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

Zhou, Longfei; Gu, Xiaohe; Cheng, Shu Fen; Yang, Guijun; Shu, Meiyan; Sun, Qian

doi:10.3390/agriculture10050146

Cited by 84 publications

(70 citation statements)

References 56 publications

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“…The identified outliers were removed from both PHground and PHaerial to perform the analysis. Finally, the R M S E d e v was computed to measure the deviation between the estimated values (PHaerial) and the measured values (PHground) across GS in each trial, according to Zhou et al (2020) .…”

Section: Methodsmentioning

confidence: 99%

High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation

et al. 2021

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Plant height (PH) is an essential trait in the screening of most crops. While in crops such as wheat, medium stature helps reduce lodging, tall plants are preferred to increase total above-ground biomass. PH is an easy trait to measure manually, although it can be labor-intense depending on the number of plots. There is an increasing demand for alternative approaches to estimate PH in a higher throughput mode. Crop surface models (CSMs) derived from dense point clouds generated via aerial imagery could be used to estimate PH. This study evaluates PH estimation at different phenological stages using plot-level information from aerial imaging-derived 3D CSM in wheat inbred lines during two consecutive years. Multi-temporal and high spatial resolution images were collected by fixed-wing (PlatFW) and multi-rotor (PlatMR) unmanned aerial vehicle (UAV) platforms over two wheat populations (50 and 150 lines). The PH was measured and compared at four growth stages (GS) using ground-truth measurements (PHground) and UAV-based estimates (PHaerial). The CSMs generated from the aerial imagery were validated using ground control points (GCPs) as fixed reference targets at different heights. The results show that PH estimations using PlatFW were consistent with those obtained from PlatMR, showing some slight differences due to image processing settings. The GCPs heights derived from CSM showed a high correlation and low error compared to their actual heights (R2 ≥ 0.90, RMSE ≤ 4 cm). The coefficient of determination (R2) between PHground and PHaerial at different GS ranged from 0.35 to 0.88, and the root mean square error (RMSE) from 0.39 to 4.02 cm for both platforms. In general, similar and higher heritability was obtained using PHaerial across different GS and years and ranged according to the variability, and environmental error of the PHground observed (0.06–0.97). Finally, we also observed high Spearman rank correlations (0.47–0.91) and R2 (0.63–0.95) of PHaerial adjusted and predicted values against PHground values. This study provides an example of the use of UAV-based high-resolution RGB imagery to obtain time-series estimates of PH, scalable to tens-of-thousands of plots, and thus suitable to be applied in plant wheat breeding trials.

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Section: Methodsmentioning

confidence: 99%

High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation

et al. 2021

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“…The use of LiDAR equipped drones has also been reported in the literature for a wide range of vegetation monitoring applications; ranging from forestry [55,104,105] to agricultural [80,88,106] applications. They come with a higher precision global positioning system (GPS) and inertial measurement unit (IMU) and offer promising results.…”

Section: Suitable Sensors For Vegetation Scoutingmentioning

confidence: 99%

“…Christiansen et al [88] have used a LiDAR sensor mounted on a drone to monitor winter wheat. Likewise, the relationship between plant height obtained from drone-based LiDAR and the lodging degree of maize has been studied [106].…”

Section: Suitable Sensors For Vegetation Scoutingmentioning

confidence: 99%

A Review on Drone-Based Data Solutions for Cereal Crops

Panday

Pratihast

Aryal

et al. 2020

Drones

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Food security is a longstanding global issue over the last few centuries. Eradicating hunger and all forms of malnutrition by 2030 is still a key challenge. The COVID-19 pandemic has placed additional stress on food production, demand, and supply chain systems; majorly impacting cereal crop producer and importer countries. Short food supply chain based on the production from local farms is less susceptible to travel and export bans and works as a smooth system in the face of these stresses. Local drone-based data solutions can provide an opportunity to address these challenges. This review aims to present a deeper understanding of how the drone-based data solutions can help to combat food insecurity caused due to the pandemic, zoonotic diseases, and other food shocks by enhancing cereal crop productivity of small-scale farming systems in low-income countries. More specifically, the review covers sensing capabilities, promising algorithms, and methods, and added-value of novel machine learning algorithms for local-scale monitoring, biomass and yield estimation, and mapping of them. Finally, we present the opportunities for linking information from citizen science, internet of things (IoT) based on low-cost sensors and drone-based information to satellite data for upscaling crop yield estimation to a larger geographical extent within the Earth Observation umbrella.

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“…The digital surface model (DSM), eight texture measures (mean, variance, ASM, entropy, contrast, correlation, dissimilarity, and homogeneity), and the SFP (single feature probability) value are valuable for rice lodging classification based on spatial and spectral hybrid image classification technology [ 43 ]. In addition to common digital and spectral images, UAV-LiDAR data from a RIEGL VUX-1UAV sensor was used to generate point cloud data for measuring the height of lodged maize [ 50 ]. The canopy height model (CHM) is calculated to obtain the canopy height of maize by subtracting the digital elevation model (DEM) from the digital terrain model (DSM), which is extracted from point cloud data [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

“…In addition to common digital and spectral images, UAV-LiDAR data from a RIEGL VUX-1UAV sensor was used to generate point cloud data for measuring the height of lodged maize [ 50 ]. The canopy height model (CHM) is calculated to obtain the canopy height of maize by subtracting the digital elevation model (DEM) from the digital terrain model (DSM), which is extracted from point cloud data [ 50 ]. This study reveals that CHM based on UAV-LiDAR provides accurate measurement of height for lodged maize [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature

Wang

Kohnen

et al. 2021

Plant Phenomics

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High-yield rice cultivation is an effective way to address the increasing food demand worldwide. Correct classification of high-yield rice is a key step of breeding. However, manual measurements within breeding programs are time consuming and have high cost and low throughput, which limit the application in large-scale field phenotyping. In this study, we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders. In total, 13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield. Using an Unmanned Aerial Vehicle (UAV) platform equipped with a hyperspectral camera to capture images over multiple time series, a rice yield classification model based on the XGBoost algorithm was proposed. Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not. The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice. Thus, we developed a low-cost, high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency.

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

Cited by 84 publications

References 56 publications

High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation

High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation

A Review on Drone-Based Data Solutions for Cereal Crops

Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature

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