Pivot Based Seed Germination Assessment (PBSGA) Pattern for Germination Quality Analysis

The desire to improve agricultural output drives research into seed quality improvement and seed germination process management technologies. This paper proposes a machine learning-based system for categorizing seed germination—this study's three-stage simulated technique rates germination as good, bad, or indifferent. PBSGA (Pivot Based Seed Germination Evaluation) is a statistical method-based assessment tool used to evaluate each seed chosen for the study dynamically. The model is trained and tested in two scenarios. First, various baskets from the KAGGLE dataset are compared, followed by a machine learning technique for assessing rice seed quality (RSGA). It demonstrates the effectiveness of the proposed solution PBSGA in establishing the correct model category and conceptualizing sequential improvements such as the quality difference between previous and current testing batches. The experimental study's findings hint at the model's potential and how future studies will investigate multi-class labeling.