BackgroundAbove-ground biomass (AGB) is a basic agronomic parameter for field investigation and is frequently used to indicate crop growth status, the effects of agricultural management practices, and the ability to sequester carbon above and below ground. The conventional way to obtain AGB is to use destructive sampling methods that require manual harvesting of crops, weighing, and recording, which makes large-area, long-term measurements challenging and time consuming. However, with the diversity of platforms and sensors and the improvements in spatial and spectral resolution, remote sensing is now regarded as the best technical means for monitoring and estimating AGB over large areas.ResultsIn this study, we used structural and spectral information provided by remote sensing from an unmanned aerial vehicle (UAV) in combination with machine learning to estimate maize biomass. Of the 14 predictor variables, six were selected to create a model by using a recursive feature elimination algorithm. Four machine-learning regression algorithms (multiple linear regression, support vector machine, artificial neural network, and random forest) were evaluated and compared to create a suitable model, following which we tested whether the two sampling methods influence the training model. To estimate the AGB of maize, we propose an improved method for extracting plant height from UAV images and a volumetric indicator (i.e., BIOVP). The results show that (1) the random forest model gave the most balanced results, with low error and a high ratio of the explained variance for both the training set and the test set. (2) BIOVP can retain the largest strength effect on the AGB estimate in four different machine learning models by using importance analysis of predictors. (3) Comparing the plant heights calculated by the three methods with manual ground-based measurements shows that the proposed method increased the ratio of the explained variance and reduced errors.ConclusionsThese results lead us to conclude that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB. This work suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters.Electronic supplementary materialThe online version of this article (10.1186/s13007-019-0394-z) contains supplementary material, which is available to authorized users.

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The application of high-throughput phenotyping (HTP) techniques based on unmanned aerial vehicle (UAV) remote-sensing platforms to study large-scale population breeding opens the way to more efficient acquisition of dynamic phenotypic traits and provides new tools that should help close the gap between genotyping and traditional field-phenotyping methods. Toward this end we used a field UAV-HTP platform to deploy a RGB high-resolution camera to acquire time-series images. By using three-dimensional reconstructed point cloud models, we developed a repeatable processing workflow to extract plant height from time-series images. The plant height determined by the UAV-HTP platform correlated strongly with that measured manually. The plant heights estimated at various growth stages form temporal profiles that give insights into changes and trends in genotyping. Based on fuzzy c-means clustering analysis, we extract the typical dynamic patterns in phenotypic traits (i.e., plant height, average rate of growth of plant height, and rate of contribution of plant height) hidden in the temporal profiles. The fuzzy c-means clustering and set-intersection operation were first applied to analyze the temporal profile to identify how plant-height patterns change and to detect differences in phenotypic variability among the genotypes. The results revealed the capacity of UAV remote sensing to easily evaluate field traits on multiple timescales, for a few breeding plots or for 1000s of breeding plots.

Purpose. Study of the effectiveness of topographical survey methods when solving the main tasks of surveying support for the disturbed lands reclamation.Methods. Comparative analysis of the topographical survey results, which was conducted with the use of electronic total station and a surface laser scanner during reclamation. The heap leaching dump at the Belaya Gorka Site of the Rodnikovoye Field has been chosen as an object for topographical survey. To compare adequately, the electronic total station and the laser scanner were chosen of the same accuracy class. The determination of the values accuracy of the area and volume of an object during a tacheometric survey depends on the discreteness of surveying pickets. In practice, the density of the pickets' arrangement is limited by the working capacity of the surveying crew, which, as a rule, is several hundred pickets per day, and the density is two or three survey points per 100 m 2 of the object. To determine the dependence of measurement accuracy on the pickets' density during the tacheometric survey, it was carried out at four different scales, with the distance between the pickets from 5 to 25 meters. The density of points (pickets) of a surface laser scanner, which was used in the studies, is 500 points per 100 m 2 of survey area. Findings.Based on the results of the tacheometric survey and surface laser scanning of the heap leaching dump, two variants of the topographic maps of the surface and its smoothed digital model have been obtained. Detailed surface laser scanning at an increased level in comparison with a tacheometric survey improves the topographic map accuracy. Improved accuracy when determining the volume on a survey scale of 1:500 -1:2000 is 12%.Originality. A new concept for topographical surveying is proposed when solving the surveying problems of reclaiming the disturbed lands, based on the methods of surface laser scanning.Practical implications. Use of the surface laser scanning technology makes possible to obtain the prompt threedimensional visualization of the surveyed area, to ensure high accuracy and degree of detailed survey, to increase the working capacity and field surveying conditions, to solve the main tasks of surveying support of the disturbed lands reclamation in the shortest possible time and with the required surveying quality.