Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data

Abstract

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.

Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

“…This research furthermore showed that there are limitations to the biomass estimation for certain crops and that models developed for a specific crop cannot directly be used for other crops, and generic models should be used with care. For a fully operational approach, an effort should be made towards combining LiDAR with hyperspectral data, as mentioned by [22,23,26], so models can be trained for a range of crops. This will increase both the accuracy and general applicability of high-throughput biomass estimation models.…”

“…Using, for example, a PLS regression as mentioned in [21], or using machine learning approaches like [22], where a deep convolutional network was used to predict biomass using RGB imagery. Or the machine learning approach of [23], where they used a range of spectral indices as predictors to estimate above ground biomass with an NRMSE of 24.95%. Combining these different methodologies with a predictor such as the 3DPI indicator could result in a better prediction of biomass.…”

“…Therefore, limiting this general applicability of the model, the 3DPI appears to be crop sensitive. Possible other factors, such a height statistics derived from the LiDAR 3D point cloud as analyzed in [21] or the spectral vegetation indices of [23], could be helpful to make the 3DPI method less crop sensitive, which is something for further research.…”

Development and Validation of a Model to Combine NDVI and Plant Height for High-Throughput Phenotyping of Herbage Yield in a Perennial Ryegrass Breeding Program

“…Small, low-cost unmanned aerial systems (UAS) and satellite technologies (mainly equipped with multispectral sensors) are mainly deployed to execute the phenotyping task. The UAS system allows proximal sensing of plant phenotypes at a small experimental unit level with high spatial and spectral resolution [20][21][22].Another alternative extensively used to estimate herbage yield is plant height [23][24][25]. Plant structural information metrics may be accessed from terrestrial and aerial sensors.…”

Unoccupied aerial system enabled functional modeling of maize (Zea mays L.) height reveals dynamic expression of loci associated to temporal growth

“…UAS height estimates of maize have previously been validated using correlations to traditional manual measurements and evidence of equivalent or greater phenotypic variation partitioned to genetic factors [7, 9, 15, 16]. To our knowledge the majority of reported field based phenotyping of maize with HTP platforms has focused on hybrid trials [6-9, 15, 17-19] but, limited reports have been published on the evaluation of inbred trials [20–22], specifically genetic mapping populations. Inbred lines in maize are substantially shorter and have less biomass than hybrids, lacking heterosis.…”