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The tractor mounted LiDAR system used in the current study showing the boom system and LiDAR sensor mounting positions, with major components annotated (A) and a closer view of one of the mounted LiDAR sensors (B).
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Above-ground biomass (AGB) is a trait with much potential for exploitation within wheat breeding programs and is linked closely to canopy height (CH). However, collecting phenotypic data for AGB and CH within breeding programs is labor intensive, and in the case of AGB, destructive and prone to assessment error. As a result, measuring these traits...
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The development of high-throughput genotyping and phenotyping has provided access to many tools to accelerate plant breeding programs. Unmanned Aerial Systems (UAS)-based remote sensing is being broadly implemented for field-based high-throughput phenotyping due to its low cost and the capacity to rapidly cover large breeding populations. The Struc...
Citations
... However, canopy height alone is insufficient for accurate biomass prediction when height variation is minimal. Other factors, such as point cloud volume and 3D indices, correlate strongly with measured biomass [2][3][4]9,16]. Point density, which captures the 3D structure of crops, has been explored through various methods [4,[16][17][18][19]. Notably, Jimenez-Berni et al. [2] introduced a voxel-based method (3DVI), dividing point clouds into voxels to calculate non-empty voxel ratios. This method is considered a 'gold standard' for non-learning-based biomass estimation approaches [5,16]. ...
... Other factors, such as point cloud volume and 3D indices, correlate strongly with measured biomass [2][3][4]9,16]. Point density, which captures the 3D structure of crops, has been explored through various methods [4,[16][17][18][19]. Notably, Jimenez-Berni et al. [2] introduced a voxel-based method (3DVI), dividing point clouds into voxels to calculate non-empty voxel ratios. ...
... Point density, which captures the 3D structure of crops, has been explored through various methods [4,[16][17][18][19]. Notably, Jimenez-Berni et al. [2] introduced a voxel-based method (3DVI), dividing point clouds into voxels to calculate non-empty voxel ratios. This method is considered a 'gold standard' for non-learning-based biomass estimation approaches [5,16]. Recent advancements in deep neural networks offer promising improvements in biomass prediction. ...
Crop biomass offers crucial insights into plant health and yield, making it essential for crop science, farming systems, and agricultural research. However, current measurement methods, which are labor-intensive, destructive, and imprecise, hinder large-scale quantification of this trait. To address this limitation, we present a biomass prediction network (BioNet), designed for adaptation across different data modalities, including point clouds and drone imagery. Our BioNet, utilizing a sparse 3D convolutional neural network (CNN) and a transformer-based prediction module, processes point clouds and other 3D data representations to predict biomass. To further extend BioNet for drone imagery, we integrate a neural feature field (NeFF) module, enabling 3D structure reconstruction and the transformation of 2D semantic features from vision foundation models into the corresponding 3D surfaces. For the point cloud modality, BioNet demonstrates superior performance on two public datasets, with an approximate 6.1% relative improvement (RI) over the state-of-the-art. In the RGB image modality, the combination of BioNet and NeFF achieves a 7.9% RI. Additionally, the NeFF-based approach utilizes inexpensive, portable drone-mounted cameras, providing a scalable solution for large field applications.
... Crop height provides an intuitive representation of the vertical structure distribution of crop plants. Previous studies have demonstrated that crop height for wheat (Lu et al., 2019;Walter et al., 2019) and maize (Shu et al., 2023) exhibit correlation with AGB. Over 90% AIH reflects the point cloud distribution of the vegetation canopy, offering a relatively accurate representation of crop height. ...
... Over 90% AIH reflects the point cloud distribution of the vegetation canopy, offering a relatively accurate representation of crop height. AIH and AGB exhibit a moderate correlation (Walter et al., 2019). In the feature importance analysis, the majority of AIH features exhibit high importance in the AGB estimation models, with only 40% AIH features having lower importance. ...
Introduction
Crop height and above-ground biomass (AGB) serve as crucial indicators for monitoring crop growth and estimating grain yield. Timely and accurate acquisition of wheat crop height and AGB data is paramount for guiding agricultural production. However, traditional data acquisition methods suffer from drawbacks such as time-consuming, laborious and destructive sampling.
Methods
The current approach to estimating AGB using unmanned aerial vehicles (UAVs) remote sensing relies solely on spectral data, resulting in low accuracy in estimation. This method fails to address the ill-posed inverse problem of mapping from two-dimensional to three-dimensional and issues related to spectral saturation. To overcome these challenges, RGB and multispectral sensors mounted on UAVs were employed to acquire spectral image data. The five-directional oblique photography technique was utilized to construct the three-dimensional point cloud for extracting crop height.
