Results and Discussion

The measured dry biomass across all five fields in this study ranged from 320 to 9,200 kg ha⁻¹ across all sampling dates. In the field where the calibration measurements were taken throughout the season, the measured dry biomass range was 420 to 8,000 kg ha⁻¹, with crop height ranging from 10 to 100 cm. The average crop height in the other four fields (referred to as Fields 1 to 4 for the remainder of the discussion) ranged from 35 to 120 cm, although in the majority of sampling points, the crop was 50- to 100-cm tall. The variation in biomass across fields allowed analysis of a broad range of crop biomass levels that could be detected with UAV imagery (Figure 3). However, crop height alone was a poor predictor of biomass, as there was considerable variability in biomass at each crop height. For example, biomass values for quadrats where the average height was approximately 100 cm ranged from 2,000 to 9,000 kg ha⁻¹ (Figure 4).

Figure 3.

Unmanned aerial vehicle (UAV) images of cereal rye at biomass levels of 7,200 kg ha⁻¹ (left), 5,200 kg ha⁻¹ (center), and 1,200 kg ha⁻¹ (right). The yellow squares indicate 0.25-m² sampling quadrats. All images were acquired at an altitude of 15 m.

Figure 4.

Nonlinear regression of measured dry biomass and crop height. Each point represents one 0.25-m² quadrat. Data are pooled across all four fields (n = 320).

Due to the significant range in crop height, both throughout the season and at the time of termination, and the fact that the SfM algorithm did not detect lower canopy layers when the crop was taller, integrating height with point cloud pixel density was necessary for producing a more accurate estimation of biomass. In contrast to the regression of crop height and measured biomass (Figure 4), the calibration model comparing DH and biomass showed an approximately 1:1 relationship, with an r² = 0.813 (Figure 5). Based on the predictive model using DH, the predicted biomass and measured biomass across all data points (n = 320) were linearly related with r² = 0.713 (Figure 6).

Figure 5.

Linear regression of density–height (DH) index and dry biomass from the calibration field. RMSE, root mean-square error.

Figure 6.

Linear regression of predicted vs. measured cereal rye dry biomass (solid line) from 320 quadrats (circles) pooled across four fields. The dashed line indicates the slope of a theoretical 1:1 relationship between predicted and measured biomass. RMSE, root mean-square error.

As seen in the created field maps for Fields 1 to 4, the high and low areas of measured biomass were in locations similar to the predicted high and low areas (Figure 7). However, there were noticeable differences among fields in the robustness of the kriging model. In Field 1, the semivariograms for measured and predicted biomass showed a similarly clear spatial dependence in the linear portion of the graph throughout a range of 10.8 and 12.0 m, respectively (Figure 8; Table 1). In addition, the difference between predicted and measured biomass in Field 1 was exclusively either zero or negative, indicating that the prediction was either equal to or larger than the measured biomass (Figure 7). Both kriging models also had nugget:sill ratios indicating moderate spatial dependence in the data (Table 1). The accuracy of the biomass predictions for Field 1 appears to be due to greater patchiness and spatial dependence, as well as an overall wider range in biomass values compared with the other four fields. For example, in Fields 2 and 3, the overall variability in measured biomass was considerably lower, and there was much greater discrepancy between the kriging models for measured and predicted biomass (Figure 8). In Field 2, the data for measured biomass showed moderate spatial dependence and leveled off at 42.2 m, while the data for predicted biomass showed weak spatial dependence and leveled off at 3.7 m (Figure 8; Table 1). In Field 3, both measured and predicted biomass showed moderate spatial dependence based on their nugget:sill ratios, with the autocorrelation leveling off at 18.6 m and 5.4 m, respectively (Figure 8; Table 1). Interestingly, in Field 4, which had more variability in measured biomass compared with Fields 2 and 3, there was a similarly robust prediction of biomass, as in Field 1. Field 4 also showed an extremely high level of spatial dependence for both measured and predicted biomass, with a nugget:sill ratio of just 0.1%. In addition, the spatial correlation leveled off at just 5.4 m for both measured and predicted data (Table 1). This is also apparent in the field map for Field 4, which shows clearly similar patterns and sizes of high and low biomass patches for measured and predicted data (Figure 7).

Figure 7.

Kriging maps of measured dry biomass, predicted dry biomass, and the difference between predicted and actual biomass, superimposed on unmanned aerial vehicle (UAV) images of four fields.

Figure 8.

Kriging semivariograms of measured biomass and predicted biomass for Fields 1–4, showing fitted models (solid blue lines) for spatial autocorrelation between pairs of measured sampling points (red dots).

Table 1.

Summary of statistics for kriging models for Fields 1–4

^a RMSE, root mean-square error.

