Automated Evaluation of Onion Seed Quality Using Physical Characteristics via Image Processing and Machine Learning Techniques

Abstract

Seed Quality is an important area of agriculture and directly influences crop yield and germination percentage. Visual examination forms the foundation of traditional seed testing techniques, which are cumbersome, inflexible, and inefficient for effective assessment. This study proposed an automated approach to seed quality assessment based on physical measurement using machine learning and image processing techniques. Snapshots of the new seeds were captured and underwent feature extraction, segmentation, and image improvement to explore notable morphological attributes, such as size and colour. To tag seeds as "good" or "bad" based on physical characteristics, Support Vector Machines (SVMs) are used as a reference model. Rather, Convolutional Neural Networks (CNNs) have been utilised for deep feature extraction and classification. Experimental findings indicate that CNNs perform better than conventional machine learning models, with a scalable and highly accurate method of seed quality assessment. Future use will utilise quantum machine learning to improve prediction and facilitate sustainable, precision agriculture. The improved framework, optimised with great care for onion seeds, is a major breakthrough in increasing the agricultural productivity of onion cultivation.