DOI：10.31220/agriRxiv.2024.00251

Plot-level satellite imagery can substitute for UAVs in assessing maize phenotypes across multistate field trials.

•  

  Abstract: Abstract Accurate genotype-specific early yield estimates at fields and plots offer potential benefits to farmers in optimizing their agronomic practices, breeders in screening thousands of varieties, and policymakers in decision-making contributing to the improvement of agriculture and food production systems. Effective approaches to track plant growth and predict yield require large datasets of remote sensing and ground truth data collected across multiple environments. Low-altitude drone flights are increasingly being used to collect data from field evaluations of new crop varieties, while satellite imagery is being explored to track yield and management practices at the regional scales. Despite their lower spatial resolution, satellite platforms exhibit logistical and technical advantages in scalability and accessibility, and could facilitate plot-level predictions, especially with steadily improving spatial resolution. However, genotype-specific, plot-level, high-resolution satellite images from multiple environments with ground truth measurements are not yet publicly available. Here we generated, described, and evaluated over 20,000 plot-level images of over 80 hybrid maize varieties grown across the US corn belt under various management practices collected from (near simultaneous) satellite and drone flights integrated with ground truth yield measurement. Of the six baseline models examined, models employing data collected from satellite images often matched or exceeded the performance of models employing drone images for both within and cross-environment yield prediction. Large, multi-environment, genetically diverse datasets such as those generated in this study, along with more complex models could help unlock the power of satellite imagery as an important addition to the tool of farmers, plant geneticists, breeders, and policymakers.

  Key words: level; plot; satellite imagery; varieties; spatial resolution; model

  提交时间：2024-05-13

  版权声明：作者本人独立拥有该论文的版权，预印本系统仅拥有论文的永久保存权利。任何人未经允许不得重复使用。
• html

• 图表

• Sebastián Varela, Xuying Zheng, Joyce Njuguna, Erik J. Sacks, Dylan Allen, Jeremy Ruhter, Andrew D. B. Leake. Deep convolutional neural networks exploit high spatial and temporal resolution aerial imagery to predict key traits in miscanthus.. 2022. doi: 10.31220/agriRxiv.2022.00155

  Yao Zhang, Aaron A. King, Elliott M. Hamilton, M. Francesca Cotruf. Representing cropping systems with the MEMS 2.0 ecosystem model.. 2023. doi: 10.31220/agriRxiv.2023.00206

  P. Sukhetha, N. Hemalatha, Raji Sukuma. Classification of fruits and vegetables using ResNet model.. 2021. doi: 10.31220/agriRxiv.2021.00075

  Firaol Legesse Mereb. Desho grass ( Pennisetum pedicellatum ) varieties adaptation trial in highland areas of the North Shewa Zone, Oromia region, Ethiopia.. 2023. doi: 10.31220/agriRxiv.2023.00220

  Firaol Legesse Mereb. Performance evaluation of Vetch ( Vicia spp. ) varieties in highland areas of North Shewa Zone, Oromia, Ethiopia.. 2023. doi: 10.31220/agriRxiv.2023.00219

  Firaol Legesse Mereb. Elephant grass ( Pennisetum purpureum ) varieties adaptation trial at North Shewa Zone, Oromia region, Ethiopia.. 2023. doi: 10.31220/agriRxiv.2023.00213

  Habte Jifar, Zerihun Tadele, Kassahun Tesfaye, Kebebew Assefa, Kifle Dagne, Alemayehu Teressa Negawo, Michael Blümmel, Chris S. Jone. Exploring the potential to capture variation in grain-straw yield and straw quality traits of tef [Eragrostis tef (Zucc.) Trotter] varieties.. 2021. doi: 10.31220/agriRxiv.2021.00078

  Sebastián Varela, Andrew D. B. Leake. Implementing spatio-temporal 3D-convolution neural networks and UAV time series imagery to better predict lodging damage in sorghum.. 2022. doi: 10.31220/agriRxiv.2022.00120

  Jiaqiang Luo, Jamie Selby‐Pham, Kimber Wise, Yin-hao Wu, Jiacan Sun, Ya-meng Qu, Tian Cao, Pangzhen Zhang, Philip J. Marriott, Kate Howel. Early prediction of Shiraz wine quality based on small volatile compounds in grapes.. 2022. doi: 10.31220/agriRxiv.2022.00162

  Clemens Jänicke, Maximilian Wesemeyer, Cristina Chiarella, Tobia Lakes, Christian Levers, Patrick Meyfroidt, Daniel Müller, Marie Pratzer, Philippe Rufi. Can we estimate farm size from field size? An empirical investigation of the field size to farm size relationship.. 2024. doi: 10.31220/agriRxiv.2024.00245

• 序号

  提交日期

  编号

  操作

• 公开评论  匿名评论  仅发给作者 提交评论