Abstract
Knowledge of sub-field yield potential is critical for guiding precision farming. The recently developed simulated observation of point cloud (SOPC) method can generate high spatial resolution winter wheat effective leaf area index (SOPC-LAIe) maps from the unmanned aerial vehicle (UAV)-based point cloud data without ground-based measurements. In this study, the SOPC-LAIe maps, for the first time, were applied to the simple algorithm for yield estimation (SAFY) to generate the sub-field biomass and yield maps. First, the dry aboveground biomass (DAM) measurements were used to determine the crop cultivar-specific parameters and simulated green leaf area index (LAI) in the SAFY model. Then, the SOPC-LAIe maps were converted to green LAI using a normalization approach. Finally, the multiple SOPC-LAIe maps were applied to the SAFY model to generate the final DAM and yield maps. The root mean square error (RMSE) between the estimated and measured yield is 88 g/m2, and the relative root mean squire error (RRMSE) is 15.2%. The pixel-based DAM and yield map generated in this study revealed clearly the within-field yield variation. This framework using the UAV-based SOPC-LAIe maps and SAFY model could be a simple and low-cost alternative for final yield estimation at the sub-field scale.
Highlights
Precision agriculture aims at optimizing input and output in field operations in order to achieve maximum economic profit while maintain environmental sustainability [1]
Through the normalization of the simulated observation of point cloud (SOPC)-leaf area index (LAIe) maps to the simulated simple algorithm for yield estimation (SAFY)-green leaf area index (GLAI) maps, the unmanned aerial vehicle (UAV)-based point cloud derived LAIe can be used as input to the SAFY model for winter wheat final dry aboveground biomass (DAM) and yield estimation
This is the first time the SOPC derived LAIe was used for final DAM and yield estimation of winter wheat
Summary
Precision agriculture aims at optimizing input and output in field operations in order to achieve maximum economic profit while maintain environmental sustainability [1]. Information on the spatial variation of crop biomass and yield at the sub-field level is directly relevant to increasing farm profit by addressing the low-productivity areas within a field. The spatial and temporal resolution of satellite imagery has been improved over the years, it is still incapable of providing timely and detailed information of within-field variations for operational applications [9]. The high spatial and temporal UAV-based imagery can provide important information for monitoring the within-field variabilities of crop status during the growing season [13,14]. The high quality and real-time UAV data gives a better solution in precision farming management, such as the monitoring of crop canopy leaf area index (LAI), nitrogen status, water stress, weed stress, and dry aboveground biomass [15,16,17,18]
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