Abstract
Low-gradient agricultural areas prone to in-field flooding impact crop development and yield potential, resulting in financial losses. Early identification of the potential reduction in yield from excess water stress at the plot scale provides stakeholders with the high-throughput information needed to assess risk and make responsive economic management decisions as well as future investments. The objective of this study is to analyze and evaluate the application of proximal remote sensing from unmanned aerial systems (UAS) to detect excess water stress in soybean and predict the potential reduction in yield due to this excess water stress. A high-throughput data processing pipeline is developed to analyze multispectral images captured at the early development stages (R4–R5) from a low-cost UAS over two radiation use efficiency experiments in West–Central Indiana, USA. Above-ground biomass is estimated remotely to assess the soybean development by considering soybean genotype classes (High Yielding, High Yielding under Drought, Diversity, all classes) and transferring estimated parameters to a replicate experiment. Digital terrain analysis using the Topographic Wetness Index (TWI) is used to objectively compare plots more susceptible to inundation with replicate plots less susceptible to inundation. The results of the study indicate that proximal remote sensing estimates above-ground biomass at the R4–R5 stage using adaptable and transferable methods, with a calculated percent bias between 0.8% and 14% and root mean square error between 72 g/m2 and 77 g/m2 across all genetic classes. The estimated biomass is sensitive to excess water stress with distinguishable differences identified between the R4 and R5 development stages; this translates into a reduction in the percent of expected yield corresponding with observations of in-field flooding and high TWI. This study demonstrates transferable methods to estimate yield loss due to excess water stress at the plot level and increased potential to provide crop status assessments to stakeholders prior to harvest using low-cost UAS and a high-throughput data processing pipeline.
Highlights
Each radiation use efficiency (RUE) experiment contained three soybean classes defined as High Yielding (HD), Diversity (DA) or High Yielding under Drought (HYD) [29]
In order to isolate the potential impact of differing wetness conditions, 28 replicate pairs were extracted from the RUE-1 and RUE-2 experiments, in which one plot experienced low Topographic Wetness Index (TWI) (13.5)
Planting of plots did not take into account TWI, which means that all plots were likely to be planted in areas that may or may not experience excess water stress
Summary
Low-gradient agricultural areas in the Midwest often experience extensive ponding of water in surface depressions, thereby damaging crops and increasing the financial risk from yield loss. In the summer of 2015, crops were planted, but excess water from heavy precipitation caused destruction to five percent of the corn and soybean in Indiana, resulting in approximately USD 300 million in crop damage [1]. The Midwest was devasted in the spring of 2019, where excessively wet conditions prevented crops from being planted or there was a complete loss of crops after planting.
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