@article{dobbs_ginn_skovsen_yadav_jha_bagavathiannan_mirsky_reberg-horton_leon_2023, title={Using structure-from-motion to estimate cover crop biomass and characterize canopy structure}, volume={302}, ISSN={["1872-6852"]}, DOI={10.1016/j.fcr.2023.109099}, abstractNote={Variability in biomass production poses a challenge for growers when using cover crops for weed control. However, most methods for assessing cover crop biomass are laborious and impractical on a field scale. The goal of the present study was to determine the feasibility of using Structure-from-Motion (SfM) photogrammetry to estimate biomass in cereal rye (Secale cereale L.) and winter wheat (Triticum aestivum L.) cover crops by correlating biomass with 3-D point cloud pixel density and crop height. Point clouds were generated using a SfM algorithm from RGB (red, green, and blue) videos collected by a hand-held GoPro camera over sixteen crop fields in North Carolina, Iowa, and Maryland, USA, throughout two growing seasons (2021–2023). Crop height, leaf area index (LAI), and photosynthetically active radiation (PAR) were also measured. Biomass was positively correlated with crop height for both cereal rye (R2 = 0.621) and wheat (R2 = 0.55). LAI was positively correlated with biomass accumulation and crop height for both species, increasing linearly in rye and exponentially in wheat. Conversely, PAR penetration below the canopy decreased with biomass accumulation and crop height in both species, with a more rapid extinction in wheat than rye. Point cloud pixel density showed a positive linear relationship with biomass in rye but saturated after 2.5 tonnes ha−1 (2500 kg ha−1). In wheat, point cloud pixel density was weakly and negatively correlated with biomass due to a denser canopy causing faster saturation of tissue detection by SfM point clouds. However, considering crop height and point cloud density integrating them both in the model allowed obtaining a positive relationship with biomass through levels of 8 tonnes ha−1 (8000 kg ha−1) in both species. When models were validated with independent data, predicted and measured biomass were positively correlated for both rye (R2 = 0.86) and wheat (R2 = 0.78). Based on the results, using SfM to generate 3-D point clouds can provide a more accurate estimation of biomass than canopy height alone by capturing species-level differences in canopy architecture. The results of this study suggest that SfM can potentially be used as a non-destructive tool for growers to monitor biomass production in cereal cover crops other systems such as energy/forage crops, which can help inform management decisions and conserve resources.}, journal={FIELD CROPS RESEARCH}, author={Dobbs, April M. and Ginn, Daniel and Skovsen, Soren Kelstrup and Yadav, Ramawatar and Jha, Prashant and Bagavathiannan, Muthukumar V and Mirsky, Steven B. and Reberg-Horton, Chris S. and Leon, Ramon G.}, year={2023}, month={Oct} }
 @article{dobbs_sousa-ortega_holland_snyder_leon_2023, title={Variability structure and heritability of germination timing in <i>Capsella bursa-pastoris</i> (L.) Medik. (Shepherd&apos;s purse)}, volume={12}, ISSN={["1365-3180"]}, url={https://doi.org/10.1111/wre.12605}, DOI={10.1111/wre.12605}, abstractNote={AbstractGermination variability enables weedy species to colonise disturbed habitats and is expected to evolve in response to changing selection pressures. The paucity of information about germination variability in weeds prompted a detailed study of this topic with two agricultural and two non‐agricultural populations of Capsella bursa‐pastoris (Shepherd's purse). Variance in germination time was partitioned amongst and within populations, and amongst racemes and silicles within individual plant, and broad‐sense heritability (H2) was estimated. Agricultural populations exhibited a shorter and more uniform germination timing than non‐agricultural populations. However, differences amongst populations explained 7%–12% of the total variance, while differences amongst individuals and racemes accounted for approximately 40–54% and 10% of the total variance for germination time. For germination time, H2 = 0.4 when averaged across all time points, peaking at H2 = 0.7 at a time coinciding with the exponential phase of the germination curve. Maintaining predominantly intrapopulation variability in germination timing appears to be important for long‐term fitness in this species.}, journal={WEED RESEARCH}, author={Dobbs, April M. and Sousa-Ortega, Carlos and Holland, James B. and Snyder, Lori Unruh and Leon, Ramon G.}, year={2023}, month={Dec} }
