Prediction of Biomass and N Fixation of Legume-Grass Mixtures Using Sensor Fusion

The role of remote sensing in tropical grassland nutrient estimation: a review

The carbon (C) and nitrogen (N) ratio is a key indicator of nutrient utilization and limitations in rangelands. To understand the distribution of herbivores and grazing patterns, information on grass quality and quantity is important. In heterogeneous environments, remote sensing offers a timely, economical, and effective method for assessing foliar biochemical ratios at varying spatial and temporal scales. Hence, this study provides a synopsis of the advancement in remote sensing technology, limitations, and emerging opportunities in mapping the C:N ratio in rangelands. Specifically, the paper focuses on multispectral and hyperspectral sensors and investigates their properties, absorption features, empirical and physical methods, and algorithms in predicting the C:N ratio in grasslands. Literature shows that the determination of the C:N ratio in grasslands is not in line with developments in remote sensing technologies. Thus, the use of advanced and freely available sensors with improved spectral and spatial properties such as Sentinel 2 and Landsat 8/9 with sophisticated algorithms may provide new opportunities to estimate C:N ratio in grasslands at regional scales, especially in developing countries. Spectral bands in the near-infrared, shortwave infrared, red, and red edge were identified to predict the C:N ratio in plants. New indices developed from recent multispectral satellite imagery, for example, Sentinel 2 aided by cutting-edge algorithms, can improve the estimation of foliar biochemical ratios. Therefore, this study recommends that future research should adopt new satellite technologies with recent development in machine learning algorithms for improved mapping of the C:N ratio in grasslands.

Dry matter production, nitrogen yield and estimation of nitrogen fixation of legumes on vertisols of the Ethiopian highlands

Traditional land management techniques on vertisols frequently lead to soil fertility loss and land degradation. The objective of this study was to evaluate the impact of improved land preparation methods on the dry biomass and nitrogen (N) content of two legume species grown under two phosphorus fertilizer applications. The experimental design employed for these experiments was a randomized complete block design, with six treatments and four replications. Land preparation methods and phosphorus application significantly (P < 0.05) increased biomass production and N content in legumes. Over years and land preparation methods, vetch N accumulation was superior (P < 0.05) to clover and teff (Eragrostis teff). Such a large amount of N accumulation may have a positive contribution to subsequent crops when incorporated into the soil. Land preparation methods and years influenced soil mineral N accumulation, particularly under legumes. The N balance values indicated that it differed among species, land preparation methods, and P treatments over the year. The N balance of vetch +P ranged from 67.1 to 185.9 kg N ha^-1 over years and land preparation methods, whereas the comparable figure for vetch-P was 40.3-141.9 kg N ha^-1. Similarly, the N balance in clover-P ranged from 13.0 to 67.2 kg N ha^-1, and in clover +P from 13.8 to 98.6 kg N ha^-1. Teff's N balance has never exceeded 35 kg ha^-1 over the years.

Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks

The objective of this study is to investigate the potential of novel neural network architectures for measuring the quality and quantity parameters of silage grass swards, using drone RGB and hyperspectral images (HSI), and compare the results with the random forest (RF) method and handcrafted features. The parameters included fresh and dry biomass (FY, DMY), the digestibility of organic matter in dry matter (D-value), neutral detergent fiber (NDF), indigestible neutral detergent fiber (iNDF), water-soluble carbohydrates (WSC), nitrogen concentration (Ncont) and nitrogen uptake (NU); datasets from spring and summer growth were used. Deep pre-trained neural network architectures, the VGG16 and the Vision Transformer (ViT), and simple 2D and 3D convolutional neural networks (CNN) were studied. In most cases, the neural networks outperformed RF. The normalized root-mean-square errors (NRMSE) of the best models were for FY 19% (2104 kg/ha), DMY 21% (512 kg DM/ha), D-value 1.2% (8.6 g/kg DM), iNDF 12% (5.1 g/kg DM), NDF 1.1.% (6.2 g/kg DM), WSC 10% (10.5 g/kg DM), Ncont 9% (2 g N/kg DM), and NU 22% (11.9 N kg/ha) using independent test dataset. The RGB data provided good results, particularly for the FY, DMY, WSC and NU. The HSI datasets provided advantages for some parameters. The ViT and VGG provided the best results with the RGB data, whereas the simple 3D-CNN was the most consistent with the HSI data.

Multisite and Multitemporal Grassland Yield Estimation Using UAV-Borne Hyperspectral Data

Grassland ecosystems can be hotspots of biodiversity and act as carbon sinks while at the same time providing the basis of forage production for ruminants in dairy and meat production. Annual grassland dry matter yield (DMY) is one of the most important agronomic parameters reflecting differences in usage intensity such as number of harvests and fertilization. Current methods for grassland DMY estimation are labor-intensive and prone to error due to small sample size. With the advent of unmanned aerial vehicles (UAVs) and miniaturized hyperspectral sensors, a novel tool for remote sensing of grassland with high spatial, temporal and radiometric resolution and coverage is available. The present study aimed at developing a robust model capable of estimating grassland biomass across a gradient of usage intensity throughout one growing season. Therefore, UAV-borne hyperspectral data from eight grassland sites in North Hesse, Germany, originating from different harvests, were utilized for the modeling of fresh matter yield (FMY) and DMY. Four machine learning (ML) algorithms were compared for their modeling performance. Among them, the rule-based ML method Cubist regression (CBR) performed best, delivering high prediction accuracies for both FMY (nRMSEp 7.6%, Rp2 0.87) and DMY (nRMSEp 12.9%, Rp2 0.75). The model showed a high robustness across sites and harvest dates. The best models were employed to produce maps for FMY and DMY, enabling the detailed analysis of spatial patterns. Although the complexity of the approach still restricts its practical application in agricultural management, the current study proved that biomass of grassland sites being subject to different management intensities can be modeled from UAV-borne hyperspectral data at high spatial resolution with high prediction accuracies.

