Wheat Lodging Assessment Using Multispectral Uav Data

Abstract

<p><strong>Abstract.</strong> Lodging is a major yield-reducing factors in wheat, causing reductions up to 80%. Timely detection of lodging can reduce its impacts and support proper decisions regarding expected yield, crop price or its insurance. Since the incidence of lodging is heterogeneous within a field, very high-resolution remote sensing data can be viable for accurate and prompt spatio-temporal assessment of lodging severity. As such unmanned aerial vehicles (UAVs) provide a versatile and cost-effective solution to monitor crops on a small scale with sub-centimetre spatial resolution. In this study, we analysed the spectral variability between different grades of lodging severity (non-lodged (NL), moderate (ML), severe (SL) and very severe (VSL)) and classified them using high-resolution UAV data. Multispectral orthomosaic UAV images with 5cm resolution and nine bands (covering the VIS-NIR spectrum with Sentinel-2 filters) were acquired in May 2018 for two wheat fields in Bonifiche Ferraresi farm, Jolanda di Savoia, Italy. Concurrent to the time of image acquisition, a field campaign was carried out in which crop characteristics and lodging related parameters were collected. The results showed that reflectance magnitude increased with lodging severity and demonstrated that the red-edge and NIR bands can be used to clearly discriminate between NL and lodged (all grades) wheat and to some extent between different lodging classes (ML, SL and VSL). The nearest neighbourhood classification performed using an object-based segmentation yielded optimal results with an overall accuracy of 90%, thus demonstrating the use of multispectral UAV data as a promising tool for wheat lodging assessment.</p>

Sentinel-2 Data and Unmanned Aerial System Products to Support Crop and Bare Soil Monitoring: Methodology Based on a Statistical Comparison between Remote Sensing Data with Identical Spectral Bands

“…Furthermore, the NIR measurements were similar in the three test areas and for the Red band, the two areas that are statistically different showed differences of less than τ', not satisfying the applicability conditions of the test itself. Therefore, the results revealed a vegetation in full activity with very strong emission in the NIR and a total absorption in the Red, as expected [47,90].…”

“…The derived advantage is a greater awareness in the application of mixed time series to monitor and to discriminate crops and bare soil. As well as S2 [42][43][44][45], MS2 also found several applications due to its centimetric spatial resolution as PA, crop classification and assessment [46][47][48]. Using a corn crop field as a case study, the accurate statistical comparison between S2 and MS2 imagery was based on the Normalized Difference Vegetation Index (NDVI), [49] and the Soil Adjusted Vegetation Index (SAVI), [50].…”

Extraction of Sunflower Lodging Information Based on UAV Multi-Spectral Remote Sensing and Deep Learning

“…The use of RGB images to monitor crop growth has limitations in terms of spectral characteristics, especially a lack of near-infrared bands that can reflect the spectral differences of different canopy structures [16]. Chauhan et al [17] used nine-band multi-spectral UAV remote sensing images combined with the nearest-neighbor algorithm to distinguish between regions of lodging and non-lodging based on differences in spectral reflectance. Kumpumaki et al [18] used the red, green, blue, red-edge, and near-infrared bands of UAV multi-spectral images to classify and map crop lodging, and examined the linear relationship between lodging and crop yield.…”

Automatic Wheat Lodging Detection and Mapping in Aerial Imagery to Support High-Throughput Phenotyping and In-Season Crop Management

“…Lodged wheat that has fallen flat on the ground reduces harvest efficiency and creates difficulties in post-season pest and residue management [7][8][9]. According to previous research, lodging can be caused by extreme weather events (e.g., wind, hail, and rain), water and nutrient stresses, diseases and insect pests, and unfavorable management practices [10,11]. Efforts to reduce lodging have been made by scientists, agricultural professionals and growers in terms of understanding lodging mechanisms, breeding lodging-resistant varieties [12,13], developing prediction models for extreme weather events [14,15], and improving management practices [16,17].…”

“…Compared with the satellite and manned aircrafts platforms, unmanned aerial vehicle (UAV) is advantageous in terms of cost and image resolution, enabling its application in research on breeding, cultivation, management at the field or plot level in precision agriculture [9,22,23]. Thus, UAV has become an emerging platform for crop lodging identification and monitoring in plot and field scales in recent years [10,[24][25][26][27].…”