Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery

Falco, Nicola; Wainwright, Haruko M.; Dafflon, Baptiste; Ulrich, Craig; Soom, Florian; Peterson, John; Brown, James; Schaettle, K. B.; Williamson, Malcolm; Cothren, Jackson; Ham, Richard G.; McEntire, Jay A.; Hubbard, Susan S.

doi:10.1038/s41598-021-86480-z

Cited by 26 publications

(10 citation statements)

References 56 publications

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“…RGB camera [ 22 , 23 ], multispectral or hyperspectral camera [ 24 ], lidar [ 25 , 26 ] and ultrasonic sensors [ 27 , 28 ] have been applied on the UAV platform. These high-throughput phenotyping platforms have been implemented in many crops such as maize [ 29 , 30 ], wheat [ 31 , 32 ], rice [ 33 , 34 ], sorghum [ 35 , 36 ], soybean [ 37 ] in terms of plant height study. However, there was no report for faba bean.…”

Section: Introductionmentioning

confidence: 99%

Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.)

Chen

Qian

et al. 2022

Plant Methods

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Background Faba bean is an important legume crop in the world. Plant height and yield are important traits for crop improvement. The traditional plant height and yield measurement are labor intensive and time consuming. Therefore, it is essential to estimate these two parameters rapidly and efficiently. The purpose of this study was to provide an alternative way to accurately identify and evaluate faba bean germplasm and breeding materials. Results The results showed that 80% of the maximum plant height extracted from two-dimensional red–green–blue (2D-RGB) images had the best fitting degree with the ground measured values, with the coefficient of determination (R2), root-mean-square error (RMSE), and normalized root-mean-square error (NRMSE) were 0.9915, 1.4411 cm and 5.02%, respectively. In terms of yield estimation, support vector machines (SVM) showed the best performance (R2 = 0.7238, RMSE = 823.54 kg ha−1, NRMSE = 18.38%), followed by random forests (RF) and decision trees (DT). Conclusion The results of this study indicated that it is feasible to monitor the plant height of faba bean during the whole growth period based on UAV imagery. Furthermore, the machine learning algorithms can estimate the yield of faba bean reasonably with the multiple time points data of plant height.

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Section: Introductionmentioning

confidence: 99%

Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.)

Chen

Qian

et al. 2022

Plant Methods

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show abstract

“…In recent years, precision agriculture has been promoted by the introduction of drones 20 , 21 . The advantage of drone measurements is that enormous amounts of field data can be obtained in a short time 22 .…”

Section: Related Workmentioning

confidence: 99%

High-precision plant height measurement by drone with RTK-GNSS and single camera for real-time processing

Matsuura

Zhang

Nakao

et al. 2023

Sci Rep

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Conventional crop height measurements performed using aerial drone images require 3D reconstruction results of several aerial images obtained through structure from motion. Therefore, they require extensive computation time and their measurement accuracy is not high; if the 3D reconstruction result fails, several aerial photos must be captured again. To overcome these challenges, this study proposes a high-precision measurement method that uses a drone equipped with a monocular camera and real-time kinematic global navigation satellite system (RTK-GNSS) for real-time processing. This method performs high-precision stereo matching based on long-baseline lengths (approximately 1 m) during the flight by linking the RTK-GNSS and aerial image capture points. As the baseline length of a typical stereo camera is fixed, once the camera is calibrated on the ground, it does not need to be calibrated again during the flight. However, the proposed system requires quick calibration in flight because the baseline length is not fixed. A new calibration method that is based on zero-mean normalized cross-correlation and two stages least square method, is proposed to further improve the accuracy and stereo matching speed. The proposed method was compared with two conventional methods in natural world environments. It was observed that error rates reduced by 62.2% and 69.4%, for flight altitudes between 10 and 20 m respectively. Moreover, a depth resolution of 1.6 mm and reduction of 44.4% and 63.0% in the error rates were achieved at an altitude of 4.1 m, and the execution time was 88 ms for images with a size of 5472 × 3468 pixels, which is sufficiently fast for real-time measurement.

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“…In recent years, precision agriculture has been promoted by the introduction of drones [14,15] . The advantage of drone measurements is that enormous amounts of eld data can be obtained in a short time [16] .…”

Section: Limitations Of Drone Measurements In the Eldmentioning

confidence: 99%

High-precision plant height measurement by drone with RTK-GNSS and single camera for real-time processing

Matsuura

Zhang

Nakao

et al. 2022

Preprint

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Conventional crop height measurements performed using aerial drone images require the 3D reconstruction results of several aerial images obtained through structure from motion. Therefore, they require extensive computation times and their measurement accuracy is not high; if the 3D reconstruction result fails, several aerial photos must be captured again. To overcome these challenges, this study proposes a high-precision measurement method that uses a drone equipped with a monocular camera and real-time kinematic global navigation satellite system (RTK-GNSS) for real-time processing. This method performs high-precision stereo matching based on long-baseline lengths during flight by linking the RTK-GNSS and aerial image capture points. A new calibration method is proposed to further improve the accuracy and stereo matching speed. Throught the comparison between the proposed method and conventional methods in natural world environments, wherein it reduced the error rates by 62.2% and 69.4%, at flight altitudes of 10 and 20 m. Moreover, a depth resolution of 1.6 mm and reduction of 44.4% and 63.0% in the errors were achieved at an altitude of 4.1 m, and the execution time was 88 ms for images with a size of 5472 × 3468 pixels, which is sufficiently fast for real-time measurement.

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Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery

Cited by 26 publications

References 56 publications

Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.)

Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.)

High-precision plant height measurement by drone with RTK-GNSS and single camera for real-time processing

High-precision plant height measurement by drone with RTK-GNSS and single camera for real-time processing

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