Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in Potato

Abstract

Assessment of disease incidence and severity at farm scale or in agronomic trials is frequently performed based on visual crop inspection, which is a labor intensive task prone to errors associated with its subjectivity. Therefore, alternative methods to relate disease incidence and severity with changes in crop traits are of great interest. Optical imagery in the visible and near-infrared (Vis-NIR) can potentially be used to detect changes in crop traits caused by pathogen development. Also, cameras on-board of Unmanned Aerial Vehicles (UAVs) have flexible data collection capabilities allowing adjustments considering the trade-off between data throughput and its resolution. However, studies focusing on the use of UAV imagery to describe changes in crop traits related to disease infection are still lacking. More specifically, evaluation of late blight (Phytophthora infestans) incidence in potato concerning early discrimination of different disease severity levels has not been extensively reported. In this article, the description of spectral changes related to the development of potato late blight under low disease severity levels is performed using sub-decimeter UAV optical imagery. The main objective was to evaluate the sensitivity of the data acquired regarding early changes in crop traits related to disease incidence. For that, UAV images were acquired on four dates during the growing season (from 37 to 78 days after planting), before and after late blight was detected in the field. The spectral variability observed in each date was summarized using Simplex Volume Maximization (SiVM), and its relationship with experimental treatments (different crop systems) and disease severity levels (evaluated by visual assessment) was determined based on pixel-wise log-likelihood ratio (LLR) calculation. Using this analytical framework it was possible to identify considerable spectral changes related to late blight incidence in different treatments and also to disease severity level as low as between 2.5 and 5.0% of affected leaf area. Comparison of disease incidence and spectral information acquired using UAV (with 4-5 cm of spatial resolution) and ground-based imagery (with 0.1-0.2 cm of spatial resolution) indicate that UAV data allowed identification of patterns comparable to those described by ground-based images, despite some differences concerning the distribution of affected areas detected within the sampling units and an attenuation in the signal measured. Finally, although aggregated information at sampling unit level provided discriminative potential for higher levels of disease development, focusing on spectral information related to disease occurrence increased the discriminative potential of the data acquired.

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

“…Image capture using mobile platforms (UAVs, ground robots etc) is being studied in the field, although disease detection is the primary focus (Johnson et al 2003;Garcia-Ruiz et al 2013;de Castro et al 2015). Measurement of severity with VIS spectrum image analysis using mobile platforms is less common (Lelong et al 2008;Sugiura et al 2016;Duarte-Carvajalino et al 2018;Franceschini et al 2019;Ganthaler et al 2018;Liu et al 2018), but is an area of research need. An automated VIS image analysis system on a UAV for measuring severity had moderate precision compared to visual rating (R 2 = 0.73), but was deemed acceptable for rating potato resistance to late blight (Sugiura et al 2016).…”

Hyperspectral Measurements Enable Pre-Symptomatic Detection and Differentiation of Contrasting Physiological Effects of Late Blight and Early Blight in Potato

“…Pre-symptomatic disease detection using spectroscopic methods is a rapidly advancing frontier in plant pathology [18,19,[31][32][33][34][35]. Symptomatic late and early blight detection has been previously established with a range of broad-band optical sensors [36][37][38][39][40][41][42][43], though none have explored pre-symptomatic detection and importantly, differentiation, using narrowband SWIR features. As well, foundational leaf-level work is needed to assess the potential for spaceborne imaging spectroscopy for disease differentiation.…”

Using UAV Collected RGB and Multispectral Images to Evaluate Winter Wheat Performance across a Site Characterized by Century-Old Biochar Patches in Belgium

“…Su et al [37] also showed that the multispectral optimized soil adjusted vegetation index (OSAVI) has a strong discriminating capability between healthy and diseased winter wheat plants. In addition, UAV imagery is able to detect changes in crop traits linked to disease incidence [39]. Therefore, high-resolution UAV remote sensing can be successfully applied for spatial explicit assessment of crop health as a consequence of the presence of century-old biochar within agricultural soils.…”