Results and Discussion
This study comparatively analyzed the potential of the mean method and the Accumulated Incremental Height (AIH) method in crop height extraction. Utilizing Vegetation Indices (VIs), AIH and their feature combinations, models including Random Forest Regression (RFR), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRT), Support Vector Regression (SVR) and Ridge Regression (RR) were constructed to estimate winter wheat AGB. The research results indicated that the AIH method performed well in crop height extraction, with minimal differences between 95% AIH and measured crop height values were observed across various growth stages of wheat, yielding R² ranging from 0.768 to 0.784. Compared to individual features, the combination of multiple features significantly improved the model’s estimate accuracy. The incorporation of AIH features helps alleviate the effects of spectral saturation. Coupling VIs with AIH features, the model’s R² increases from 0.694-0.885 with only VIs features to 0.728-0.925. In comparing the performance of five machine learning algorithms, it was discovered that models constructed based on decision trees were superior to other machine learning algorithms. Among them, the RFR algorithm performed optimally, with R² ranging from 0.9 to 0.93.
Conclusion
In conclusion, leveraging multi-source remote sensing data from UAVs with machine learning algorithms overcomes the limitations of traditional crop monitoring methods, offering a technological reference for precision agriculture management and decision-making.
... In research trials and surveys, AGB is conventionally measured by destructively harvesting physical samples from plots, which is prone to errors stemming from the representativeness of sampling areas and technical methodologies of sampling and drying materials. In many situations such as field breeding programs, destructive harvesting through the season is undesirable due to the loss of plot area and consequent edge effects on crop growth or infeasible due to large numbers of plots or remoteness of trial locations (David et al., 2022;Walter et al., 2019). Remote and proximal sensing techniques allow nondestructive data collection on plant canopy traits, such as spectral reflectance and architecture information with the potential to estimate AGB dynamics under field conditions (Bendig et al., 2015;Jimenez-Berni et al., 2018). ...
Aboveground biomass (AGB) of plants is an agroecological indicator that can be used to monitor crop growth status and quantify biomass carbon stock in agricultural ecosystems. Although satellite remote sensing data and crop models are widely employed to estimate AGB dynamics, their application in small-scale research plots is often constrained by the unavailability of freely and timely high-resolution satellite imageries and the challenge of acquiring reliable model inputs like initial soil water and nitrogen content. This study integrated a crop growth model (APSIM-Wheat) and radiative transfer model (PROSAIL) to estimate AGB dynamics at a small plot scale (ca. 5 to 20 m2) within variety trials (ca. 1 to 2 ha) by assimilating trial-level remote sensing data into the hybrid APSIM-PROSAIL model. The APSIM-PROSAIL integration, developed by deriving PROSAIL input parameters from APSIM-Wheat output variables, was verified by evaluating its capacity to reproduce trial-level Sentinel-2 (S2) NDVI dynamics across 30 variety trials in 2020. The comparison of the simulated and observed trial-level S2 NDVI dynamics yielded an overall RRMSE of 18.2% (n = 646 observations), suggesting the potential of using observed S2 NDVI dynamics to constrain APSIM-PROSAIL and estimate environment variables in cropping systems. This constraint was subsequently assessed in 28 variety trials in 2021, where APSIM-PROSAIL inversely estimated unknown trial-specific variables including initial soil water and nitrogen content across the trials. Using the estimated initial soil status and the plot information including genotypes and management as sown, we demonstrated that this method could also facilitate accurate retrievals of plot-level AGB dynamics with an overall RRMSE of 20.9% (n = 976 measurements). The proposed APSIM-PROSAIL demonstrated the capability to accurately retrieve plot-level AGB dynamics and reproduce trial-level S2 NDVI dynamics across a broad and diverse range of variety trials in the Australian Wheatbelt, indicating its potential utility to facilitate biomass carbon stock assessment. It holds the potential as a tool to explore the relationships between canopy spectral information and crop traits in response to genotype-environment-management interactions in agricultural ecosystems. The method enables the examinations of within-field variabilities for crop traits through retrieving them at small spatial scales from coarse remote sensing data.
... The study was conducted on a 10.1 ha bermudagrass field (34 • 39 ′ 26 ′′ N 82 • 43 ′ 45 ′′ W), out of which two sections of 0.4 ha were used for the data collection on 16 August 2023 (Section 1) and 1 September 2023 (Section 2), respectively ( Figure 1). The data was collected Remote Sens. 2024, 16,2646 4 of 17 at solar noon to ensure optimal light conditions and minimum shadow effects during data capturing. Initially, the plots were marked in the section with survey flags. ...
