The Use of Image Analysis to Detect Seed Contamination-A Case Study of Triticale

Samples of triticale seeds of various qualities were assessed in the study. The seeds were obtained during experiments, reflecting the actual sowing conditions. The experiments were conducted on an original test facility designed by the authors of this study. The speed of the air (15, 20, 25 m/s) transporting seeds in the pneumatic conduit was adjusted to sowing. The resulting graphic database enabled the distinction of six classes of seeds according to their quality and sowing speed. The database was prepared to build training, validation and test sets. The neural model generation process was based on multi-layer perceptron networks (MLPN) and statistical (machine training). When the MLPN was used to identify contaminants in seeds sown at a speed of 15 m/s, the lowest RMS error of 0.052 was noted, whereas the classification correctness coefficient amounted to 0.99.

Automated summary

This study assessed samples of triticale seeds of various qualities, obtained during experiments on an original test facility. The neural model generation process, based on MLPN and statistical machine training, was successful in classifying seed quality at different sowing speeds. The lowest RMS error of 0.052 and a classification correctness coefficient of 0.99 were achieved when MLPN was used to identify contaminants in seeds sown at a speed of 15 m/s.