Analysis of cotton height spatial variability based on UAV-LiDAR

Kuan Liu, Xiaoya Dong, Baijing Qiu

Abstract


Abstract: The spatial variance of geometric information of farmland crops is the basis of field management.  Therefore, it has significance for variable mechanical operations to accurately obtain the spatial difference of crop height information.  In the present study, UAV-LiDAR was used to collect data at the cotton planting base in Korla to estimate the spatial differences in cotton plant height.  The crop height was estimated using the average height of a certain number of highest points per m2 point cloud.  First, the plant heights of different spatial locations in the field were collected manually and compared with the system measurement.  The results showed that the maximum relative error of sampling was 12.73%, the error value was3.48 cm, and the height map was visualized.  In order to explain the height change of plant height in the direction of crop rows and vertical crop rows, this paper used the coefficient of variation as a measure.  The results showed that the plant height variation coefficient in the crop row direction ranged from 0.54-1.04 and the average variation coefficient was 0.73; perpendicular to the crop row direction, the crop height variation coefficient range was 0.06-1.27 and the average variation coefficient was 0.58.  The spatial difference information was characterized by the coefficient of variation of the geometrical features of the crop height.  This work can provide information for cotton field variable operation machinery and provide reference for the extraction of field crop geometric information.

Keywords: LiDAR, plant parameter, spatial variability, plant height

DOI: 10.33440/j.ijpaa.20200303.79

 

Citation: Liu K, Dong X Y, Qiu B J.  Analysis of cotton height spatial variability based on UAV-LiDAR.  Int J Precis Agric Aviat, 2020; 3(3): 72–76.


Full Text:

PDF

References


Thapa S, Zhu F Y, Walia H, et al. A novel LiDAR-based instrument for high-throughput, 3D measurement of morphological traits in maize and sorghum. Sensors, 2018, 18(4): 1187–1201. doi: 10.3390/s18041187.

Pabuayon I L B, Sun Y, Guo W, et al. High-throughput phenotyping in cotton: a review. Journal of Cotton Research, 2019, 2(1): 18–27. doi: 10.1186/s42397-019-0035-0.

Wilkerson G, Jones J, Boote K, et al. Modeling soybean growth for crop management. Transactions of the ASAE, 1983, 26(1): 63–73. doi: 10.13031/2013.33877.

Richardson C W Weather Simulation for Crop Management Models. Transactions of the ASAE, 1985, 28(5): 1602–1606. doi: 10.13031/2013.32484.

Qiu Q, Sun N, Bai H, et al. Field-based high-throughput phenotyping for Maize plant using 3D LiDAR point cloud generated with a “Phenomobileâ€. Frontiers in plant science, 2019, 10(1): 554–569. doi: 10.3389/fpls.2019.00554.

Jimenez-Berni J A, Deery D M, Rozas-Larraondo P, et al. High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR. Frontiers in plant science, 2018, 9(1): 237–255. doi: 10.3389/fpls.2018.00237.

Blancon J, Dutartre D, Tixier M-H, et al. A high-throughput model-assisted method for phenotyping maize green leaf area index dynamics using unmanned aerial vehicle imagery. Frontiers in plant science, 2019, 10(1): 685–701. doi: 10.3389/fpls.2019.00685.

Sun J, Cong S L, Mao H P, et al. CARS-ABC-SVR model for predicting leaf moisture of leaf-used lettuce based on hyperspectral. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(5): 178–184. doi: 10.11975/j.issn.1002-6819.2017.05.026.

Haboudane D, Tremblay N, Miller J R, et al. Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(2): 423–437. doi: 10.1109/TGRS.2007.904836.

Cammarano D, Fitzgerald G J, Casa R, et al. Assessing the robustness of vegetation indices to estimate wheat N in Mediterranean environments. Remote Sensing, 2014, 6(4): 2827–2844. doi: 10.3390/rs6042827.

Mahan J R, Conaty W, Neilsen J, et al. Field performance in agricultural settings of a wireless temperature monitoring system based on a low-cost infrared sensor. Computers and Electronics in Agriculture, 2010, 71(2): 176–181. doi: 10.1016/j.compag.2010.0.

Whitaker R T A level-set approach to 3D reconstruction from range data. International journal of computer vision, 1998, 29(3): 203–231. doi: 10.1023/A:1008036829907.

Bietresato M, Carabin G, Vidoni R, et al. Evaluation of a LiDAR-based 3D-stereoscopic vision system for crop-monitoring applications. Computers and Electronics in Agriculture, 2016, 124(1): 1–13. doi: 10.1016/j.compag.2016.03.017.

