Sarah Opeyemi Ajayi1, Qingwu Xue2, Nithya Rajan3, Amir M.H. Ibrahim3, Srirama Krishna Reddy4, Jackie C. Rudd2, Shuyu Liu4, Ruixiu Sui5 and Kirk E Jessup4, (1)Department of Soil and Crop Sciences, Texas A&M University, COLLEGE STATION, TX (2)Soil and Crop Science, Texas A&M University, Texas A&M AgriLife Research and Extension Center, Amarillo, TX (3)Soil and Crop Sciences, Texas A&M University, College Station, TX (4)Texas A&M AgriLife Research, Amarillo, TX (5)Crop Production Systems Research Unit, USDA-ARS, Stoneville, MS
Wheat (Triticum aestivum L.) is one of the world’s most important cereals and staple food, and there is increasing demand for its production. Wheat production can be enhanced through the development of improved cultivars with wider genetic background, capable of producing better yield under various agro-climatic conditions and stresses. The process of monitoring plant stress, development and phenology contributes to better understanding the relationships between environmental conditions, and crop yields. Several factors influence the early growth in wheat, such as planting date, type of cultivar, water management or growing condition among others. It is essential to monitor the crop performance during the growing season by taking accurate measurements of crop growth parameters that can provide information to increase yield potential. Conventional methods can be time-consuming, labor-intensive and can cause large sampling errors. Remote sensing tools have provided easy and quick measurements of plant characteristics without destructive sampling. The objective of this study is to evaluate the growth and performance of twenty wheat genotypes under two water regimes (rainfed and irrigated conditions), using GreenSeeker®, aerial imagery and digital photography at four growth stages from emergence to heading. Field experiments were conducted at the Texas A&M AgriLife Research Station, Bushland, Texas during the 2014-2015 growing season. Vegetation indices were calculated and showed genotypic variations. Significant relationships (R2: 49%-95%) were recorded among the sensor and image outputs. Results showed that non-destructive remote sensing techniques may be used for growth assessment of wheat under water-limited and optimum conditions for high-throughput phenotyping.