugar beet field located in Southwestern Minnesota. This data was used to determine the sugar content in sugar beets.

Identifying Soil Condition

This data could be used for spotting differences in fields due to climatic conditions. Even small differences in soil condition or vegetation could be used to identify possible problem areas. This kind of data can aid farmers in making the best possible choices available.