Evaluating the Potential of Narrow-Band Indices to Predict Soybean (Glycine Max L. Merr) Grain Yield in The Free State and Mpumalanga of South Africa

Volume 3 - Issue 1

Siphokazi R Gcayi^1,2*, George J Chirima¹, Samuel A Adelabu², Elhadi Adam³ and Khaled Abutaleb¹

Received: June 14, 2019;   Published: June 24, 2019

DOI: 10.32474/OAJESS.2019.02.000153

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Abstract

Yield predictions allow for decision making regarding management of agricultural yield before and after harvest by government and decision-makers. Traditional approaches to collect yield statistics such as manual field surveys and physical computation of yield are costly and take a long time for information to be available. Remote sensing platforms such as hyperspectral data provide real-time, fast, and reliable statistics that can be used to derive yield information. Vegetation indices are ratios used to combine multiple band observations of the hyperspectral data into one index and applied to derive soybean grain yield. The objective of this study was to evaluate the potential of vegetation indices derived from hyperspectral data to predict soybean grain yield. Soybean hyperspectral data was acquired using a handheld spectroradiometer with a spectral range of 350 to 2500 nm in March and April of the summer season of 2017. The random forest regression algorithm was used to predict the soybean grain yield. NDVI, SR and EVI were calculated from the hyperspectral data for all probable bands situated in the 400 nm and 2399 regions. The results showed that relevant wavelengths in predicting soybean were combinations situated in the red-edge (680-750 nm), NIR and the MIR (1300 to 2399 nm) of the electromagnetic spectrum. Furthermore, regression results showed that SR better predicted the soybean grain yield (R² = 0.843) compared to NDVI (R² = 0.841) and EVI (R² = 0.537). In overall, the results of this study suggest that narrow-band indices have the potential to predict soybean grain yield.

Keywords: Soybean Yield; Hyperspectral Data; Vegetation Indices

Abbreviations: NDVI: Normalised Difference Vegetation Index; SR: Simple Ratio; EVI: Enhanced Vegetation Index; RF: Random Forest; RMSE: Root Mean Square Error; FS: Free State; MP: Mpumalanga

Abstract| Introduction| Materials and Methods| Data analysiss| Results| Discussion| Conclusion| Acknowledgement| References|