... Remote Sens. 2024,16, 2646 ...
Accurate information about the amount of standing biomass is important in pasture management for monitoring forage growth patterns, minimizing the risk of overgrazing, and ensuring the necessary feed requirements of livestock. The morphological features of plants, like crop height and density, have been proven to be prominent predictors of crop yield. The objective of this study was to evaluate the effectiveness of stereovision-based crop height and vegetation coverage measurements in predicting the aboveground biomass yield of bermudagrass (Cynodon dactylon) in a pasture. Data were collected from 136 experimental plots within a 0.81 ha bermudagrass pasture using an RGB-depth camera mounted on a ground rover. The crop height was determined based on the disparity between images captured by two stereo cameras of the depth camera. The vegetation coverage was extracted from the RGB images using a machine learning algorithm by segmenting vegetative and non-vegetative pixels. After camera measurements, the plots were harvested and sub-sampled to measure the wet and dry biomass yields for each plot. The wet biomass yield prediction function based on crop height and vegetation coverage was generated using a linear regression analysis. The results indicated that the combination of crop height and vegetation coverage showed a promising correlation with aboveground wet biomass yield. However, the prediction function based only on the crop height showed less residuals at the extremes compared to the combined prediction function (crop height and vegetation coverage) and was thus declared the recommended approach (R² = 0.91; SeY= 1824 kg-wet/ha). The crop height-based prediction function was used to estimate the dry biomass yield using the mean dry matter fraction.
... As one of the agricultural application fields of LiDAR, crop phenotype feature extraction had also become a research focus of many scholars. Among the research on crop population phenotypes, many studies concentrated on crop canopy height [9][10][11], canopy biomass [12][13][14], and leaf area index (LAI) [15,16] using unmanned aerial vehicle (UAV) systems. Luo et al. estimated maize LAI, canopy height, and aboveground biomass using the combined hyperspectral imagery and LiDAR pseudo-waveforms, showing the strong liner correlation between LiDAR variables and LAI, height, and biomass [17]. ...
To quickly obtain rice plant phenotypic traits, this study put forward the computational process of six rice phenotype features (e.g., crown diameter, perimeter of stem, plant height, surface area, volume, and projected leaf area) using terrestrial laser scanning (TLS) data, and proposed the extraction method for the tiller number of rice plants. Specifically, for the first time, we designed and developed an automated phenotype extraction tool for rice plants with a three-layer architecture based on the PyQt5 framework and Open3D library. The results show that the linear coefficients of determination (R2) between the measured values and the extracted values marked a better reliability among the selected four verification features. The root mean square error (RMSE) of crown diameter, perimeter of stem, and plant height is stable at the centimeter level, and that of the tiller number is as low as 1.63. The relative root mean squared error (RRMSE) of crown diameter, plant height, and tiller number stays within 10%, and that of perimeter of stem is 18.29%. In addition, the user-friendly automatic extraction tool can efficiently extract the phenotypic features of rice plant, and provide a convenient tool for quickly gaining phenotypic trait features of rice plant point clouds. However, the comparison and verification of phenotype feature extraction results supported by more rice plant sample data, as well as the improvement of accuracy algorithms, remain as the focus of our future research. The study can offer a reference for crop phenotype extraction using 3D point clouds.
... Manual measurements were taken every 25 cm on the scan path, and the average value was taken as the value of CH a . Moreover, three scans (Scans 1, Scans 2, and Scans 3) were conducted for each case to study the repeatability (Walter et al., 2019) of the ultrasonic measurement. The measurements of Scans 1 and Scans 2 were used to compare the repeatability in the same direction, and the measurements of Scans 1 and Scans 3 were used to compare the repeatability in the opposite direction. ...
... The wheat leaves in the mature stage withered when the canopy height tended to be stable, and this situation did not comply with the trend that canopy height increased with increasing coverage ( Figure 4B). In addition, crop height is stable during the late growth stages, and observing canopy height before these stages is more practical for production (Scotford and Miller, 2004;Aziz et al., 2004;Walter et al., 2019). Therefore, only the crop growth stage prior to leaf withering was studied when calibrating ultrasonic measurements to reduce the complexity of verification. ...
... In addition to ultrasonic measurements, the technologies widely used in canopy height measurement include LiDAR (Jimenez-Berni et al., 2018;Walter et al., 2019) and UAS imagery (Friedli et al., 2016;Madec et al., 2017). Among them, LiDAR is based on the principle of time of fly (Sun et al., 2017). ...
