2024
DOI: 10.3390/agronomy14061098
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Quantifying Soybean Defects: A Computational Approach to Seed Classification Using Deep Learning Techniques

Amar Sable,
Parminder Singh,
Avinash Kaur
et al.

Abstract: This paper presents a computational approach for quantifying soybean defects through seed classification using deep learning techniques. To differentiate between good and defective soybean seeds quickly and accurately, we introduce a lightweight soybean seed defect identification network (SSDINet). Initially, the labeled soybean seed dataset is developed and processed through the proposed seed contour detection (SCD) algorithm, which enhances the quality of soybean seed images and performs segmentation, follow… Show more

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