I. Application Background

Pollen analysis plays a significant role in fields such as allergen identification and botanical research. Traditional methods rely on manual microscopic observation, which is inefficient and highly subjective. By leveraging machine vision and AI model training, automatic pollen identification can be achieved, significantly improving both detection efficiency and accuracy.

II. Testing Challenges

Pollen grains vary in size and shape. Testing requirements:

1. Observe pollen density and the morphological structure of large grains under a 2mm field of view

2. Examine the details of small grains under a narrow field of view

3. Collect high-quality images for AI model training to ultimately achieve automatic classification and recognition

III. Test Samples

The sample tested in this study was lily pollen, which was pale yellow in color, consisting of individual ellipsoidal particles that were uniform in size and free of clumps, with a particle size of approximately 0.2 μm (as indicated on the label).

IV. Testing Plan

• Camera: 4K HD industrial camera

• Tubus: 1x tubus

• Lens: 12.5x ZOOM LENS + 10x APO objective lens

• Light source: Flat-bottom light source

V. Testing Process

Place the pollen sample on a glass slide, adjust the working distance and focal length, and use a ZOOM LENS to magnify the sample step by step from low to high magnification to clearly examine the morphology of pollen grains of different sizes, then capture the images.

VI. Equipment Advantages

1. High resolution: No color fringing; clear image across the entire field of view

2. Easy zoom adjustment: Distinct details at low magnification; minimal distortion at high magnification

3. Precise measurement: Consistent magnification across the entire object plane; more accurate measurement of particle size, circumference, and area

4. User-friendly operation: Optimal working distance; easy to adjust

5. True-to-life imaging: True-to-life colors; sharp edges; no noticeable chromatic aberration