Predicting Mung Bean Germination Potential via Digital Imaging Analytics

1. Executive Summary

Quality control in the seed industry often relies on visual inspection or random sampling to determine germination rates. This case study evaluates the efficacy of the Vibe QM3i Grain Analyzer in predicting the germination potential of Mung beans. By analyzing specific visual properties- such as color and dimensions- and segregating seeds into distinct classifications, the study demonstrates a strong correlation between digital classification and biological viability. Notably, the system successfully identified and isolated a seed class with a 0% germination rate, highlighting the technology's potential for non-invasive, high throughput  seed sorting and quality assurance.

2. Introduction

The gap between human visual inspection and digital inspection often leads to inconsistencies in seed grading. While a sample may appear uniform to the naked eye, digital imaging can detect subtle variations in color, texture, and size that correlate with seed health.

The objective of this experiment was to determine if the Vibe QM3 analysis algorithm could accurately predict which mung beans would germinate and which would not, based solely on imaging data prior to the sprouting process.

3. Methodology

3.1 Sample Analysis and Classification

A sample of Mung beans was analyzed using the Vibe QM3i Grain Analyzer. Using a proprietary discovery method to understand seed properties, the Vibe algorithm divided the sample into different color and size classes.

The image below demonstrates the Vibe QM3i’s ability to digitally separate the sample into classes (indicated by colored bounding boxes) based on measured properties.

Figure 1: Digital Classification-Vibe QM3 digital inspection showing the segregation of the sample into distinct classes based on visual properties.

Based on this digital analysis, four distinct test groups (10 seeds each) were selected based on the algorithm's confidence, alongside random control groups:

• M1 (Big / "Bad"): Seeds identified by the algorithm as having "Bad" properties.

• M2 (Big / "Good"): Seeds identified as "Good".

• M3 (Big / "Abnormal"): Seeds identified as "Abnormal".

• M4 (Small / "Good"): Smaller seeds identified as "Good".

• M5 & M6 (Control): Randomly picked seeds to establish a baseline.

  
  

3.2 Experimental Protocol

The selected seeds were placed in plastic containers and subjected to a home sprouting process involving soaking in water. All containers received identical treatment and were placed in the same location to ensure consistent environmental conditions.

Figure 2: Experimental Setup-The isolated seed groups placed in containers for the germination test.

4. Results

The experiment yielded significant variances in germination rates between the digitally classified groups. The germination progress was monitored over 7 days.

4.1 Quantitative Results

The final results observed on the 7th day are summarized below :

Group	Classification	Seed Size	Sprouted	Did Not Sprout	Outcome
M1	Bad	Big	0	10	0% Success. 2 seeds rotten.
M2	Good	Big	8	2	80% Success. Healthy sprouts.
M3	Abnormal	Big	5	5	50% Success. Issues with mold/color.
M4	Good	Small	5	5	50% Success. 5 seeds dormant.
M5	Random	Random	9	1	90% Success (Control).
M6	Random	Random	8	2	80% Success (Control).

4.2 Visual Evidence: The "Bad" vs. "Good" Class

The most striking finding was the difference between Group M1 (predicted "Bad") and Group M2 (predicted "Good").

Figure 3: Group M1 (Predicted Bad) – on the 7th day: Group M1 seeds failed to sprout and showed signs of rot, confirming the "Bad" classification.

Figure 4: Group M2 (Predicted Good) – on the 7th day: Group M2 seeds demonstrated strong germination and healthy sprout development.

5. Discussion & Conclusion

The results confirm the capability of the Vibe QM3 Grain Analyzer to predict germination outcomes before the physical process begins.

1. Detection of Non-Viable Seeds: The algorithm successfully identified a specific class of seeds (M1) that resulted in a 0% germination rate. Visual evidence confirmed these seeds were biologically compromised rather than just dormant.

2. Validation of "Good" Classes: The M2 group performed on par with the random control groups (M5, M6), confirming that the algorithm can verify high-quality seeds.

3. Abnormal Indicators: The M3 "Abnormal" group's susceptibility to mold suggests the imaging analysis detected surface fungal precursors or shell integrity issues invisible to the standard inspection.

By utilizing digital imaging analytics, operators can surgically segregate non-viable stock from premium stock, ensuring higher germination rates and product consistency.