Corn Grain Yield Prediction and Mapping from Unmanned Aerial System (UAS) Multispectral Imagery

S Sunoj,Joe Guinness,Karl J Czymmek,Jan Van Aardt,Jason Cho,Quirine M Ketterings

doi:10.3390/rs13193948

S Sunoj, Joe Guinness + Show 4 more

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https://doi.org/10.3390/rs13193948

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Abstract

Harvester-mounted yield monitor sensors are expensive and require calibration and data cleaning. Therefore, we evaluated six vegetation indices (VI) from unmanned aerial system (Quantix™ Mapper) imagery for corn (Zea mays L.) yield prediction. A field trial was conducted with N sidedress treatments applied at four growth stages (V4, V6, V8, or V10) compared against zero-N and N-rich controls. Normalized difference vegetation index (NDVI) and enhanced vegetation index 2 (EVI2), based on flights at R4, resulted in the most accurate yield estimations, as long as sidedressing was performed before V6. Yield estimations based on earlier flights were less accurate. Estimations were most accurate when imagery from both N-rich and zero-N control plots were included, but elimination of the zero-N data only slightly reduced the accuracy. Use of a ratio approach (VITrt/VIN-rich and YieldTrt/YieldN-rich) enables the extension of findings across fields and only slightly reduced the model performance. Finally, a smaller plot size (9 or 75 m2 compared to 150 m2) resulted in a slightly reduced model performance. We concluded that accurate yield estimates can be obtained using NDVI and EVI2, as long as there is an N-rich strip in the field, sidedressing is performed prior to V6, and sensing takes place at R3 or R4.

Highlights

This study showed that the delaying of sidedressing impacted the yield, and the ability to accurately estimate the yield from Normalized difference vegetation index (NDVI) signals
This study had as its main objectives to compare various vegetation indices for their ability to estimate yield (R4 growth stage), to assess the effect of sidedress application timing on yield estimation, to evaluate the impact of the growth stage/timing on VI-based corn yield prediction models, and to determine whether the zero-N strip is essential for deriving accurate yield estimation models, while we evaluated the effect of sampling area on model accuracy
Among the six vegetation indices tested, the NDVI and enhanced vegetation index 2 (EVI2) were most suitable for estimating corn grain yield

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Corn (Zea mays L.) grain yield is an important measure for farmers to evaluate field management [1]. Yield can be measured at different scales, such as county-scale, fieldscale, and within-field-scale, and each scale is useful for different management approaches. Yield monitor systems installed on grain combines have been used for over 25 years [2]

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Conclusion