- Open Access
Evaluating maize phenotype dynamics under drought stress using terrestrial lidar
- Yanjun Su†1, 2,
- Fangfang Wu†1, 2,
- Zurui Ao3,
- Shichao Jin1, 2,
- Feng Qin4,
- Boxin Liu1, 2,
- Shuxin Pang1,
- Lingli Liu1, 2 and
- Qinghua Guo1, 2Email author
© The Author(s) 2019
- Received: 26 October 2018
- Accepted: 25 January 2019
- Published: 4 February 2019
Maize (Zea mays L.) is the third most consumed grain in the world and improving maize yield is of great importance of the world food security, especially under global climate change and more frequent severe droughts. Due to the limitation of phenotyping methods, most current studies only focused on the responses of phenotypes on certain key growth stages. Although light detection and ranging (lidar) technology showed great potential in acquiring three-dimensional (3D) vegetation information, it has been rarely used in monitoring maize phenotype dynamics at an individual plant level.
In this study, we used a terrestrial laser scanner to collect lidar data at six growth stages for 20 maize varieties under drought stress. Three drought-related phenotypes, i.e., plant height, plant area index (PAI) and projected leaf area (PLA), were calculated from the lidar point clouds at the individual plant level. The results showed that terrestrial lidar data can be used to estimate plant height, PAI and PLA at an accuracy of 96%, 70% and 92%, respectively. All three phenotypes showed a pattern of first increasing and then decreasing during the growth period. The high drought tolerance group tended to keep lower plant height and PAI without losing PLA during the tasseling stage. Moreover, the high drought tolerance group inclined to have lower plant area density in the upper canopy than the low drought tolerance group.
The results demonstrate the feasibility of using terrestrial lidar to monitor 3D maize phenotypes under drought stress in the field and may provide new insights on identifying the key phenotypes and growth stages influenced by drought stress.
- Drought stress
In recent decades, the global climate change has brought more and more frequent heat-waves and severe droughts , which has become an explicit threat to the global food security . Maize (Zea mays L.) is the third most consumed grain in the world and studying how to secure maize yield under drought stress is of great significance. Beyond improving the irrigation technology, cultivating maize varieties with high drought resistance potential is another effective way to reduce the influence of drought stress . Crop phenotyping can provide crop trait estimations and help to identify the traits influenced by drought stress, which is a critical step for crop breeding [45, 47, 67].
Field-based method is the most commonly used for acquiring phenotype measurements currently  and has been widely used to assess the drought resistance of different crops [10, 64]. For example, Faroop et al. , Getnet et al.  and Xu et al.  found that drought stress can influence crop physiological metabolism, leaf size and yield based on field phenotype observations. Among various crop phenotypes, plant height and leaf area have been proved to be the key indictors related to drought stress [11, 23, 34, 52, 58, 71]. Maize plants have to reach a sufficient height to have enough photosynthate for yields, and drought stress can delay the plant development to influence yields . The structure of crop leaves can influence the water and light use efficiency, which are important factors indicating the drought resistance [4, 38, 63]. The vertical structure of crop leaves is often represented by the leaf area density (LAD) and leaf area index (LAI) . LAD is defined as the one-sided leaf area per unit of a horizontal layer volume , and the sum of LAD along the vertical profile is LAI . The horizontal structure of crop leaves can be represented by the projected leaf area (PLA), which is defined as the percentage of the vertically projected canopy area to the total ground area. However, taking field measurements is very time-consuming and labor-intensive, and destructive harvesting methods are frequently used to obtain LAD and LAI. This limits most current studies only focusing on certain key growth stages, such as the tasseling stage and the ripening stage, which cannot reflect the cumulative impact of drought stress on crops through the growing period [14, 53, 71]. Therefore, it is of great significance to monitor the response of maize phenotypes to drought stress during the whole growing period using new crop phenotyping technology.
The development of near-surface remote sensing technology provides new opportunity for non-destructive, high-efficiency and high-resolution (both temporal and spatial) phenotyping. Vegetation indices derived from multispectral/hyperspectral imagery (e.g., normalized difference vegetation index and enhanced vegetation index) have been proven to be correlated to crop phenotypes, such as LAI, biomass, yield, and crop physiological processes [13, 30, 31, 48, 49]. Photogrammetry and computer vision technologies can be further used to estimate three-dimensional (3D) crop phenotypes [1, 7, 8, 15]. For example, Meyer and Davison  used images taken from two perpendicular directions to reconstruct 3D crop models and measure crop phenotypes (e.g. stem diameter and leaf angle) from the 3D models; Paproki et al.  successfully used 64 images taken from different angles to reconstruct 3D surface models of cotton plants; Duan et al. , Rovira-Más et al.  and Chen et al.  used the structure-from-motion method to derive 3D crop point cloud and measure crop phenotypes; Kise et al.  proved that the computer vision-based methods can be used to retrieve plant height at a centimeter-level accuracy. However, these imagery-based remote sensing methods are easily influenced by light conditions and cannot penetrate crop canopy, which limits their applications in field practices [42, 46].
