da Silva, D.A.; de Freitas, E.D.G.; Abud, H.F.; Gomes, D.G. Applying YOLOv8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds. AgriEngineering2024, 6, 869-880.
da Silva, D.A.; de Freitas, E.D.G.; Abud, H.F.; Gomes, D.G. Applying YOLOv8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds. AgriEngineering 2024, 6, 869-880.
da Silva, D.A.; de Freitas, E.D.G.; Abud, H.F.; Gomes, D.G. Applying YOLOv8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds. AgriEngineering2024, 6, 869-880.
da Silva, D.A.; de Freitas, E.D.G.; Abud, H.F.; Gomes, D.G. Applying YOLOv8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds. AgriEngineering 2024, 6, 869-880.
Abstract
Agricultural crops productivity is heavily influenced by the strength of their seeds. Current methods for testing seed vigor, such as the germination rate and tetrazolium test, rely on time-consuming human visual inspections. Computer Vision, a crucial technology in smart production processes, offers an automated alternative and is increasingly applied in digital agriculture processes. Here we propose a method that combines YOLOv8 and morphological analysis of seeds using X-ray images to aid seed quality analysts in classifying seed lots based on their physiological potential. We examined the internal morphology of three seed lots and generated X-ray images to train a YOLOv8-based model with a post-segmentation module. This model assessed various parameters such as the total seed area, area filled by the embryo and endosperm, length, width, and calculated the percentage occupied by the embryo and endosperm in relation to the total seed area. Subsequently, seeds were classified into four categories based on the internal area occupied by the endosperm and embryo. Our findings reveal the robust performance of the YOLOv8 model in segmenting and classifying despite the challenges posed by a relatively small dataset. Notably, the proposed model achieved an impressive accuracy of up to 95.6% in identifying and segmenting the endosperm over 1,500 epochs with just 15 training images. The endosperm/seed area ratio, particularly within the 50-60% range covering over 50% of the samples, emerged as a significant metric for evaluating the viability of seed batches.
Keywords
Deep learning; Seed quality; X-ray imaging
Subject
Biology and Life Sciences, Food Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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