First ensure that the picture present the natural appearance or entity of the biological object.

Agreed and thank you,

A data picture must represent how the object appears naturally and without distortion or other artifact.

Depending on the optical perception acquisition approach selected, a resultant image may characterize and represent an environmental phenomena via a recorded pattern of energy, and in various degrees of approximation.

However, in order to accurately quantify a phenomena using imagery, many layered considerations come into play. Such as, will more than one image be needed? Or will multiple images be comparable?

Please find the following technical elements I think are important in the generation and interpretation of environmental imagery to include biological objects.

1. Camera detector type

2. Radiometric bands detected, their number and band-widths

3. Lensing structure and optical ray clarity

4. Camera distance and sampling resolution relative to a signal of interest

5. Photographic exposure settings, such as aperture, shutter speed, and ISO

6. Image capture rate, and file format as RAW or loss of information

7. Data calibrations, is there a control panel in image

8. Illumination, contrast, shade, diffuse solar is best

9. Camera position and view angle, GPS location, IMU accelerometry

10. EXIF meta information attached to each image file

11. Camera data throughput and storage

12. Image transfer for backup and compute

13. Geometric corrections

14. Radiometric corrections

15. NIR or hyperspectral options

16. Principal components analysis

17. HSB color space analysis

18. RGB color space analysis

19. Segmentations and clustering models

20. Change detections

A note regarding stitching imagery relative to analysis – Run analysis on raw images to avoid error. A tiled or stitched field image is valuable, and may serve to select individual representative images within a plot, however a processed image will contain more error than the original.

Imagery capture and analysis is growing quickly to become almost ubiquitous across the agricultural field space, especially regarding assessment of large fields for management, or the measuring of breeding trial plots. Therein commercial UAS platforms and cloud-based high throughput compute analysis function as a mobile device service available to producers.

So how do we delineate an appropriate theoretical standardization for imagery based environmental awareness?

As an example, an image is shown in the Maricopa Phenotyping Project page, where green pixels are selected as a basis of estimating cover from proximal terrestrial consumer grade based imagery.

https://www.researchgate.net/project/Maricopa-Phenotyping/update/5c4547b0cfe4a7645512b291

Please add your important and valued ideas, comments or corrections to this discussion space.

Please find a few references available on RG:

Article High-Throughput Phenotyping of Crop Water Use Efficiency via...

Preprint Estimating Phenotypic Traits From UAV Based RGB Imagery

Article UAV-Based High Resolution Thermal Imaging for Vegetation Mon...

Article Rapeseed Seedling Stand Counting and Seeding Performance Eva...

Article Ear density estimation from high resolution RGB imagery usin...

Article Quantitative Estimation of Wheat Phenotyping Traits Using Gr...

Poster Grassland monitoring and phenotyping based on UAV-imagery

Article Multi-modality Imagery Database for Plant Phenotyping