scholarly journals Analysis of Plant Height Changes of Lodged Maize Using UAV-LiDAR Data

Agriculture ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 146 ◽  
Author(s):  
Longfei Zhou ◽  
Xiaohe Gu ◽  
Shu Cheng ◽  
Guijun Yang ◽  
Meiyan Shu ◽  
...  
Keyword(s):  
Plant Height ◽  
Maize Yield ◽  
Canopy Height ◽  
Estimation Accuracy ◽  
Yield Reduction ◽  
Lidar Data ◽  
Height Estimation ◽  
Aerial Vehicle ◽  
Yield Quality ◽  
Maize Plants

Lodging stress seriously affects the yield, quality, and mechanical harvesting of maize, and is a major natural disaster causing maize yield reduction. The aim of this study was to obtain light detection and ranging (LiDAR) data of lodged maize using an unmanned aerial vehicle (UAV) equipped with a RIEGL VUX-1UAV sensor to analyze changes in the vertical structure of maize plants with different degrees of lodging, and thus to use plant height to quantitatively study maize lodging. Based on the UAV-LiDAR data, the height of the maize canopy was retrieved using a canopy height model to determine the height of the lodged maize canopy at different times. The profiles were analyzed to assess changes in maize plant height with different degrees of lodging. The differences in plant height growth of maize with different degrees of lodging were evaluated to determine the plant height recovery ability of maize with different degrees of lodging. Furthermore, the correlation between plant heights measured on the ground and LiDAR-estimated plant heights was used to verify the accuracy of plant height estimation. The results show that UAV-LiDAR data can be used to achieve maize canopy height estimation, with plant height estimation accuracy parameters of R2 = 0.964, RMSE = 0.127, and nRMSE = 7.449%. Thus, it can reflect changes of plant height of lodging maize and the recovery ability of plant height of different lodging types. Plant height can be used to quantitatively evaluate the lodging degree of maize. Studies have shown that the use of UAV-LiDAR data can effectively estimate plant heights and confirm the feasibility of LiDAR data in crop lodging monitoring.

scholarly journals Assessing the Self-Recovery Ability of Maize after Lodging Using UAV-LiDAR Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2270
Author(s):  
Xueqian Hu ◽  
Lin Sun ◽  
Xiaohe Gu ◽  
Qian Sun ◽  
Zhonghui Wei ◽  
...  
Keyword(s):  
Reduction Rate ◽  
Canopy Height ◽  
The Self ◽  
Yield Reduction ◽  
Lidar Data ◽  
Growth Stages ◽  
Loss Assessment ◽  
Filling Stage ◽  
Maize Plants ◽  
Different Growth Stages

Lodging is one of the main problems in maize production. Assessing the self-recovery ability of maize plants after lodging at different growth stages is of great significance for yield loss assessment and agricultural insurance claims. The objective of this study was to quantitatively analyse the effects of different growth stages and lodging severity on the self-recovery ability of maize plants using UAV-LiDAR data. The multi-temporal point cloud data obtained by the RIEGL VUX-1 laser scanner were used to construct the canopy height model of the lodging maize. Then the estimated canopy heights of the maize at different growth stages and lodging severity were obtained. The measured values were used to verify the accuracy of the canopy height estimation and to invert the corresponding lodging angle. After verifying the accuracy of the canopy height, the accuracy parameter of the tasselling stage was R2 = 0.9824, root mean square error (RMSE) = 0.0613 m, and nRMSE = 3.745%. That of the filling stage was R2 = 0.9470, RMSE = 0.1294 m, and nRMSE = 9.889%, which showed that the UAV-LiDAR could accurately estimate the height of the maize canopy. By comparing the yield, canopy height, and lodging angle of maize, it was found that the self-recovery ability of maize at the tasselling stage was stronger than that at the filling stage, but the yield reduction rate was 14.16~26.37% higher than that at the filling stage. The more serious the damage of the lodging is to the roots and support structure of the maize plant, the weaker is the self-recovery ability. Therefore, the self-recovery ability of the stem tilt was the strongest, while that of root lodging and root stem folding was the weakest. The results showed that the UAV-LiDAR could effectively assess the self-recovery ability of maize after lodging.

scholarly journals Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season

2021 ◽  
Author(s):  
Yan Gong ◽  
Kali Yang ◽  
Zhiheng Lin ◽  
Shenghui Fang ◽  
Xianting Wu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Large Scale ◽  
Growing Season ◽  
Canopy Height ◽  
Dynamic Monitoring ◽  
Estimation Accuracy ◽  
Rice Cultivars ◽  
Simple Method ◽  
Area Index ◽  
Aerial Vehicle

