New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

Abstract

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.

Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

“…However, field-based phenotyping is considered a bottleneck in wheat breeding because it is time-consuming, destructive, and has a high error probability [7,8]. In recent years, the advances in high-throughput phenotyping platforms (HTPP) such as an unmanned aerial vehicle (UAV) carrying multispectral sensors have provided a non-destructive and rapid approach to collect data from multiple sites at low cost [9][10][11][12][13].…”

Spatial variability of biophysical multispectral indexes under heterogeneity and anisotropy for precision monitoring

“…Another possibility is the intensification of anisotropy due to the distortion caused by the reflectance of the exposed soil, better characterized by the infrared indexes. According to Lin et al (2021), only vegetation coverings higher than 50% of the area would suffer distortions close to zero from the influence of the soil, reducing the model uncertainties.…”

“…Geostatistics is largely used to account for such spatial correlations and allows the understanding of structural and vector characteristics. The incorporation of anisotropy is necessary in several studies of surface reflectance imaging in cases where the vegetative indexes are conditioned to the adopted cartesian direction, interfering with the optical and structural properties of plants and soil (Lin et al, 2021). Despite many studies applying vegetation indexes from UAV images for mapping, investigations addressing small-scale autocorrelation structures in anisotropic fields are still incipient, particularly in northeast of Brazil.…”

Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income

“…One challenge is the insufficient quality of the canopy model (2D field map and 3D point cloud) obtained from photogrammetry. Due to the plant structure movement in different drone images caused by wind, the canopy map and 3D model made by photogrammetry often have the effects of double mapping (ghost effect) and seamline distortion [ 21 ]. Many studies tried to fix the low quality using machine learning (ML) algorithms [ 22 – 24 ] or multispectral sensors [ 25 , 26 ]; these can be time-consuming and costly and are often not robust on different crops.…”