Estimation of maize plant height and leaf area index dynamics using an unmanned aerial vehicle with oblique and nadir photography

Abstract

Background and Aims High-throughput phenotyping is a limitation in plant genetics and breeding due to large-scale experiments in the field. Unmanned aerial vehicles (UAVs) can help to extract plant phenotypic traits rapidly and non-destructively with high efficiency. The general aim of this study is to estimate the dynamic plant height and leaf area index (LAI) by nadir and oblique photography with a UAV, and to compare the integrity of the established three-dimensional (3-D) canopy by these two methods. Methods Images were captured by a high-resolution digital RGB camera mounted on a UAV at five stages with nadir and oblique photography, and processed by Agisoft Metashape to generate point clouds, orthomosaic maps and digital surface models. Individual plots were segmented according to their positions in the experimental design layout. The plant height of each inbred line was calculated automatically by a reference ground method. The LAI was calculated by the 3-D voxel method. The reconstructed canopy was sliced into different layers to compare leaf area density obtained from oblique and nadir photography. Key Results Good agreements were found for plant height between nadir photography, oblique photography and manual measurement during the whole growing season. The estimated LAI by oblique photography correlated better with measured LAI (slope = 0.87, R2 = 0.67), compared with that of nadir photography (slope = 0.74, R2 = 0.56). The total number of point clouds obtained by oblique photography was about 2.7–3.1 times than those by nadir photography. Leaf area density calculated by nadir photography was much less than that obtained by oblique photography, especially near the plant base. Conclusions Plant height and LAI can be extracted automatically and efficiently by both photography methods. Oblique photography can provide intensive point clouds and relatively complete canopy information at low cost. The reconstructed 3-D profile of the plant canopy can be easily recognized by oblique photography.

The Application of UAV-Based Hyperspectral Imaging to Estimate Crop Traits in Maize Inbred Lines

“…UAV imaging technology has become one of the important techniques to obtain high-throughput phenotypic traits in breeding and has been widely used in wheat [ 14 ], maize [ 15 ], rice [ 16 ], sorghum [ 17 ], soybean [ 18 ], sugar-beet [ 19 ], potatoes [ 20 ], etc. Initially, UAV digital images were firstly applied in extracting crop phenotypic traits, including emergence rate [ 21 ], canopy coverage [ 22 – 24 ], leaf area index [ 4 , 5 ], and aboveground biomass [ 25 – 27 ]. Due to the lack of near-infrared bands more related to crop nutritional activity, it is difficult to achieve high-precision monitoring of crop nutritional traits, such as canopy chlorophyll content and canopy nitrogen content.…”

UAV Oblique Imagery with an Adaptive Micro-Terrain Model for Estimation of Leaf Area Index and Height of Maize Canopy from 3D Point Clouds

“…Concerning photogrammetry-based 3D imaging approaches and techniques, such as structure from motion (SfM), a general trend can be observed in the literature, indicating that the depth and quality of point cloud, which is influenced by the sensing range determines the accuracy of crop size measurements. As an example, it has been shown that although SfM is a low-cost (compared to LiDAR) and robust solution for providing detailed point clouds from wheat fields depending on the camera angles, it requires significant computational effort for 3D reconstruction [1,35]. In addition, illuminations, ambient light conditions, and external disturbances, such as wind and occlusion, can significantly affect the quality of the reconstructed point clouds.…”

“…Comba et al [23] measured LAI from 3D point clouds acquired in vineyards using UAV multispectral imagery and calculated through a multivariate linear regression model was Research works involving other common approaches for determining crop size traits, such as descriptive and outline-based shape analysis methods [39], have incorporated mathematical models, such as Fourier [40,41] and wavelet analysis [42], as well as artificial intelligence techniques [43,44]. These studies show promising results in deriving height, ratios, LAI, and angles for quantifying and describing object shapes in studies related to maize [35,45], vineyard grape leaves [12], cotton leaves [46], and grapevine berries [47]. As alternative solutions for UAV photogrammetry, 3D imaging and laser scanning instruments, such as LiDAR, have also been used for rapid phenotyping [7,48,49], including estimation of height and the volume of crops and plant canopy [50]; however, the main burden to employ these devices is their high cost and unavailability.…”

“…Chu et al [62] estimated maize canopy height using UAV 3D data combined with color features to accurately identify corn lodging. Che et al [35] estimated height using oblique UAV imagery, and their results showed an improvement between nadir imagery(R 2 = 0.90) and oblique imagery(R 2 = 0.91). A summary of the reviewed literature is provided in Table 1.…”

VISmaF: Synthetic Tree for Immersive Virtual Visualization in Smart Farming. Part I: Scientific Background Review and Model Proposal

“…The Functional-Structural Plant Models (FSPMs) in the last two decades have been developed by scientists in order to explore and integrate the structure and the underlying processes of a plant [10]. Nowadays, the complexity of digital design has reached elevated levels of quality standards allowing the use of 3D structure as output of FSPMs in order to characterize plant phenotypes [11] or as feedback to assess and calculate light partitioning [12]. 3D plants can be part of several modeling systems, being main components of many digital representations of landscapes or natural scenarios [13].…”