Tarbiat Modares University Journal of Agricultural Science and Technology 1680-7073 28 3 2026 05 01 Non-Invasive Detection of Early Germination in Beans Using Laser Speckle Temporal Analysis and Deep Learning 679 698 24094 10.48311/jast.2026.24094 EN Eli Veiga Junior Informatics and Knowledge Management Post-Graduation Program, Nove de Julho University (UNINOVE), Vergueiro Street 235/249, São Paulo 01504-001, Brazil. Sidnei Alves De Araújo Informatics and Knowledge Management Post-Graduation Program, Nove de Julho University (UNINOVE), Vergueiro Street 235/249, São Paulo 01504-001, Brazil. Alessandro Melo Deana Informatics and Knowledge Management Post-Graduation Program, Nove de Julho University (UNINOVE), Vergueiro Street 235/249, São Paulo 01504-001, Brazil. 2 Biophotonics Applied to Health Sciences Graduate Program, Nove de Julho University (UNINOVE), Vergueiro Street 235/249, São Paulo 01504-001, Brazil. 0000-0002-0014-6953 Journal Article 2025 09 16 In their early stages, germination of bean is not externally visible, requiring invasive methods such as physically breaking the seed for detection. This study proposes a non-invasive and automated approach that combines laser speckle imaging with deep learning to identify early-stage germinated defects in beans. Speckle images were captured under coherent laser illumination (λ= 633 nm) for samples subjected to germination periods of 0, 6, 12, and 24 hours. Two contrast analysis techniques were evaluated: Laser Speckle Spatial Contrast Analysis (LASCA) and the more advanced Laser Speckle Temporal Contrast Analysis (LASTCA), the latter using temporal intensity fluctuations across 120 video frames. From the resulting contrast maps, regions of interest (50×50 pixels) were extracted and used to train a Convolutional Neural Network (CNN) for binary classification. The proposed system achieved high performance, with an accuracy of 92.33% and sensitivity of 98.21%, successfully detecting germinated defects invisible to conventional inspection. By integrating temporal bio-speckle analysis with deep learning, this method offers a scalable solution for intelligent, non-destructive grain inspection—addressing a critical gap in current computer vision systems and contributing to the advancement of Agribusiness 4.0.<br><br><br><br><br> https://jast.modares.ac.ir/article_24094_8c3af53f6554ac306d481a872a47fb83.pdf