Computers perceive the world using sensors, devices that detect inputs and converts them into signals suitable for processing. Computers learn from and process data to make predictions from this information. When combined together, sensor fusion provides a more accurate view of the environment.
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Machine learning empowers computers to learn to program themselves and interpret things the way humans do. However, instead of writing code, data is fed into an algorithm, which builds logic based on that information. Machine learning tools analyze data sets looking for patterns. Those patterns are stored as model and can be used to make predictions about the data set. The predictions are tested and the model is tuned to be made more accurate with each successive pass.
See how image classifications can be used for planet health
The patterns identified in images often begin with an analysis of attributes like textures, colors, and shapes. Once identified, the attribute can be classified and labeled, enabling the training of the model. Computer vision is being deployed in agriculture to monitor plant health, determining harvest readiness and keep track of cows with facial detection.
Click the button that best describes the pixelated image.
Healthy Leaves shine with a rich, dark green color when adequately fed.
Phosphorus (phosphate) shortage marks leaves with reddish purple, particularly on young plants.
Potassium (potash) deficiency appears as a firing or drying along the tips and edges of lowest leaves.
Nitrogen hunger sign is yellowing that starts at tip and moves along middle of leaf.
Magnesium deficiency causes whitish stripes along the veins and often a purplish color on the underside of the lower leaves.
Drought causes corn plants to have a grayish green color; leaves may roll up to the size of a pencil.
Disease helminthosporium blight, starts in small spots, gradually spreads across leaf.
Chemicals may sometimes burn tip, edges of leaves, and at other contacts. Tissue dies, leaf becomes whitecap.
Corn Plant Leaves: Burton, L. DeVere, (2019) Agriscience, Fundamentals and Applications (6th ed), (p 194). Boston, MA. Cengage.
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