Triticale field phenotyping using RGB camera for ear counting and yield estimation
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Periodical |
| Magazine / Source | Journal of Applied Genetics |
| MU Faculty or unit | |
| Citation | |
| web | https://link.springer.com/article/10.1007/s13353-024-00835-6 |
| Doi | http://dx.doi.org/10.1007/s13353-024-00835-6 |
| Keywords | plant breeding; yield potential; ear detection; deep learning; field imaging; statistical analysis |
| Description | Triticale (X Triticosecale Wittmack), a wheat-rye small grain crop hybrid, combines wheat and rye attributes in one hexaploid genome. It is characterized by high adaptability to adverse environmental conditions: drought, soil acidity, salinity and heavy metal ions, poorer soil quality, and waterlogging. So that its cultivation is prospective in a changing climate. Here, we describe RGB on-ground phenotyping of field-grown eighteen triticale market-available cultivars, made in naturally changing light conditions, in two consecutive winter cereals growing seasons: 2018–2019 and 2019–2020. The number of ears was counted on top-down images with an accuracy of 95% and mean average precision (mAP) of 0.71 using advanced object detection algorithm YOLOv4, with ensemble modeling of field imaging captured in two different illumination conditions. A correlation between the number of ears and yield was achieved at the statistical importance of 0.16 for data from 2019. Results are discussed from the perspective of modern breeding and phenotyping bottleneck. |
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