Across all four fields, in areas of higher biomass, the predicted biomass was approximately 1,000 to 1,500 kg ha⁻¹ higher than what was measured and, in a few cases, by as much as 3,140 kg ha⁻¹ (i.e., orange and red in biomass difference maps; Figure 7). Conversely, in lower-biomass areas, the model underestimated the biomass at similar levels and, in extreme cases, by as much as 2,837 kg ha⁻¹ (i.e., light and dark green in biomass difference maps; Figure 7). The linear regression between predicted and estimated biomass also reflects this trend, as the slope was steeper than it would have been at 1:1 (Figure 6). This is partially due to the bias introduced by calculating predicted biomass by multiplying point cloud pixel density by average crop height within each quadrat. In areas of lower biomass, where the crop had sparse cover, the SfM algorithm was less likely to detect all of the individual plants and leaves, especially if there was any excessive movement of the foliage caused by wind or trampling. Therefore, even in areas where the crop was sparse but tall, biomass predictions were lower due to the low pixel density. In higher-biomass areas, the individual plants were approximately 100-cm tall (Figure 4). Even in cases where there was variation in height within a single quadrat, the fact that crop height was averaged across three plants meant that the tallest measurements likely skewed the average for that quadrat. This introduced a bias in high crop height values, which caused overpredictions of biomass by multiplying pixel density by a single uniform height within that quadrat.

Despite the variability in biomass predictions, the present study serves as a proof of concept to identify the potential for and challenges to improving prediction accuracy. Further testing in large commercial farms, over multiple growing seasons, and in multiple locations is warranted. In addition, it is still unknown how crop management variability could modify the relationship between SfM image generation and biomass. Factors such as variation in morphology and growth patterns among varieties of cereal rye as well as differences in planting densities and equipment will certainly affect image collection, processing, and re-creation of canopy structure.

In addition to increased accuracy, there are several significant improvements that would be required for commercial applications of this technology for growers. First, the method in the study did not have a real-time measurement of crop height. While the SfM generated point clouds by triangulating the location of points relative to the camera and soil surface, it did not assign a height value to the points (Dobbs et al. Reference Dobbs, Ginn, Skovsen, Yadav, Jha, Bagavathiannan, Mirsky, Reberg-Horton and Leon2022). Therefore, for this method to be more accurate and efficient, crop height would need to be measured automatically, whether by using a depth camera or a sensor or by incorporating height measurements into the SfM algorithm. Second, because the SfM point clouds were generated based on common features in overlapping images, any excessive movement in the canopy due to wind or trampling caused the algorithm to discard those feature points, leading to sparse, less informative point clouds. Third, this method is able to generate point clouds using a simple (and relatively inexpensive) RGB camera, but the algorithm requires extensive computing power. For example, generating clouds for 80 sampling points in a single field in this study required 2 to 3 d of processing using a more powerful computer than most home computers. After taking videos over the field, the grower would upload them to a cloud-based server, generating the point clouds and creating a map of predicted biomass using kriging. Although the present study created biomass maps using sampling in a 2 by 2 m grid, a reasonably accurate map could potentially be generated using fewer samples, which would reduce the computing time. Therefore, depending on the relative variability of biomass in the field, the sampling frequency could be optimized and generate a reasonably accurate map of biomass using the shortest possible computing time. The grower could visually estimate the level of evenness of biomass (e.g., on a scale of 1 to 10), which would automatically adjust the sampling rate of videos.

Based on this proof-of-concept study, this ground-based approach using SfM has the potential for estimating biomass in cover crops on a field scale. With improved accuracy, it could help growers not only to monitor cover crops, but also to predict and manage weed occurrence, including late-season weed escapes. For example, it could be used to identify areas where predicted dry biomass is near or above the level of 7,000 to 8,000 kg ha⁻¹, which is recommended for optimal weed suppression in the subsequent cash crop (Liebert et al. Reference Liebert, DiTommaso and Ryan2017; Teasdale and Mohler Reference Teasdale and Mohler2000). It could also identify areas with low biomass (<2,000 kg ha⁻¹), which are associated with high degrees of weed infestation and late-season weed biomass (Teasdale and Mohler Reference Teasdale and Mohler2000). With additional testing and improvements, this method could potentially be developed into an integrated system for growers to map biomass at the same time as cover crop termination using a tractor-mounted camera or smartphone and identify low-performing areas as being at higher risk for weed pressure (Figure 1). If used at the same time as termination, this would eliminate the need for additional passes through the field. Furthermore, if used before termination, it could inform decisions about expected yield and timing of termination. For example, if the cover crop performance was extremely variable, with lots of low-density patches that could become weedy in the future, this could warrant earlier termination. Finally, due to the relatively inexpensive equipment requirement and potential for automation, this method could be developed and incorporated into existing management systems, presumably in a cost-effective manner.