Light detection and ranging (lidar), an active remote sensing technology, can provide accurate 3D information through measuring the time of flight of an emitted laser pulse between the sensor and the target. Besides, the focused short-wavelength laser pulse used by lidar sensors can effectively penetrate vegetation canopy and less influenced by the light condition [12, 21, 61]. Therefore, it has shown great potential for field-based high-throughput crop phenotyping [2, 3, 29, 32, 41, 51, 60, 62, 69]. However, lidar is still a relatively new technology to the field of crop phenotyping. Recently, more efforts have been spent on developing algorithms to automatically extract crop phenotypes from lidar data. For example, Jin et al. [35, 36] proposed methods combining deep learning algorithms with geometric principles to accurately extract 3D maize phenotypes (e.g., plant height, stem diameter, crown diameter, leaf area, leaf inclination angle, leaf length, and leaf width) from terrestrial lidar data. These studies further proved that lidar is an ideal tool for monitoring crop growth dynamics non-destructively in field practices. Nevertheless, to the best of our knowledge, no study has been conducted to explore the responses of 3D maize phenotypes to drought stress using lidar technology. The feasibility of lidar in monitoring maize phenotype dynamics and how maize phenotypes respond to drought stress cumulatively still need to be evaluated and analyzed.
The aim of this study is to evaluate the performance of lidar in monitoring time-series maize phenotypes in field practices and analyze the growth dynamics of different maize varieties under drought stress. Specifically, three questions were addressed. First, how accurate is lidar for maize phenotype extraction in field practices, and how do maize phenotypes change under drought stress during the whole growing period? Second, what maize phenotypes are associated with drought stress, and how can they indicate the occurrence and development of drought in 3D? Third, what are the key phenotypes that lead different maize varieties to have different drought resistance?
Study site and field measurements
To further reduce the influence of wind and edge effect, we sowed 20 maize varieties in the middle of the study site (10 m × 3 m) on May 10th, 2016, and we harvested them on September 20th, 2016. All maize individuals were planted in a regular grid. The distance between each column was 50 cm, and the distance between two adjacent plants along a column was 30 cm. Each column represented one maize variety with 10 individual plants (Fig. 1c). All maize varieties were watered during the first 20 days from sowing (before May 30th, 2016) to ensure the survival rate. The soil moisture was maintained at a level of higher than 30% (volumetric water content) during this stage. Since May 31st, 2016, all maize varieties were not watered anymore, and that day was counted as Day 0 (D0) under drought stress hereafter.
The six maize key growth stages used in this study and their corresponding dates
Days since sowing
Days of drought stressa
To analyze the drought tolerance level of each maize variety, we planted a control group with the same 20 maize varieties in a field nearby the study site. Maize individuals of the control group were sowed and harvested in the same day as the group under drought stress and the same rules were used to manage them, except that they were watered all the time to keep the soil moisture higher than 30% (volumetric water content). After being harvested, the yields of all plants from both the control group and the group under drought stress were collected, dried, weighted and recorded. In this study, plant yields represent grain yields instead of biomass yields.
Terrestrial lidar data collection and preprocessing
Specifications of the FARO Focus3D X120 laser scanner used in this study
Field of view
Emission point density
Laser scan resolution
2 mm @ a 25 m distance
70 million pixels
Phenotype extraction from lidar data
It has been found that phenotypes related to maize plant height and leaf area are highly correlated to drought stress [11, 52, 71]. Therefore, in this study, we calculated the plant height, PAI, PAD and PLA for each maize individual from the lidar data of each growth stage for drought stress analysis. To derive these four parameters for each individual maize, we need to first identify and segment each individual plant from lidar point clouds. Because all maize individuals were planted in a regular grid with large intervals, we created a simple grid with a size of 50 cm × 30 cm and treated the points in each pixel as one maize individual.
Analysis of the influence of drought stress on maize phenotypes
Classification of drought tolerance level
Analysis of maize phenotype dynamics under drought stress
The average plant height, PAI and PLA and the corresponding standard deviations of maize varieties with the same drought tolerance level were calculated at each growth stage, and the change rates of each parameter compared to the previous stage were calculated. These statistics were used to analyze the change dynamics of phenotypes with different drought tolerance levels. Moreover, the statistical test was used to evaluate whether the differences in plant height, PAI and PLA were significant among different growth stages for each drought tolerance level. The null hypothesis was that there was no difference between the values of a phenotype from two growth stages. Besides, we further calculated the average PAD at each height layer for maize varieties with the same drought tolerance level. The time-series vertical PAD profiles from maize varieties with different drought tolerance levels were compared to analyze the responses of maize vertical structures to drought stress.