D. Tumbo S, Salyani M, D. Whitney J, et al. Investigation of laser and ultrasonic ranging sensors for measurements of citrus canopy volume. Applied Engineering in Agriculture, 2002, 18(3): 367. doi: 10.13031/2013.8587.

Wei J T, Salyani M. Development of a laser scanner for measuring tree canopy characteristics: Phase 1. Prototype development. Transactions of the ASAE, 2004, 47(6): 2101–2107. doi: 10.13031/2013.17795.

Wei J T, Salyani M. Development of a laser scanner for measuring tree canopy characteristics: Phase 2. Foliage density measurement. Transactions of the ASAE, 2005, 48(4): 1595–1601. doi: 10.13031/2013.19174.

[17] Rosell Polo J R, Sanz Cortiella R, Llorens Calveras J, et al. A tractor-mounted scanning LIDAR for the non-destructive measurement of vegetative volume and surface area of tree-row plantations: A comparison with conventional destructive measurements. Biosystems Engineering, 2009, 102(2): 128–134. doi: 10.1016/j.biosystemseng.2008.10.009.

del-Moral-Martínez I, Arnó J, Sanz R, et al. Georeferenced scanning system to estimate the leaf wall area in tree crops. Sensors, 2015, 15(4): 8382–8405. doi: 10.3390/s150408382.

Shi Y, Thomasson J A, Murray S C, et al. Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PloS one, 2016, 11(7): e0159781. doi: 10.1371/journal.pone.0159781.

Colaço A F, Trevisan R G, Molin J P, et al. A method to obtain orange crop geometry information using a mobile terrestrial laser scanner and 3D modeling. Remote Sensing, 2017, 9(8): 763. doi: 10.3390/rs9080763.

Lei L, Qiu C, Li Z, et al. Effect of leaf occlusion on leaf area index inversion of maize using UAV–LiDAR data. Remote Sensing, 2019, 11(9): 1067–1082. doi: 10.3390/rs11091067.

Jin S, Su Y, Gao S, et al. Deep learning: individual maize segmentation from terrestrial lidar data using faster R-CNN and regional growth algorithms. Frontiers in plant science, 2018, 9(1): 866–876. doi: 10.3389/fpls.2018.00866.

Sun S,Li C. In-field high throughput phenotyping and phenotype data analysis for cotton plant growth using LiDAR. 2017 ASABE Annual International Meeting, 2017: 1.

Sun S, Li C,Paterson A H In-field high-throughput phenotyping of cotton plant height using LiDAR. Remote Sensing, 2017, 9(4): 377–398. doi: 10.3390/rs9040377.

Yuan H, Bennett R S, Wang N, et al. Development of a peanut canopy measurement system using a ground-based lidar sensor. Frontiers in plant science, 2019, 10(1): 203–216. doi: 10.3389/fpls.2019.00203.

Guo T, Fang Y, Cheng T, et al. Detection of wheat height using optimized multi-scan mode of LiDAR during the entire growth stages. Computers and Electronics in Agriculture, 2019, 165(104959). doi: 10.1016/j.compag.2019.104959.

Cheng M, Cai Z J, Wang N, et al. System design for peanut canopy height information acquisition based on LiDAR. Transactions of the CSAE, 2019, 35(1): 180–187. doi: 10.11975/j.issn.1002-6819.2019.01.022. (in Chinese)

Ashapure A, Jung J, Yeom J, et al. A novel framework to detect conventional tillage and no-tillage cropping system effect on cotton growth and development using multi-temporal UAS data. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 152 49–64. doi: 10.1016/j.isprsjprs.2019.04.003.

Colaço A F, Molin J P,Rosell-Polo J R Spatial variability in commercial orange groves. Part 1: canopy volume and height. Precision Agriculture, 2019, 20(4): 788–804. doi: 10.1007/s11119-018-9612-3.

Elmegreen B G The Initial Stellar Mass Function from Random Sampling in a Turbulent Fractal Clou. The Astrophysical Journal, 1997, 486(2): 944–954. doi: 10.1086/304562.

Wang K, Guo H, Liu W, et al. Extraction method of pig body size measurement points based on rotation normalization of point cloud. Transactions of the CSAE, 2017, 33(1): 253–259. doi: 10.11975/j.issn.1002-6819.2017.z1.038. (in Chinese)

Guan X P, Liu K, Qiu B J, et al. Extraction of geometric parameters of soybean canopy by airborne 3D laser scanning. Transactions of the CSAE, 2019, 35(23). doi: 10.11975/j.issn.1002-6819.2019.23.012. (in Chinese)


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.