Canopy height serves as an important dynamic indicator of crop growth in the decision-making process of field management. Compared with other commonly used canopy height measurement techniques, ultrasonic sensors are inexpensive and can be exposed in fields for long periods of time to obtain easy-to-process data. However, the acoustic wave characteristics and crop canopy structure affect the measurement accuracy. To improve the ultrasonic sensor measurement accuracy, a four-year (2018−2021) field experiment was conducted on maize and wheat, and a measurement platform was developed. A series of single-factor experiments were conducted to investigate the significant factors affecting measurements, including the observation angle (0−60°), observation height (0.5−2.5 m), observation period (8:00−18:00), platform moving speed with respect to the crop (0−2.0 m min⁻¹), planting density (0.2−1 time of standard planting density), and growth stage (maize from three−leaf to harvest period and wheat from regreening to maturity period). The results indicated that both the observation angle and planting density significantly affected the results of ultrasonic measurements (p-value< 0.05), whereas the effects of other factors on measurement accuracy were negligible (p-value > 0.05). Moreover, a double-input factor calibration model was constructed to assess canopy height under different years by utilizing the normalized difference vegetation index and ultrasonic measurements. The model was developed by employing the least-squares method, and ultrasonic measurement accuracy was significantly improved when integrating the measured value of canopy heights and the normalized difference vegetation index (NDVI). The maize measurement accuracy had a root mean squared error (RMSE) ranging from 81.4 mm to 93.6 mm, while the wheat measurement accuracy had an RMSE from 37.1 mm to 47.2 mm. The research results effectively combine stable and low-cost commercial sensors with ground-based agricultural machinery platforms, enabling efficient and non-destructive acquisition of crop height information.
... Such ground-based platforms have been used frequently throughout the literature to estimate crop parameters that require a higher resolution of data, such as spike counting [24], 3D reconstruction of canopies [25], and determining growth stages [26]. More recently, the use of LIDAR as a phenotyping tool has also been tested to quantify crop traits such as canopy biomass and canopy height [27]. In the work reported below, we have chosen the more laborious but more accurate alternative of ground-based observation of wheat fields in order to capture as much detail as possible. ...
As the global population and resource scarcity simultaneously increase, the pressure on plant breeders and growers to maximise the effectiveness of their operations is immense. In this article, we explore the usefulness of image-based data collection and analysis of field experiments consisting of multiple field sites, plant varieties, and treatments. The goal of this approach is to determine whether the noninvasive acquisition and analysis of image data can be used to find relationships between the canopy traits of field experiments and environmental factors. Our results are based on data from three field trials in 2016, 2017, and 2018 in South Australia. Image data were supplemented by environmental data such as rainfall, temperature, and soil composition in order to explain differences in growth and the development of plants across field trials. We have shown that the combination of high-throughput image-based data and independently recorded environmental data can reveal valuable connections between the variables influencing wheat crop growth; meanwhile, further studies involving more field trials under different conditions are required to test hypotheses and draw statistically significant conclusions. This work highlights some of the more responsive traits and their dependencies.
... Thus, the measurement method determines the mean height of a crop surface area and not the maximum height of single plants or awns. In general, canopy height is a reliable estimate of above-ground plant biomass across ecosystems and accordingly, therefore, indicates growth rates [35] and grain yield production [36]. In addition, the current developmental stage of the plants is categorized according to the general stages of the BBCH scale. ...
Precision agriculture relies on understanding crop growth dynamics and plant responses to short-term changes in abiotic factors. In this technical note, we present and discuss a technical approach for cost-effective, non-invasive, time-lapse crop monitoring that automates the process of deriving further plant parameters, such as biomass, from 3D object information obtained via stereo images in the red, green, and blue (RGB) color space. The novelty of our approach lies in the automated workflow, which includes a reliable automated data pipeline for 3D point cloud reconstruction from dynamic scenes of RGB images with high spatio-temporal resolution. The setup is based on a permanent rigid and calibrated stereo camera installation and was tested over an entire growing season of winter barley at the Global Change Experimental Facility (GCEF) in Bad Lauchstädt, Germany. For this study, radiometrically aligned image pairs were captured several times per day from 3 November 2021 to 28 June 2022. We performed image preselection using a random forest (RF) classifier with a prediction accuracy of 94.2% to eliminate unsuitable, e.g., shadowed, images in advance and obtained 3D object information for 86 records of the time series using the 4D processing option of the Agisoft Metashape software package, achieving mean standard deviations (STDs) of 17.3–30.4 mm. Finally, we determined vegetation heights by calculating cloud-to-cloud (C2C) distances between a reference point cloud, computed at the beginning of the time-lapse observation, and the respective point clouds measured in succession with an absolute error of 24.9–35.6 mm in depth direction. The calculated growth rates derived from RGB stereo images match the corresponding reference measurements, demonstrating the adequacy of our method in monitoring geometric plant traits, such as vegetation heights and growth spurts during the stand development using automated workflows.
