Luoni Sofia A Bengoa, Ricci Riccardo, Corzo Melanie A, Hoxha Genc, Melgani Farid, Fernandez Paula
Laboratory of Genetics, Wageningen University & Research, 6708 PB Wageningen, The Netherlands.
Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy.
Plants (Basel). 2024 Jul 22;13(14):1998. doi: 10.3390/plants13141998.
Leaf senescence is a complex trait which becomes crucial for grain filling because photoassimilates are translocated to the seeds. Therefore, a correct sync between leaf senescence and phenological stages is necessary to obtain increasing yields. In this study, we evaluated the performance of five deep machine-learning methods for the evaluation of the phenological stages of sunflowers using images taken with cell phones in the field. From the analysis, we found that the method based on the pre-trained network resnet50 outperformed the other methods, both in terms of accuracy and velocity. Finally, the model generated, Sunpheno, was used to evaluate the phenological stages of two contrasting lines, B481_6 and R453, during senescence. We observed clear differences in phenological stages, confirming the results obtained in previous studies. A database with 5000 images was generated and was classified by an expert. This is important to end the subjectivity involved in decision making regarding the progression of this trait in the field and could be correlated with performance and senescence parameters that are highly associated with yield increase.
叶片衰老 是一种复杂的性状，对于籽粒灌浆至关重要，因为光合产物会转运到种子中。因此，叶片衰老与物候期之间的正确同步对于提高产量是必要的。在本研究中，我们使用在田间用手机拍摄的图像，评估了五种深度机器学习方法用于评估向日葵物候期的性能。通过分析，我们发现基于预训练网络resnet50的方法在准确性和速度方面均优于其他方法。最后，所生成的模型Sunpheno用于评估两个对比品系B481_6和R453在衰老过程中的物候期。我们观察到物候期存在明显差异，证实了先前研究中获得的结果。生成了一个包含5000张图像的数据库，并由专家进行分类。这对于消除田间该性状进展决策中涉及的主观性很重要，并且可以与与产量增加高度相关的性能和衰老参数相关联。
