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

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.

Integrating plant morphological traits with remote-sensed multispectral imageries for accurate corn grain yield prediction

“…For the single linear and exponential regression models (i.e., SLR and SER), NDRE and NDVI provided better model performance at the R3 stage (61 DAE) than either at the early (V6) or the mature stage ( Fig 6 and Table 6 ). The weaker yield estimations during earlier flights (V6 stage) are consistent with previous work by Shajahan et al [ 57 ] who found low yield estimates while evaluating six VIs in corn that received N side-dress treatments. Furthermore, the model trait combination with only VIs exhibited good performance (R 2 > 0.73), except for the early growing season (V6).…”

Yield Predictions of Four Hybrids of Maize (Zea mays) using Multispectral Images Obtained from RPAS in the Coast of Peru

“…On the other hand, the R 2 values are lower than those obtained by Barzin et al [57], who used the index OSAVI y SCCCI. At the same time Sunoj et al [58] used exponential and nonlinear NDVI models for yield prediction and obtained R 2 values greater than 0.90. The lower correlations in the early stage may be due to the fact that the physiological characteristics of maize do not yet show significant differences.…”

A comparison of traditional and machine learning corn yield models using hyperspectral UAS and Landsat imagery

“…Several studies have focused on the exploitation of remote sensing data and indices for use in constructing mathematical models for forecasting maize silage yield. [5][6][7][8][9][10][11][12] Two examples of vegetation indices (VIs) that have shown promise as predictors are the normalized difference vegetative index (NDVI) and enhanced vegetative index (EVI). Both indices are regularly used in linear and exponential regression models:…”

“…where a and b are fitted parameters and V I represents Both linear and exponential models have been shown to vary in performance based on geographic region and scale, i.e., field vs county-wide yield estimates. [8][9][10] These models thus can safely rely on low spatial and spectral resolution data, i.e., typical multispectral systems, provided that there is high confidence in the collection system's radiometric calibration. The National Aeronautics and Space Administration's (NASA) Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery systems, Planet Labs' Dove and Superdove constellations, and the European Space Agency's (ESA) Sentinel satellites provide calibrated reflectance imagery which have been used for such yield predictions.…”