High-Throughput Canopy and Belowground Phenotyping of a Set of Peanut CSSLs Detects Lines with Increased Pod Weight and Foliar Disease Tolerance

Author:

Gimode Davis12,Chu Ye1,Holbrook Corley C.3ORCID,Fonceka Daniel4ORCID,Porter Wesley5,Dobreva Iliyana6ORCID,Teare Brody7,Ruiz-Guzman Henry7ORCID,Hays Dirk78,Ozias-Akins Peggy1

Affiliation:

1. Institute of Plant Breeding Genetics and Genomics, University of Georgia, Tifton, GA 31793, USA

2. International Crops Research Institute for the Semi-Arid Tropics, Nairobi P.O. Box 39063-00623, Kenya

3. United States Department of Agriculture, Agricultural Research Service, Tifton, GA 31793, USA

4. Centre d’Etude Régional pour l’Amélioration de l’Adaptation à la Sécheresse (CERAAS), Institut Sénégalais de Recherches Agricoles (ISRA), BP 3320, Route de Khombole, Thiès 21000, Senegal

5. Crop and Soil Sciences Department, University of Georgia, Tifton, GA 31793, USA

6. Department of Geography, The Ohio State University, Colombus, OH 43210, USA

7. Molecular & Environmental Plant Sciences, Texas A&M University, College Station, TX 77843, USA

8. Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA

Abstract

We deployed field-based high-throughput phenotyping (HTP) techniques to acquire trait data for a subset of a peanut chromosome segment substitution line (CSSL) population. Sensors mounted on an unmanned aerial vehicle (UAV) were used to derive various vegetative indices as well as canopy temperatures. A combination of aerial imaging and manual scoring showed that CSSL 100, CSSL 84, CSSL 111, and CSSL 15 had remarkably low tomato spotted wilt virus (TSWV) incidence, a devastating disease in South Georgia, USA. The four lines also performed well under leaf spot pressure. The vegetative indices showed strong correlations of up to 0.94 with visual disease scores, indicating that aerial phenotyping is a reliable way of selecting under disease pressure. Since the yield components of peanut are below the soil surface, we deployed ground penetrating radar (GPR) technology to detect pods non-destructively. Moderate correlations of up to 0.5 between pod weight and data acquired from GPR signals were observed. Both the manually acquired pod data and GPR variables highlighted the three lines, CSSL 84, CSSL 100, and CSSL 111, as the best-performing lines, with pod weights comparable to the cultivated check Tifguard. Through the combined application of manual and HTP techniques, this study reinforces the premise that chromosome segments from peanut wild relatives may be a potential source of valuable agronomic traits.

Funder

USAID

Publisher

MDPI AG

Subject

Agronomy and Crop Science

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