Seed Purity – Differentiation of alfa-alfa and sweet clover seeds

The world of agriculture has seen rapid advancements in recent years, particularly with regards to technological innovations that are aimed at streamlining farming practices and improving crop yields. In a recent study published in the journal Seed Science and Technology, researchers have demonstrated the effectiveness of the Videometer technology in achieving seed purity in a fast and efficient way.

One of the challenges that farmers face when it comes to seed quality is ensuring that their seeds are pure and free of contamination. This is particularly important when growing crops that are intended for human consumption, as contaminated seeds can lead to health problems and crop failure. Sweet clover and alfalfa seeds are two examples of crops that are often prone to contamination, making it difficult for farmers to achieve the desired level of seed purity.

The study, carried out by researchers involved the use of the VideometerLab to discriminate between sweet clover and alfalfa seeds. The VideometerLab is a non-destructive imaging system that captures images of seeds in various spectral regions, including visible and near-infrared regions. These images contain valuable information about the seeds, which can be used to differentiate between different seed types.

Seed Purity Analysis

The results of the study showed that spectral imaging when combined with the Videometer Software – advanced data analysis techniques – was able to achieve seed purity in a fast and effective way. The method was able to accurately discriminate between sweet clover and alfalfa seeds, even when they were mixed together in varying ratios.

By combining non-destructive imaging systems and data analysis techniques, it is possible to develop fast, reliable, and efficient methods for achieving seed purity, which can help to improve crop yields and ensure the safety of crops intended for human consumption.

The abstract states:

“It is hard to remove sweet clover seeds from alfalfa seed lots by conventional methods, affecting the purity of seed lots and resulting losses in for alfalfa hay production as well as seed yield. However, the discrimination of sweet clover seed contaminates in alfalfa seed lots is difficult without special training. In this study, multispectral imaging with object-wise multivariate image analysis was evaluated for its potential to separate sweet clover and alfalfa seeds.”.

Hu, X., Yang, L., Zhang, Z., & Wang, Y. (2020). Differentiation of alfalfa and sweet clover seeds via multispectral imaging. Seed Science and Technology, 48(1), 83–99. https://doi.org/10.15258/sst.2020.48.1.11