Author(s)

Aurora Manley¹, Waltram Ravelombola^1,2*, John Cason^2,3, Brian Bennett³, Hanh Pham⁴, Emi Kimura⁵, Caroline Ruhl¹, Waqas Ahmad¹, Madeline Brown¹

¹Texas A&M AgriLife Research, Vernon, Texas, USA.
²Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA.
³Texas A&M AgriLife Research, Stephenville, Texas, USA.
⁴Texas A&M AgriLife Research, Lubbock, Texas, USA.
⁵Texas A&M AgriLife Extension, Vernon, Texas, USA.

Peanut (Arachis hypogaea L.) is a highly nutritious food that is an excellent source of protein and is associated with increased coronary health, lower risk of type-2 diabetes, lower risk of breast cancer and a healthy profile of inflammatory biomarkers. The domestic demand for organic peanuts has significantly increased, requiring new breeding efforts to develop peanut varieties adapted to the organic farming system. The use of unmanned aerial system (UAS) has gained scientific attention because of the ability to generate high-throughput phenotypic data. However, it has not been fully investigated for phenotyping agronomic traits of organic peanuts. Peanuts are beneficial for cardio system protection and are widely used. Within the U.S., peanuts are grown in 11 states on roughly 600,000 hectares and averaging 4500 kg/ha. This study’s objective was to test the accuracy of UAS data in the phenotyping pod and seed yield of organic peanuts. UAS data was collected from a field plot with 20 Spanish peanut breeding lines on July 07, 2021 and September 27, 2021. The study was a randomized complete block design (RCBD) with 3 blocks. Twenty-five vegetation indices (VIs) were calculated. The analysis of variance showed significant genotypic effects on all 25 vegetation indices for both flights (p < 0.05). The vegetation index Red edge (RE) from the first flight was the most significantly correlated with both pod (r = 0.44) and seed yield (r = 0.64). These results can be used to further advance organic peanut breeding efforts with high-throughput data collection.

Peanut, Unmanned Aerial System, Vegetation Indices, Phenotyping, Pod Yield, Seed Yield

Manley, A. , Ravelombola, W. , Cason, J. , Bennett, B. , Pham, H. , Kimura, E. , Ruhl, C. , Ahmad, W. and Brown, M. (2023) Use of Unmanned Aerial System (UAS) Phenotyping to Predict Pod and Seed Yield in Organic Peanuts. American Journal of Plant Sciences, 14, 415-426. doi: 10.4236/ajps.2023.143027.