GIS-Based Analysis for UAV-Supported Field Experiments Reveals Soybean Traits Associated With Rotational Benefit

Abstract

Recent advances in unmanned aerial vehicle (UAV) remote sensing and image analysis provide large amounts of plant canopy data, but there is no method to integrate the large imagery datasets with the much smaller manually collected datasets. A simple geographic information system (GIS)-based analysis for a UAV-supported field study (GAUSS) analytical framework was developed to integrate these datasets. It has three steps: developing a model for predicting sample values from UAV imagery, field gridding and trait value prediction, and statistical testing of predicted values. A field cultivation experiment was conducted to examine the effectiveness of the GAUSS framework, using a soybean–wheat crop rotation as the model system Fourteen soybean cultivars and subsequently a single wheat cultivar were grown in the same field. The crop rotation benefits of the soybeans for wheat yield were examined using GAUSS. Combining manually sampled data (n = 143) and pixel-based UAV imagery indices produced a large amount of high-spatial-resolution predicted wheat yields (n = 8,756). Significant differences were detected among soybean cultivars in their effects on wheat yield, and soybean plant traits were associated with the increases. This is the first reported study that links traits of legume plants with rotational benefits to the subsequent crop. Although some limitations and challenges remain, the GAUSS approach can be applied to many types of field-based plant experimentation, and has potential for extensive use in future studies.

Dynamics and Drivers of Net Primary Production (NPP) in Southern Africa Based on Estimates from Earth Observation and Process-Based Dynamic Vegetation Modelling

“…with high spatial resolution and short temporal intervals (Gao et al 2013). Earth observation data from various sources has become a valuable tool for analysing vegetation productivity in combination with in situ NPP estimates Fukano et al 2021). A variety of light use efficiency (LUE) models have been developed (Running et al 2004;Zhang et al 2015) to calculate GPP from measured absorbed photosynthetically active radiation (APAR) (Xiao et al 2019).…”

A Novel Approach to Pod Count Estimation Using a Depth Camera in Support of Soybean Breeding Applications

“…During the plant breeding process, agronomic and seed quality attributes can be improved by crossing phenotypically superior crop cultivars and selecting improved offspring in each cycle [ 6 ]. Desirable traits in soybean breeding lines include high yield, resistance to diseases, and abiotic stresses [ 7 , 8 , 9 ]. Among the desirable traits, crop yield is considered a primary trait for selecting superior genotypes in crop breeding programs from numerous breeding lines in multiple years and locations [ 10 ].…”

Development of a high-throughput field phenotyping rover optimized for size-limited breeding fields as open-source hardware

“…For cultivations in outdoor environments, where introducing such a complex system is challenging, large gantry cranes equipped with various sensors have been deployed ( Virlet et al 2016 ). Another prominent phenotyping approach is remote sensing with unmanned aerial vehicles (UAVs), which offer efficient and affordable high-throughput phenotyping and already have numerous applications ( Fukano et al 2021 , Watanabe et al 2017 , Xie and Yang 2020 , Yang et al 2017 ). UAVs, however, have limited battery duration and payload, restricting the amount and variety of sensors that can be equipped, they are also subject to a trade-off between obtaining high-resolution images by lowering altitude and avoiding their downwash effect on plants ( Yang et al 2020 ).…”