A Theoretical Exploration And Holistic Survey Of Non-Intrusive Measurement Methodologies Employed In Determining Cotton Seed Quality

Cotton seed quality plays a crucial role in agricultural productivity, fiber characteristics, and overall textile industry efficiency. In recent years, non-invasive and non-destructive measurement techniques have become one of the most promising directions for seed quality analysis due to their ability to preserve seed integrity while ensuring precise evaluation. This study provides a theoretical investigation and comprehensive overview of state-of-the-art computer vision and deep learning–based approaches applied in seed assessment. Special attention is given to modern detection frameworks, including YOLO-based architectures, convolutional attention mechanisms such as CBAM and SegNext, and instance segmentation techniques like SOLO and SOLOv2. Additionally, the effects of model parameters such as batch size on generalizability and the role of modular frameworks like MMDetection are discussed. The findings highlight that integrating non-destructive imaging with advanced neural models significantly improves accuracy, speed, and robustness in seed quality assessment processes.

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