|VONG, CHIN NEE - University Of Missouri|
|CONWAY, LANCE - University Of Missouri|
|ZHOU, JIANFENG - University Of Missouri|
|Sudduth, Kenneth - Ken|
Submitted to: ASABE Annual International Meeting
Publication Type: Proceedings
Publication Acceptance Date: 6/15/2022
Publication Date: 7/17/2022
Citation: Vong, C., Conway, L.S., Zhou, J., Kitchen, N.R., Sudduth, K.A. 2022. Corn emergence uniformity at different planting depths and yield estimation using UAV imagery. Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE), July 17-20, 2022, Houston, Texas. Paper No. 2200545. https://doi.org/10.13031/aim.202200545.
Interpretive Summary: Corn seeds sometimes do not emerge uniformly, affected by cold and wet soils, planter malfunction, inconsistent seeding depths, and tillage practices. When they do not emerge at about the same time or with inconsistent spacings between plants, grain yield can be reduced. For farmers to detect corn stand problems for replanting and crop management decisions, as well as for researchers to examine emergence of different treatments and management, automated field-level assessment tools are needed. This research was conducted to evaluate unmanned aerial vehicle (UAV) images for detecting emergence difference and estimating final grain yield at field scale. Results showed emergence difference among different planting depths and across landscape variability. However, emergence information alone could not explain yield variation. On the other hand, image features used as early-plant growth indicators helped explain 34% of the yield variation. While the use of UAV imagery for detecting emergence difference and estimating yield was feasible, more studies are needed to cover more field types under different weather scenarios to have a more robust procedure. Then, farmers and researchers will be able to successfully utilize UAV scouting to help evaluate emergence uniformity and final yield for large areas.
Technical Abstract: Uniform corn emergence is critical for maintaining optimum yield. Historically, studies relating plant emergence and early growth to yield for different treatments and management were based on small plots due to limitations in labor and time for in-field evaluation. Precision agriculture technologies (e.g., proximal sensors, yield monitors, and unmanned-aerial-vehicle (UAV)-based remote sensing) have now enabled field-scale evaluation. This study aimed to demonstrate UAV imagery applications in corn production at field scale with two case studies: 1) investigating corn emergence spatial variability at different planting depths; 2) estimating corn yield using image features. Red-green-blue and multispectral images were acquired to determine emergence parameters and vegetation indices (VIs) for early plant growth (V4, V6, and V7) indicators. In case study 1, average and coefficient of variation of emergence parameters were computed in 1.0 m × 6.1 m grids for four planting depths. In case study 2, yield was estimated by different feature datasets in random forest models. Results demonstrated that there was spatial variability within the planting depth treatments along the transects. Emergence data alone could not explain variation in yield (R^2 = 0.01); however, the combination of VIs at all growth stages could estimate yield with R^2 of 0.34. These case studies demonstrated UAV imagery usage in studying crop emergence variability and estimating yield at field scale. Future studies should include more timely UAV data along the growing season in different fields and years to develop a more robust model.