... This observation was also found by [59], who employed UAV RGB images to estimate maize plant heights, indicating that the estimated values were relatively lower when contrasted with field observations. This discrepancy could stem from the limited accuracy of the dense point cloud in capturing the uppermost point of the maize plant, as indicated by [60]. To enhance the quality of information extracted from the DEM, a potential solution is to strategically distribute more ground control points within the study area, thereby mitigating model errors and refining spatial accuracy, as suggested by [29]. ...
The integration of aerial monitoring, utilizing both unmanned aerial vehicles (UAVs) and satellites, alongside sophisticated machine learning algorithms, has witnessed a burgeoning prevalence within contemporary agricultural frameworks. This study endeavors to systematically explore the inherent potential encapsulated in high-resolution satellite imagery, concomitantly accompanied by an RGB camera seamlessly integrated into an UAV. The overarching objective is to elucidate the viability of this technological amalgamation for accurate maize plant height estimation, facilitated by the application of advanced machine learning algorithms. The research involves the computation of key vegetation indices—NDVI, NDRE, and GNDVI—extracted from PlanetScope satellite images. Concurrently, UAV-based plant height estimation is executed using digital elevation models (DEMs). Data acquisition encompasses images captured on days 20, 29, 37, 44, 50, 61, and 71 post-sowing. The study yields compelling results: (1) Maize plant height, derived from DEMs, demonstrates a robust correlation with manual field measurements (r = 0.96) and establishes noteworthy associations with NDVI (r = 0.80), NDRE (r = 0.78), and GNDVI (r = 0.81). (2) The random forest (RF) model emerges as the frontrunner, displaying the most pronounced correlations between observed and estimated height values (r = 0.99). Additionally, the RF model’s superiority extends to performance metrics when fueled by input parameters, NDVI, NDRE, and GNDVI. This research underscores the transformative potential of combining satellite imagery, UAV technology, and machine learning for precision agriculture and maize plant height estimation.
... Traditional methods for estimating biochemical content typically involve destructive sampling of field leaves, followed by laboratory-based analysis [9]. For instance, dry matter and water content are deduced from the weight difference between fresh and dried samples [10]. Chlorophyll content is commonly determined via chemical extractions and spectrophotometry [11], while the Dumas method is frequently used for nitrogen content measurement [12]. ...
... These techniques are selected for their precision and reliability. Yet, they are labor-intensive, require specialized expertise, and have limited throughput [10,13,14]. The need to transport samples from field to laboratory before analysis further complicates the measurement process. ...
Accurate assessment of crop biochemical profiles plays a crucial role in diagnosing their physiological status. The conventional destructive methods, although reliable, demand extensive laboratory work for measuring various traits. On the other hand, nondestructive techniques, while efficient and adaptable, often suffer from reduced precision due to the intricate interplay of the field environment and canopy structure. Striking a delicate balance between efficiency and accuracy, we have developed the Bio-Master phenotyping system. This system is capable of simultaneously measuring four vital biochemical components of the canopy profile: dry matter, water, chlorophyll, and nitrogen content. Bio-Master initiates the process by addressing structural influences, through segmenting the fresh plant and then further chopping the segment into uniform small pieces. Subsequently, the system quantifies hyperspectral reflectance and fresh weight over the sample within a controlled dark chamber, utilizing an independent light source. The final step involves employing an embedded estimation model to provide synchronous estimates for the four biochemical components of the measured sample. In this study, we established a comprehensive training dataset encompassing a wide range of rice varieties, nitrogen levels, and growth stages. Gaussian process regression model was used to estimate biochemical contents utilizing reflectance data obtained by Bio-Master. Leave-one-out validation revealed the model’s capacity to accurately estimate these contents at both leaf and plant scales. With Bio-Master, measuring a single rice plant takes approximately only 5 min, yielding around 10 values for each of the four biochemical components across the vertical profile. Furthermore, the Bio-Master system allows for immediate measurements near the field, mitigating potential alterations in plant status during transportation and processing. As a result, our measurements are more likely to faithfully represent in situ values. To summarize, the Bio-Master phenotyping system offers an efficient tool for comprehensive crop biochemical profiling. It harnesses the benefits of remote sensing techniques, providing significantly greater efficiency than conventional destructive methods while maintaining superior accuracy when compared to nondestructive approaches.