Abstract Background: Rice is one of the most important grain crops worldwide. The accurate and dynamic monitoring of leaf are index (LAI) provides important information to evaluate rice growth and production. Methods: This study explores a simple method to remotely estimate LAI with Unmanned Aerial Vehicle (UAV) imaging for a variety of rice cultivars throughout the entire growing season. 48 different rice cultivars were planted in the study site and field campaigns were conducted once a week. For each campaign, several widely used vegetation indices (VI) were calculated from canopy reflectance obtained by 12-band UAV images, canopy height was derived from UAV RGB images and LAI was destructively measured by plant sampling. Results: The results showed the correlation of VI and LAI in rice throughout the entire growing season was weak, and for all tested indices there existed significant hysteresis of VI vs. LAI relationship between rice pre-heading and post-heading stages. The model based on the product of VI and canopy height could reduce such hysteresis and estimate rice LAI of the whole season with estimation errors under 24%, not requiring algorithm re-parameterization for different phenology stages. Conclusions: The progressing phenology can affect VI vs. LAI relationship in crops, especially for rice having quite different canopy spectra and structure after its panicle exsertion. Thus the models solely using VI to estimate rice LAI are phenology-specific and have high uncertainties for post-heading stages. The model developed in this study combines both remotely sensed canopy height and VI information, considerably improving rice LAI estimation accuracy at both pre- and post-heading stages. This method can be easily and efficiently implemented in UAV platforms for various rice cultivars during the entire growing season with no rice phenology and cultivar pre-knowledge, which has great potential for assisting rice breeding and field management studies at a large scale.

scholarly journals Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images

2019 ◽  
Vol 11 (17) ◽  
pp. 2021 ◽  
Author(s):  
Wei Su ◽  
Mingzheng Zhang ◽  
Dahong Bian ◽  
Zhe Liu ◽  
Jianxi Huang ◽  
...  
Keyword(s):  
Plant Height ◽  
Unmanned Aerial Vehicle ◽  
Estimation Error ◽  
Surface Model ◽  
Lodging Resistance ◽  
Height Estimation ◽  
Area Estimation ◽  
Corn Breeding ◽  
Aerial Vehicle ◽  
Uav Images

Phenotyping provides important support for corn breeding. Unfortunately, the rapid detection of phenotypes has been the major limiting factor in estimating and predicting the outcomes of breeding programs. This study was focused on the potential of phenotyping to support corn breeding using unmanned aerial vehicle (UAV) images, aiming at mining and deepening UAV techniques for comparing phenotypes and screening new corn varieties. Two geometric traits (plant height, canopy leaf area index (LAI)) and one lodging resistance trait (lodging area) were estimated in this study. It was found that stereoscopic and photogrammetric methods were promising ways to calculate a digital surface model (DSM) for estimating corn plant height from UAV images, with R2 = 0.7833 (p < 0.001) and a root mean square error (RMSE) = 0.1677. In addition to a height estimation, the height variation was analyzed for depicting and validating the corn canopy uniformity stability for different varieties. For the lodging area estimation, the normalized DSM (nDSM) method was more promising than the gray-level co-occurrence matrix (GLCM) textural features method. The estimation error using the nDSM ranged from 0.8% to 5.3%, and the estimation error using the GLCM ranged from 10.0% to 16.2%. Associations between the height estimation and lodging area estimation were done to find the corn varieties with optimal plant heights and lodging resistance. For the LAI estimation, the physical radiative transfer PROSAIL model offered both an accurate and robust estimation performance both at the middle (R2 = 0.7490, RMSE = 0.3443) and later growing stages (R2 = 0.7450, RMSE = 0.3154). What was more exciting was that the estimated sequential time series LAIs revealed a corn variety with poor resistance to lodging in a study area of Baogaofeng Farm. Overall, UAVs appear to provide a promising method to support phenotyping for crop breeding, and the phenotyping of corn breeding in this study validated this application.

scholarly journals Impact of Base-to-Height Ratio on Canopy Height Estimation Accuracy of Hemiboreal Forest Tree Species by Using Satellite and Airborne Stereo Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 2941
Author(s):  
Grigorijs Goldbergs
Keyword(s):  
Tree Species ◽  
Canopy Height ◽  
Deciduous Tree ◽  
Estimation Accuracy ◽  
Surface Model ◽  
Height Estimation ◽  
Detection Rates ◽  
Median Error ◽  
Stereo Imagery ◽  
Closed Canopy

The present study assessed the large-format airborne (UltraCam) and satellite (GeoEye1 and Pleiades1B) image-based digital surface model (DSM) performance for canopy height estimation in predominantly mature, closed-canopy Latvian hemiboreal forestland. The research performed the direct comparison of calculated image-based DSM models with canopy peaks heights extracted from reference LiDAR data. The study confirmed the tendency for canopy height underestimation for all satellite-based models. The obtained accuracy of the canopy height estimation GeoEye1-based models varied as follows: for a pine (−1.49 median error, 1.52 m normalised median absolute deviation (NMAD)), spruce (−0.94 median, 1.97 m NMAD), birch (−0.26 median, 1.96 m NMAD), and black alder (−0.31 median, 1.52 m NMAD). The canopy detection rates (completeness) using GeoEye1 stereo imagery varied from 98% (pine) to >99% for spruce and deciduous tree species. This research has shown that determining the optimum base-to-height (B/H) ratio is critical for canopy height estimation efficiency and completeness using image-based DSMs. This study found that stereo imagery with a B/H ratio range of 0.2–0.3 (or convergence angle range 10–15°) is optimal for image-based DSMs in closed-canopy hemiboreal forest areas.

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