Abstract：
:New computer vision solutions combined with artificial intelligence algorithms can help recognize patterns in biological images, reducing subjectivity and optimizing the analysis process. The aim of this study was to propose an approach based on interactive and traditional machine learning methods to classify soybean seeds and seedlings according to their appearance and physiological potential. In addition, we correlated the appearance of seeds to their physiological performance. Images of soybean seeds and seedlings were used to develop models using low-cost approaches and free-access software. The models developed showed high performance, with overall accuracy reaching 0.94 for seeds and seedling classification. The high precision of the models that were developed based on interactive and traditional machine learning demonstrated that the method can easily be used to classify soybean seeds according to their appearance, as well as to classify soybean seedling vigor quickly and non-subjectively. The appearance of soybean seeds is strongly correlated with their physiological performance.
journal_name
Sci Repjournal_title
Scientific reportsauthors
de Medeiros AD,Capobiango NP,da Silva JM,da Silva LJ,da Silva CB,Dos Santos Dias DCFdoi
10.1038/s41598-020-68273-ysubject
Has Abstractpub_date
2020-07-09 00:00:00pages
11267issue
1issn
2045-2322pii
10.1038/s41598-020-68273-yjournal_volume
10pub_type
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