Prediction of Aboveground Biomass of Three Cassava (Manihot esculenta) Genotypes Using a Terrestrial Laser Scanner

Adams, Tyler; Bruton, Richard Kyle; Ruiz, Henry; Barrios-Perez, Ilse; Selvaraj, Michael Gomez; Hays, Dirk B.

doi:10.3390/rs13071272

Cited by 8 publications

(9 citation statements)

References 31 publications

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“…The seed was then discharged from the seed-metering device into the seed trench drawn by the double-disc opener of the seed tube. These components were operated together to precisely seed the fields [15] . The main working parameters of the seeder are listed in Table 1.…”

Section: Experimental Materials and Design 221 Experimental Materialsmentioning

confidence: 99%

Effect of soil surface roughness on emergence rate and yield of mechanized direct-seeded rapeseed based on 3D laser scanning

Chen,

Gao,

et al. 2023

International Journal of Agricultural and Biological Engineering

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The quality of seedbed after sowing such as soil surface roughness is one of the key factors affecting the seedling emergence of rapeseed, which ultimately affected crop yield. However, the effect of soil surface roughness on seedling emergence and yield of rapeseed is still unclear. In this study, field experiments at the experimental site of Jianli and Shayang were carried out. Three treatments were designed: relative slow (M1), medium (M2), and fast (M3) forward speed of the unit. Soil surface roughness measured by a 3D laser scanner, seed quantity of the seeder, emergence rate and yield of rapeseed were determined to investigate the soil surface roughness effect on emergence rate and yield of rapeseed. The results showed that as the forward speed of the unit increased, the compartment surface became rougher. Compared with the M1 and M2 treatments, soil surface roughness under the M3 treatment increased by 36.5% and 9.8%, respectively. The actual seed quantity of the seeder under different treatments ranged from 3 806.56 to 4 158.18 g/hm 2 . The average error rate of the actual and theoretical seed quantity was less than 5%, which met the operational quality requirements for seeding rapeseed crops. As the forward speed of the unit increased, the actual seed quantity of the seeder gradually increased while the emergence rate and yield of rapeseed decreased. The seed quantity under the M3 treatment increased by 6.9% and 4.7%, while the emergence rate of rapeseed decreased by 3.3% and 2.0%, and the yield decreased by 23.2% and 13.1%, compared with the M1 and M2 treatments, respectively. Correlation analysis indicated that emergence rate and yield of rapeseed were negatively influenced by soil surface roughness. Considering rapeseed emergence rate, seed yield, and economic benefits, the M1 treatment was recommended. But considering the factor that the M1 treatment may reduce the unit operation efficiency, and thus resulting in lower cost of production, M2 could be recommended in actual farming. The results of this study laid a theoretical foundation for analyzing the relationship between the seedbed surface quality and seedling emergence and yield.

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Section: Experimental Materials and Design 221 Experimental Materialsmentioning

confidence: 99%

Effect of soil surface roughness on emergence rate and yield of mechanized direct-seeded rapeseed based on 3D laser scanning

Chen,

Gao,

et al. 2023

International Journal of Agricultural and Biological Engineering

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“…The Dijkstra's shortest path algorithm was used to extract tree initial skeletons. The main branching points are predetermined and concentrated to improve the quality of the skeleton [51,52]. To reconstruct the final lightweight skeleton, the vertices and edges were assigned weight values and small noisy components were removed based on this value.…”

Section: Enable Adqsm To Automatically Rebuild Tree Structurementioning

confidence: 99%

“…To test the accuracy of QSM method in estimating trunk volume from TP point clouds, we compared reconstructed trunk volume values of AdQSM and TreeQSM with reference trunk volume of the allometric equation respectively, Figure 14 Figure 16 [40,41]. At present, AdQSM has attracted more and more people's attention [50][51][52][53]. AdQSM needs to be tested using photogrammetric point clouds.…”

Section: Trunk Volumementioning

confidence: 99%

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Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Dong

Fan

Zhou

et al. 2021

Forests

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The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

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“…Remote sensing technologies, such as satellite imagery, [21], and aerial drones, [17], [7], provide a bird's-eye view of agricultural landscapes, enabling farmers to assess crop health, detect anomalies, [13], and make informed decisions. In addition, in recent agricultural research, advanced technologies such as convolutional neural networks, [12], and LiDAR scanners have been also employed in plant analysis, [18], [1]. These tools, combined with data analytics, facilitate real-time monitoring and timely interventions.…”

Section: Introductionmentioning

confidence: 99%

Smart agriculture: Predictive height analysis for universal crop health classification

Tsakiridis,

Papaioannou,

Tsimpiris

et al. 2023

ces

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Prediction of Aboveground Biomass of Three Cassava (Manihot esculenta) Genotypes Using a Terrestrial Laser Scanner

Cited by 8 publications

References 31 publications

Effect of soil surface roughness on emergence rate and yield of mechanized direct-seeded rapeseed based on 3D laser scanning

Effect of soil surface roughness on emergence rate and yield of mechanized direct-seeded rapeseed based on 3D laser scanning

Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Smart agriculture: Predictive height analysis for universal crop health classification

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