Location: Sustainable Water Management ResearchTitle: Using aerial and digital photography and aerial imagery to monitor growth and yield in winter wheat
|OLANREWAJU, SARAH - Texas A&M University|
|RAJAN, NITHYA - Texas A&M Agrilife|
|IBRAHIM, AMIR - Texas A&M Agrilife|
|RUDD, JACKIE - Texas A&M Agrilife|
|LIU, SHUYU - Texas A&M Agrilife|
|JESSUP, KIRK - Texas A&M Agrilife|
|XUE, QINGWU - Texas A&M Agrilife|
Submitted to: International Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/25/2019
Publication Date: 4/3/2019
Citation: Olanrewaju, S., Rajan, N., Ibrahim, A.M., Rudd, J.C., Liu, S., Sui, R., Jessup, K.E., Xue, Q. 2019. Using aerial and digital photography and aerial imagery to monitor growth and yield in winter wheat. International Journal of Remote Sensing. 40(18):6905-6929. https://doi.org/10:1080/01431161.2019.1597303.
Interpretive Summary: An easy and objective selection tool is needed for breeders to reduce the laborious and time-consuming process of high yield and drought tolerant genotype selection when screening large number of genotypes in crop breeding. In collaboration with researchers in Texas A&M University and Texas A&M AgriLife Research, Scientist at USDA ARS Crop production Systems Research Unit in Stoneville, MS conducted experiments using remote sensing system to detect wheat genotypes with higher aboveground biomass and yield. Aerial imagery was used to assess the growth, performance, and yield of winter wheat under rainfed and irrigated conditions. Methods developed in this study could be useful for high-throughput phenotyping to screen for drought-tolerant and high-yielding genotypes.
Technical Abstract: Monitoring wheat (Triticum aestivum L.) performance throughout the growing season provides information on productivity and yield potential. Remote sensing tools have provided easy and quick measurements without destructive sampling. The objective of this study was to evaluate genetic variability in growth and performance of twenty wheat genotypes under two water regimes (rainfed and irrigated), using spectral vegetation indices (SVI) estimated from aerial imagery, and percent groundcover (%GC) estimated from digital photos. Field experiments were conducted at Bushland, Texas in two growing seasons (2014-2015 and 2015-2016). Digital photographs were taken using a digital camera in each plot, while a manned aircraft collected images of the entire field using a 12 band multiple camera array Tetracam system at three growth stages (tillering, jointing and heading). There was a significant variation in SVI, %GC, aboveground biomass and yield among wheat genotypes, mostly at tillering and jointing stages. Significant correlations for %GC from digital photo at jointing were recorded with Normalized Difference Vegetation Index (NDVI) at tillering (R2 = 0.84, P < 0.0001) and %GC estimated from Perpendicular Vegetation Index (PVI) at tillering (R2 = 0.83, P < 0.0001). Among the indices, Ratio Vegetation Index (RVI), Green-Red VI, Green Leaf Index (GLI), Generalized DVI (squared), DVI, Enhanced VI, Enhanced NDVI, and NDVI explained 37-99% of the variability in aboveground biomass and yield. Results indicate that these indices could be used as an indirect selection tool for screening large number of early-generation and advanced wheat lines.