Quantification of Grassland Biomass and Nitrogen Content through UAV Hyperspectral Imagery—Active Sample Selection for Model Transfer

Accurate retrieval of grassland traits is important to support management of pasture production and phenotyping studies. In general, conventional methods used to measure forage yield and quality rely on costly destructive sampling and laboratory analysis, which is often not viable in practical applications. Optical imaging systems carried as payload in Unmanned Aerial Vehicles (UAVs) platforms have increasingly been proposed as alternative non-destructive solutions for crop characterization and monitoring. The vegetation spectral response in the visible and near-infrared wavelengths provides information on many aspects of its composition and structure. Combining spectral measurements and multivariate modelling approaches it is possible to represent the often complex relationship between canopy reflectance and specific plant traits. However, empirical models are limited and strictly represent characteristics of the observations used during model training, therefore having low generalization potential. A method to mitigate this issue consists of adding informative samples from the target domain (i.e., new observations) to the training dataset. This approach searches for a compromise between representing the variability in new data and selecting only a minimal number of additional samples for calibration transfer. In this study, a method to actively choose new training samples based on their spectral diversity and prediction uncertainty was implemented and tested using a multi-annual dataset. Accurate predictions were obtained using hyperspectral imagery and linear multivariate models (Partial Least Squares Regression—PLSR) for grassland dry matter (DM; R2 = 0.92, RMSE = 3.25 dt ha−1), nitrogen (N) content in % of DM (R2 = 0.58, RMSE = 0.27%) and N-uptake (R2 = 0.91, RMSE = 6.50 kg ha−1). In addition, the number of samples from the target dates added to the training dataset could be reduced by up to 77% and 74% for DM and N-related traits, respectively, after model transfer. Despite this reduction, RMSE values for optimal transfer sets (identified after validation and used as benchmark) were only 20–30% lower than those values obtained after model transfer based on prediction uncertainty reduction, indicating that loss of accuracy was relatively small. These results demonstrate that considerably simple approaches based on UAV hyperspectral data can be applied in preliminary grassland monitoring frameworks, even with limited datasets.

Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non-forest ecosystems

Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R ² of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1-10 ha^-1. Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.

Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning

High-throughput field phenotyping using close remote sensing platforms and sensors for non-destructive assessment of plant traits can support the objective evaluation of yield predictions of large breeding trials. The main objective of this study was to examine the potential of unmanned aerial vehicle (UAV)-based structural and spectral features and their combination in herbage yield predictions across diploid and tetraploid varieties and breeding populations of perennial ryegrass (Lolium perenne L.). Canopy structural (i.e., canopy height) and spectral (i.e., vegetation indices) information were derived from data gathered with two sensors: a consumer-grade RGB and a 10-band multispectral (MS) camera system, which were compared in the analysis. A total of 468 field plots comprising 115 diploid and 112 tetraploid varieties and populations were considered in this study. A modelling framework established to predict dry matter yield (DMY), was used to test three machine learning algorithms, including Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Machines (SVM). The results of the nested cross-validation revealed: (a) the fusion of structural and spectral features achieved better DMY estimates as compared to models fitted with structural or spectral data only, irrespective of the sensor, ploidy level or machine learning algorithm applied; (b) models built with MS-based predictor variables, despite their lower spatial resolution, slightly outperformed the RGB-based models, as lower mean relative root mean square error (rRMSE) values were delivered; and (c) on average, the RF technique reported the best model performances among tested algorithms, regardless of the dataset used. The approach introduced in this study can provide accurate yield estimates (up to an RMSE = 308 kg ha−1) and useful information for breeders and practical farm-scale applications.

Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

Agroforestry systems (AFS) can provide positive ecosystem services while at the same time stabilizing yields under increasingly common drought conditions. The effect of distance to trees in alley cropping AFS on yield-related crop parameters has predominantly been studied using point data from transects. Unmanned aerial vehicles (UAVs) offer a novel possibility to map plant traits with high spatial resolution and coverage. In the present study, UAV-borne red, green, blue (RGB) and multispectral imagery was utilized for the prediction of whole crop dry biomass yield (DM) and leaf area index (LAI) of barley at three different conventionally managed silvoarable alley cropping agroforestry sites located in Germany. DM and LAI were modelled using random forest regression models with good accuracies (DM: R² 0.62, nRMSEp 14.9%, LAI: R² 0.92, nRMSEp 7.1%). Important variables for prediction included normalized reflectance, vegetation indices, texture and plant height. Maps were produced from model predictions for spatial analysis, showing significant effects of distance to trees on DM and LAI. Spatial patterns differed greatly between the sampled sites and suggested management and soil effects overriding tree effects across large portions of 96 m wide crop alleys, thus questioning alleged impacts of AFS tree rows on yield distribution in intensively managed barley populations. Models based on UAV-borne imagery proved to be a valuable novel tool for prediction of DM and LAI at high accuracies, revealing spatial variability in AFS with high spatial resolution and coverage.