Automated Seed Quality Assessment and Classification Using Watershed Algorithm and Ensemble Learning

In response to the evolving landscape of precision agriculture, this project presents a pioneering solution for automated seed quality assessment. Leveraging the Watershed Algorithm, we conduct sophisticated image processing on Bas_370 seeds, extracting intricate features that form the backbone of a comprehensive CSV dataset. The subsequent implementation of an ensemble machine learning model, crafted through a fusion of classifiers, revolutionizes seed quality categorization, offering a robust and accurate alternative to conventional methods. Our approach not only addresses the limitations of traditional assessment techniques but also sets the stage for heightened precision and productivity in agriculture. The subsequent sections meticulously unravel the application of the Watershed Algorithm, the nuances of feature extraction, and the intricate development and evaluation processes of the ensemble machine learning model, providing a thorough comprehension of this groundbreaking seed quality assessment methodology.