Using computer vision for analysis of plant growth condition:

what to consider? Hans Jørgen Andersen Computer Vision and

Media Technology laboratoryAalborg University

Background

►Lower prices of cameras opens new possibilities for sensor development

►Cameras – Computer Vision – used in industry normally takes place in a controlled environment

►Within agriculture this is often not feasible

Problem

►If the surrounding environment for image acquisition cannot be controlled

Then the computer vision system has to adjust to the environment

Outdoor Images of Wheat Plants

Sunshine, unclouded. Sunshine, clouded.

Skylight, clouded. Skylight.

If Spectra of Light Source Changes

Spectra of Reflected Light Changes

Spectral Variation of the Illumination

Light Sources

►Outdoor Condition poses the problem ofTwo Illumination sources The Sun and The Sky

Sunlit Sky light

How can you analysisthe green color ofthe vegetation ?

Characteristic of Reflections

►Sunlit condition may pose two reflection components: Highlight, i.e. the color of the sun / light source Body, i.e. the color of the plant / object

Plastic cup

With Highlight Pure Body reflection

Outdoor Image Formation

Light Sources

ObjectPlant

Transmittance

Absorption

Reflectance

ObserverCamera

Ambient

Point Uniform

Modeling of Daylight

BlackBody

T, Kelvin

Black Body spectra

Daylight may be modelled as a Black BodyCorrelated Colour Temperature, CCT

Daylight model spectra

Segmentation

Classifying Reflections

►Reflections from Coffee classified intoBody and Highlight Components

ProbabilityofBody Reflection

Use within Gap Fraction Estimation

Original Image Classifying each pixel asSoil (”gap”) = 0, Plant = 1

Multi - Spectral Images

►470-720 nm, 26 bands

Modeling Spectra

►Endmember Spectra Known (measured)

Classifying Reflections

►First-order body scatteringVegetation Soil

Classifying Reflections (2)Specular reflection Vegetation - Vegetation

Vegetation - Soil Soil - Soil

Conclusion

►Modeling the Image Formation Process: Is valuable for

• Robust segmentation• Analysis of vegetation growth status• Assessment of various reflection components

Perspectives

►Modeling of vegetation