Seedling emergence rate is one of the important factors affecting crops yield. In this paper, the estimation method of peanut seedling emergence rate based on UAV visible light image was proposed to improve the timeliness and accuracy of peanut seedling emergence rate information acquisition. Firstly, obtained high resolution remote sensing images of June 25, 2021 and July 3, 2021 by self-built low altitude unmanned aerial vehicle platform at summer sowing peanut seedling stage, and the number of peanut plants was identified and counted by combining HSV and Otsu adaptive threshold method. Then, combined with visual interpretation data, linear regression was used to establish a linear relationship model between visual interpretation and computer automatic extraction of peanut plant number. Finally, the information of peanut emergence rate was extracted based on this model to evaluate the sowing effect and quality. The results showed that the correlation coefficient R2 between computer automatic identification of plant number and artificial visual interpretation of plant number was more than 0.85 in two periods. The overall accuracy of peanut plant number extraction was higher than 95 %, and the average relative errors of seedling rate estimation in two stages were 6.0 % and 0.4 %, respectively. The monitoring accuracy of UAV in the middle and late stages of seedling was better than that in the early stage of seedling, which was suitable for peanut seedling rate monitoring. The emergence rate of narrow high ridge sowing was better than wide high ridge sowing. This paper quantitatively analyzed the sowing effect and quality of wide high ridge planting and narrow high ridge planting, and provided technical support for fine management of peanut industrial park.
The height (H) and above-ground biomass (AGB), as important phenotypic parameters, are important guides for growth monitoring and genetic improvement of winter wheat. In this study, digital orthophoto map (DOM) and digital surface model (DSM) of winter wheat at the filling stage were obtained based on different water and nitrogen treatments, using a UAV-mounted multispectral imager. Then a model was established for estimating height and biomass using the partial least squares method. The results showed that (1) at the same nitrogen application level, when each sample in various nitrogen levels was modeled separately, the model for estimating height established by N0 and N6 was better, with R2 of 0.773 and 0.592, respectively; when all samples were modeled together, the R2 was 0.831, which was better than the former. (2) The model of the relationship between height and measured biomass extracted by UAV under different nitrogen levels was robust, and the estimated model R2 and RMSE were 0.598 and 4308.99 kg/ha, and the validated model R2 and RMSE were 0.507 and 4939.31 kg/ha. (3) The R2 and RMSE of the model using biomass per unit plant height with measured biomass were 0.881 and 2335.46 kg/ha, and the R2 and RMSE of the transformed biomass estimation model were 0.876 and 2425.39 kg/ha, which improved the accuracy by 72.7% compared with the estimation model built by directly extracting the height using UAV. This indicates that DSM can accurately estimate the height and biomass of winter wheat, providing a reference for phenotypic studies as well as water and nitrogen management of winter wheat.
As part of the "High-Resolution Earth Observation System," many major projects are being implemented. The first optical satellite (GF-1) in the high-resolution satellite series has completed in-orbit tests and entered the stage of data acquisition. GF-1 owns high resolution and information of wide field view sensor (WFV sensor) and the panchromatic and multispectral sensor (PMS sensor). In this study, GF-1 WFV sensor data with a resolution of 16 m, integrated with Landsat-8 and RapidEye data were selected to recognize maize in Xuchang using Support Vector Machine (SVM) and Spectral Angle Mapper (SAM) method. The results showed that the precision of classification varies greatly among WFV sensors. In particular, WFV3 was of the highest accuracy to identify crops and planting area with accuracy higher than Landsat-8 and close to RapidEye. With regard to WFV1 and WFV4, the application effect was worse and less viable to identify species of complex autumn crops. In brief, the classification accuracy of SVM classifier is better than SAM classifier. It can be also concluded that SVM is more suitable for the identification of crops and planting area of extraction in the study area.
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