HYBRID COMPUTER VISION TECHNIQUES FOR SEED CLASSIFICATION AND QUALITY ASSESSMENT IN PRECISION AGRICULTURE

Abstract

The classification of grain seeds is important in agricultural quality and productivity. Inspection procedures use an unqualified time and are completely dependent on manual classification, highlighting a huge potential for human mistakes. With the development of computer vision and machine learning, automated systems can now classify grain seeds according to effectiveness, accuracy, and on scale.  This study aims to refine the image processing techniques for grain seed classification to develop robust shape-distinguishing algorithms. Furthermore, we outline the principles of a hybrid technology based on computer vision intended to facilitate assessments of the grain's quality and growth potential. Incorporation of deep learning structure with traditional imaging techniques will be shown to improve the accuracy of the system, which reduces the total charge. The proposed feature involves obtaining and preparing data, followed by functional extraction and application of a classification model, and ends with an evaluation to measure performance. The experiments illustrate that hybrid systems overwhelmingly outperform conventional methods, thus proving their grain seed classification speed, accuracy, and robustness capabilities. This study addresses the lack of scalable and automated solutions for grain quality assessment in precision agriculture, expanding its boundaries. Future directions include the integration of edge calculation for real-time classification and the expansion of datasets to include different seeds for improvement in model generalization.