Artificial intelligence applied to the classification of greenish seeds and prediction of physiological quality in soybean

Abstract

The presence of greenish seeds represents an obstacle to the productive potential of soybean cultivation, causing significant impacts on the visual aspect and physiological quality of seeds. Traditionally, seeds are evaluated visually, a method that is subject to subjectivity and human error. This research proposes an innovative approach that integrates image analysis and artificial intelligence to develop a machine learning model capable of distinguishing greenish seeds from yellow ones based on color parameters. This study aims to enhance the accuracy of seed evaluation and expand understanding of the relationship between seed color tone and their physiological quality. The artificial intelligence was trained with 12,000 images captured and processed by the GroundEye® S800D. The methodology employed to train the system involved the use of a decision tree, utilizing the sklearn.tree library from Python. Each seed, after image capture, underwent a standard germination test. The normal seedlings were then reanalyzed using the GroundEye® S800D to determine their vigor through measurements of primary root and hypocotyl sizes. Yellow soybean seeds exhibit superior physiological quality compared to greenish ones, particularly in terms of germination and seedling growth. The hue angle (h) and luminosity (L) proved to be the most responsive criteria in the machine learning model, achieving an accuracy of 89.7%. The hue angle was demonstrated to be a robust predictor, correlating with higher germination rates in seeds with an angle less than 97.5°. The relationship between seed viability and hue angle was supported by a coefficient of determination (R²) of 73%.