 @article{dobbs_reberg-horton_snyder_leon_2022, title={Assessing weediness potential of Brassica carinata (A.) Braun in the southeastern United States}, volume={188}, ISSN={["1872-633X"]}, DOI={10.1016/j.indcrop.2022.115611}, abstractNote={Carinata (Brassica carinata (A.) Braun) is a promising winter oilseed crop in the southeastern US, and ensuring agricultural and ecological safety is critical for growers. The present study evaluated the weediness and invasiveness potential for carinata in the southeastern US. A field study was conducted in Goldsboro and Clayton, North Carolina comparing emergence and survival of volunteer carinata with and without predator exclusion. Cumulative seedling emergence at both locations was highest for buried seeds with predator exclusion (42% and 15%) and lowest in unburied seeds without predator exclusion (16% and 1%). Survival 90 days after planting (DAP) at both locations was highest for buried seeds with predator exclusion (10% and 5%) and lowest in unburied seeds without predator exclusion (3% and <1%). Frost damage contributed to predator damage increasing mortality of established plants to 100% 120 DAP. In addition to the field study, the Australian Weed Risk Assessment (WRA) and Plant Risk Evaluation were conducted for carinata. The cumulative score for the Australian WRA was –1 (low risk), which was below the minimum score of 6 for rejection of introduction. The agricultural and environmental scores were –5 and –2, corresponding to a low risk in agricultural and non-agricultural settings. The cumulative Plant Risk Evaluation score was 6 (low risk), which was below the minimum score of 13 for rejection. Based on the field study and risk assessments, it was concluded that there is low risk of weediness and invasiveness for volunteer carinata in the southeastern US.}, journal={INDUSTRIAL CROPS AND PRODUCTS}, author={Dobbs, April M. and Reberg-Horton, S. Chris and Snyder, Lori Unruh and Leon, Ramon G.}, year={2022}, month={Nov} }
 @article{dobbs_ginn_skovsen_bagavathiannan_mirsky_reberg-horton_leon_2022, title={New directions in weed management and research using 3D imaging}, volume={10}, ISSN={["1550-2759"]}, url={https://doi.org/10.1017/wsc.2022.56}, DOI={10.1017/wsc.2022.56}, abstractNote={AbstractRecent innovations in 3D imaging technology have created unprecedented potential for better understanding weed responses to management tactics. Although traditional 2D imaging methods for mapping weed populations can be limited in the field by factors such as shadows and tissue overlap, 3D imaging mitigates these challenges by using depth data to create accurate plant models. Three-dimensional imaging can be used to generate spatiotemporal maps of weed populations in the field and target weeds for site-specific weed management, including automated precision weed control. This technology will also help growers monitor cover crop performance for weed suppression and detect late-season weed escapes for timely control, thereby reducing seedbank persistence and slowing the evolution of herbicide resistance. In addition to its many applications in weed management, 3D imaging offers weed researchers new tools for understanding spatial and temporal heterogeneity in weed responses to integrated weed management tactics, including weed–crop competition and weed community dynamics. This technology will provide simple and low-cost tools for growers and researchers alike to better understand weed responses in diverse agronomic contexts, which will aid in reducing herbicide use, mitigating herbicide-resistance evolution, and improving environmental health.}, journal={WEED SCIENCE}, author={Dobbs, April M. and Ginn, Daniel and Skovsen, Soren Kelstrup and Bagavathiannan, Muthukumar V and Mirsky, Steven B. and Reberg-Horton, Chris S. and Leon, Ramon G.}, year={2022}, month={Oct} }