Agronomy Department, Federal University of Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil.
Center for Nuclear Energy in Agriculture (CENA), University of Sao Paulo (USP), Piracicaba, São Paulo, 13416-000, Brazil.
Sci Rep. 2020 Jul 9;10(1):11267. doi: 10.1038/s41598-020-68273-y.
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.
新的计算机视觉解决方案与人工智能算法相结合，可以帮助识别生物图像中的模式，减少主观性并优化分析过程。本研究旨在提出一种基于交互式和传统机器学习方法的方法，根据外观和生理潜力对大豆种子和幼苗进行分类。此外，我们还将种子的外观与其生理性能相关联。使用低成本方法和免费访问软件对大豆种子和幼苗的图像进行建模。所开发的模型表现出高性能，种子和幼苗分类的整体准确性达到 0.94。基于交互式和传统机器学习开发的模型具有高精度，表明该方法可以轻松地根据外观对大豆种子进行分类，并且可以快速、非主观地对大豆幼苗活力进行分类。大豆种子的外观与它们的生理性能密切相关。
