Using normalized difference vegetation index to estimate sesame drydown and seed yield

ABSTRACT

Sesame (Sesamum indicum L.) as an ancient crop has received wide attention in subtropical and temperate regions of the world. Yet there is limited data linking maturity with seed yield, which hampers sesame improvement. We used a multi-channel spectral sensor to measure canopy vegetation indices during the period of drydown to facilitate a rapid yield estimation for sesame genotypes under field conditions. We hypothesized that (a) a high normalized difference vegetation index (NDVI) at initial drydown would indicate a high yield; and (b) a rapid drydown rate would lead to a reduced seed yield. The results for 60 sesame genotypes confirmed the first hypothesis. Analysis of the NDVI data for the entire drydown period indicated that sesame genotypes with a higher initial NDVI showed an apparent trend of drying down faster and vice versa. This contradicts the second hypothesis and likely resulted from treating the full drydown as a linear process, which masked variations in NDVI in a short timeframe during initial drydown that could significantly impact the seed yield. However, the variations in NDVI during the initial nine days of drydown had a significant relationship with the measured seed yields, which was compatible with the prediction of the second hypothesis. To capture detailed changes in canopy features during sesame drydown, the value of making more frequent measurements of the vegetation indices from a wider time window was considered. Our study demonstrated that vegetation indices derived from a ground-based sensing tool were useful for characterizing the drydown process of sesame.

KEYWORDS:

• Crop production
• genotype
• maturity
• phenotyping
• Sesamum indicum L.
• spectral reflectance

Acknowledgments

Funding for this research was provided by Equinom Ltd, Visiting Scholar Program at Yancheng Institute of Technology, as well as by USDA NIFA through Texas A&M AgriLife Research Hatch project TEX0-1-9574. We thank Michael Tidwell, Shane Sieckenius, Yaniv Levin and Kayla Flores for assistance in field work, Sixto Silva for technical support, and Nimrod Bardanov and Erin Sim for advice and discussion on sesame management. The authors are indebted to three anonymous reviewers who offered insightful suggestions for improving this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

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Additional information

Funding

This work was supported by the USDA NIFA; Equinom, Ltd; Texas A&M AgriLife Research Center; Yancheng Institute of Technology