Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images

Abstract

Lodging has been recognized as one of the major destructive factors for crop quality and yield, resulting in an increasing need to develop cost-efficient and accurate methods for detecting crop lodging in a routine manner. Using structure-from-motion (SfM) and novel geospatial computing algorithms, this study investigated the potential of high resolution imaging with unmanned aircraft system (UAS) technology for detecting and assessing lodging severity over an experimental maize field at the Texas A&M AgriLife Research and Extension Center in Corpus Christi, Texas, during the 2016 growing season. The method was proposed to not only detect the occurrence of lodging at the field scale, but also to quantitatively estimate the number of lodged plants and the lodging rate within individual rows. Nadir-view images of the field trial were taken by multiple UAS platforms equipped with consumer grade red, green, and blue (RGB), and near-infrared (NIR) cameras on a routine basis, enabling a timely observation of the plant growth until harvesting. Models of canopy structure were reconstructed via an SfM photogrammetric workflow. The UAS-estimated maize height was characterized by polygons developed and expanded from individual row centerlines, and produced reliable accuracy when compared against field measures of height obtained from multiple dates. The proposed method then segmented the individual maize rows into multiple grid cells and determined the lodging severity based on the height percentiles against preset thresholds within individual grid cells. From the analysis derived from this method, the UAS-based lodging results were generally comparable in accuracy to those measured by a human data collector on the ground, measuring the number of lodging plants (R 2 = 0.48) and the lodging rate (R 2 = 0.50) on a per-row basis. The results also displayed a negative relationship of ground-measured yield with UAS-estimated and ground-measured lodging rate.

Identification of Maize Lodging: A Case Study Using a Remotely Piloted Aircraft System

“…Lodging refers to the permanent displacement of aerial portions of the plantations from the vertical position due to stem buckling (stem tipping) or failure of the groundroot anchorage system (root housing) (Wu & Ma, 2016). The causes of lodging are varied, ranging from an excess of nitrogen, storm damage, soil bulk density, crop disease and overpopulation (Chu et al, 2017). Maize is one of the main crops affected by plant lodging, mostly because it makes mechanical harvesting difficult, leading to grain quality reduction and yield reduction (Wu & Ma, 2016;Chu et al, 2017).…”

“…The causes of lodging are varied, ranging from an excess of nitrogen, storm damage, soil bulk density, crop disease and overpopulation (Chu et al, 2017). Maize is one of the main crops affected by plant lodging, mostly because it makes mechanical harvesting difficult, leading to grain quality reduction and yield reduction (Wu & Ma, 2016;Chu et al, 2017). Lodging maps can be useful during yield variability analysis, since lodging is considered a source of error in yield maps and should be removed.…”

“…Lodging maps can be useful during yield variability analysis, since lodging is considered a source of error in yield maps and should be removed. In addition, these maps can be further used to carry out in situ investigations and help in understanding its relation with environmental factors and management practices (Li et al, 2014;Chu et al, 2017;Liu et al, 2018). The maps are a useful tool for precision farming and applications that rely on spatial information regarding lodging, such as damage assessments for insurance purposes (Wilke et al, 2019).…”

Examining the utility of Simultaneous Localisation and Mapping (SLAM) LiDAR on a low-altitude unmanned aerial vehicle (UAV) across environments of various composition and dynamic complexity

“…The results from RedEdge were somewhat surprising given that mean height has been reported as a suitable parameter for non-contact, indirect, measures of crop biomass [61] and consistent with field-verified data [156] in previous SfM studies. To [157], who utilised a similar, multi-temporal design in the measure of sorghum crop heights.…”

“…Similar to the surface differencing approach in crop height modelling of many previous UAV-based studies [22,26,62,[155][156][157], the objective here was to identify those points corresponding to the ground (GRND) and compare them to those returned from the sugarcane canopy (Non-GRND) within each Survey. Like these previous works, our approach was to first develop a digital terrain model (DTM) using the Ground Extractor tool within 3DR.…”