Department of Horticulture, University of Georgia, Athens, GA 30602, USA.
Department of Statistics, University of Georgia, Athens, GA 30602, USA.
Sensors (Basel). 2024 Jun 29;24(13):4225. doi: 10.3390/s24134225.
The survival and growth of young plants hinge on various factors, such as seed quality and environmental conditions. Assessing seedling potential/vigor for a robust crop yield is crucial but often resource-intensive. This study explores cost-effective imaging techniques for rapid evaluation of seedling vigor, offering a practical solution to a common problem in agricultural research. In the first phase, nine lettuce () cultivars were sown in trays and monitored using chlorophyll fluorescence imaging thrice weekly for two weeks. The second phase involved integrating embedded computers equipped with cameras for phenotyping. These systems captured and analyzed images four times daily, covering the entire growth cycle from seeding to harvest for four specific cultivars. All resulting data were promptly uploaded to the cloud, allowing for remote access and providing real-time information on plant performance. Results consistently showed the 'Muir' cultivar to have a larger canopy size and better germination, though 'Sparx' and 'Crispino' surpassed it in final dry weight. A non-linear model accurately predicted lettuce plant weight using seedling canopy size in the first study. The second study improved prediction accuracy with a sigmoidal growth curve from multiple harvests ( = 0.88, = 0.27, < 0.001). Utilizing embedded computers in controlled environments offers efficient plant monitoring, provided there is a uniform canopy structure and minimal plant overlap.
幼苗的生存和生长取决于多种因素,如种子质量和环境条件。评估幼苗活力以获得稳健的作物产量至关重要,但通常需要大量资源。本研究探讨了经济高效的成像技术,以快速评估幼苗活力,为农业研究中的常见问题提供了实际解决方案。在第一阶段,将九个生菜()品种播种在托盘,并使用叶绿素荧光成像技术每周监测三次,持续两周。第二阶段涉及集成配备摄像头的嵌入式计算机进行表型分析。这些系统每天拍摄和分析四次图像,覆盖四个特定品种从播种到收获的整个生长周期。所有生成的数据都迅速上传到云端,允许远程访问,并提供有关植物性能的实时信息。结果一致表明,'Muir' 品种的冠层较大,发芽较好,但 'Sparx' 和 'Crispino' 在最终干重方面超过了它。第一项研究中,使用幼苗冠层大小的非线性模型准确预测了生菜植物的重量。第二项研究通过多次收获的( = 0.88, = 0.27, < 0.001)的 sigmoidal 生长曲线提高了预测准确性。在受控环境中使用嵌入式计算机可以提供高效的植物监测,但前提是冠层结构均匀且植物重叠最小。