Abstract
Crop yield prediction and estimation play essential roles in the precision crop management system. The Simple Algorithm for Yield Estimation (SAFY) has been applied to Unmanned Aerial Vehicle (UAV)-based data to provide high spatial yield prediction and estimation for winter wheat. However, this crop model relies on the relationship between crop leaf weight and biomass, which only considers the contribution of leaves on the final biomass and yield calculation. This study developed the modified SAFY-height model by incorporating an allometric relationship between ground-based measured crop height and biomass. A piecewise linear regression model is used to establish the relationship between crop height and biomass. The parameters of the modified SAFY-height model are calibrated using ground measurements. Then, the calibrated modified SAFY-height model is applied on the UAV-based photogrammetric point cloud derived crop height and effective leaf area index (LAIe) maps to predict winter wheat yield. The growing accumulated temperature turning points of an allometric relationship between crop height and biomass is 712 °C. The modified SAFY-height model, relative to traditional SAFY, provided more accurate yield estimation for areas with LAI higher than 1.01 m2/m2. The RMSE and RRMSE are improved by 3.3% and 0.5%, respectively.
Highlights
Crop biomass is an important parameter for crop yield potential prediction
The piecewise linear regression model was well established from the plant height and dry aboveground biomass (DAM) in sub-field 1 (S1)
(2012) [14] where the piecewise linear regression model can model the relationship between the plant height and DAM
Summary
Crop biomass is an important parameter for crop yield potential prediction. Environmental conditions such as solar energy, temperature, soil nutrients, water, pests, disease, weeds, and other stresses can affect dry aboveground biomass (DAM) and potential yield of a crop [1]. Mapping field-scale crop DAM play an essential role in precision farming. Conventional crop yield estimation methods are labor intensive and have limited sampling numbers due to the accessibility of fieldwork. These methods provide rough estimations and advising in farming activities. Remote sensing technology allows monitoring of variability for large-scale fields without a large amount of ground data. The cost of high temporal and spatial resolution satellite imagery limits its use. Unmanned Aerial Vehicle (UAV) collected high spatial and temporal imagery can monitor within-field DAM and yield variability in real-time. A practical and accurate approach is required to achieve within-field crop biomass monitoring and estimation using UAV-based data
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