Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

Abstract

The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%. Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages.

Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring

“…The bare ground model was represented by a constant, which was the mean pixel value of bare soil in the DSM at a GSD of 1.35 cm. The bare soil was separated from NIR images at 1.35 cm GSD by using the Ostu method [46], which was reported to be able to efficiently and quickly determine the threshold and realize the segmentation of soil and seedling rapeseed [47]. Then, the average PH DSM within each circular buffer was calculated.…”

Remote estimation of crop canopy parameters by statistical regression algorithms for winter rapeseed using Sentinel-2 multispectral images

“…To be able to further distinguish between their performance, each model is applied to the remote sensing images and the estimated values are compared to the measured values. The models that meet the criteria of R 2 > 0.514 [36] and slop > 0.6 are considered only, Table 6 and Fig. 2.…”

“…The DBM was evaluated by fitting parametric models and VIs [7], but the DBM is sampled from the whole growing season of the rapeseed, not only until Inflorescence Emergence stage (BBCH50). The NbPlant was sampled only at emergence and no measurement of accuracy is published [9] or Unmanned Aerial Vehicule (UAV) is used with shape feature recognition or classification [36][37]. The VF is studied with UAV with spatial resolution of 2.5 cm [8].…”

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

“…Canopy color and texture features obtained by UAV platforms (Yue et al, 2019) at high spatial and temporal resolution facilitate phenotyping tasks, since the improvement of image quality and quantity provides detailed information for feature mining and analysis. Accordingly, high-resolution UAV imagery has been adopted for various phenotyping purposes, such as leaf area index estimation (Yao et al, 2017), wheat ear identification (Madec et al, 2019), weed detection (Hung et al, 2014), and seeding performance evaluation in rapeseed (Zhao et al, 2018). In addition, researchers have paid great attention to optimal resolution determination.…”