Lidar-derived maize phenotypes
Statistics of the lidar-derived plant height, PAI and PLA for all maize individuals at each growth stage
Classification of drought tolerance level
Maize phenotype dynamics under drought stress
The PAI of three drought tolerance groups followed a similar changing pattern as the plant height across the growth period, which increased first and then decreased (Fig. 7b). Before D20, the PAI values of three drought levels were close to each other, and the medium drought tolerance group had a relatively higher PAI than the other two groups. From D20 to D45, all three drought tolerance groups still had significant increases in PAI (p < 0.01), but the increase speed became much smaller (Figs. 7b, 8). The PAI of the medium drought tolerance group remained the highest among the three groups. From D45 to D60, the low drought tolerance group and high drought tolerance group still kept a relatively high PAI growth rate, but the PAI growth rate of the medium drought tolerance group began to decrease significantly. The low drought tolerance group replaced the medium drought tolerance group to have the highest PAI among the three groups, and it was also the only group having a significant change in PAI at this period (p < 0.05) (Figs. 7b, 8). From D60 to D95, the PAI of all three groups began to decrease, and the high drought tolerance group had the smallest change magnitude. The high drought tolerance group was also the only group having an insignificant change in PAI during these stages (p > 0.05) (Fig. 8).
The PLA of all three groups also followed the pattern of increasing first and then decreasing (Fig. 7c). Before D20, the PLA of all three groups increased rapidly. The PLA growth rate during this stage was the highest among all growth stages. Among three drought tolerance groups, the medium and high drought tolerance groups had a slightly higher PLA growth rate than the low drought tolerance group. From D20 to D60, all three drought tolerance groups still had continuous increases in PLA, but the increase speed became much slower. The low drought tolerance group was the only group having a significant change in PLA during this period (p < 0.01) (Fig. 8). All three drought tolerance groups had the highest PLA at the stage of D60, and the highest PLA values were close to each other. From D60 to D95, the PLA of all three groups began to have significant decreases (p < 0.05) (Fig. 7c, 8). The medium drought tolerance group had a relatively larger decreasing speed than the other two groups, and its PLA value at the final stage was the smallest among all three groups.
PAD vertical profile dynamics under drought stress
Sensitivity of maize phenotypes to drought stress
All phenotypes showed quick increases in the early growth stages and decreases in the final two growth stages. The decrease of plant height in the final two stages was caused by the fact that the loss of water in the ripening stages made the tassel branches could be easily broken . The decreases of PLA and PAI in the final two stages were possibly caused by the fact that the loss of water in the ripening stages resulted in the rolling of leaves . Since the broken of tassel branches was mostly random in the last two growth stages, but the rolling of leaves was systematic, the relative change of the plant height was the smallest compared to the relative decreases of PLA and PAI (Fig. 7).
From the 3D view, the PAI decrease at the key growth stage of D60 for the high drought tolerance group was caused by the relatively small PAD at the upper two height layers. As can be seen from Fig. 9d, the PAD of the upper two canopy layers become the lowest for the high drought tolerance group, while that of the lower canopy layers was close to each other. Although the upper levels of the high drought tolerance group had a similar number of leaves as the low drought tolerance group, the size of individual leaf at the upper levels of the high drought tolerance group was around 20% smaller than that of the low drought tolerance group. Zhang et al.  found that the transpiration rate and stomatal conductance of maize lower canopy in northern China was smaller than those of higher maize canopy due to the shading effect. Therefore, reducing the upper canopy PAD might be more efficient for maize plants to reach the goal of reducing water demand .
The potential of lidar in field-based phenotyping practices
This study showed that lidar can provide accurate estimations of plant height and PLA. Although the estimation accuracy of PAI was relatively low compared to the other two phenotypes, the estimation accuracy still reached 70% and the RMSE only counted for around 10% of the average PAI value. The relatively low accuracy of PAI estimations might be caused by the following two reasons. First, there was a mismatch between field-based PAI measurements and lidar-derived PAI estimations. The stem, tassel and leaf sheath were very hard to be scanned, and it was difficult to break off leaves at the exact height threshold in the field if a leaf intersected with two height layers, which could possibly bring errors to the field-based PAI measurements. Second, some leaves of one maize plant might grow into the cubic space of another plant, and some maize point clouds from the individual maize segmentation step were incomplete because of the occlusion of leaves, which might bring uncertainty in the lidar-derived estimations. Recently, Jin et al.  proved that the deep learning technique can reach an accuracy of over 90% in individual maize segmentation from lidar data, which has a great potential to further improve the phenotype estimation accuracy at the individual plant level .
The non-destructive and high-accuracy characteristics made lidar technology an ideal tool in phenotyping applications. Especially, lidar technology is not influenced by light conditions, and therefore it can be used in field phenotyping practices. However, currently, the methods to acquire lidar data are still very limited . Although the terrestrial lidar sensor can collect lidar point cloud with high accuracy and high point density, the data collection and preprocessing (e.g., registration among lidar scans) could be very time-consuming and complicate. Moreover, the fusion of lidar with other remote-sensing sensors (e.g., thermal sensor, solar-induced fluorescence sensor, and hyperspectral sensor) are needed to acquire physiology-related phenotypes beyond 3D structures [5, 27, 59, 60]. Therefore, a new platform that can automatically collect and register multi-source remote sensing data for high-throughput field-based phenotyping practices is in great need .
This study used terrestrial lidar technology to extract temporal maize phenotypes. Overall, lidar showed a strong capability in estimating plant height and PLA non-destructively and accurately. Although the accuracy of PAI estimation from lidar was not as high as plant height and PLA estimations, it still reached a R2 of 0.70 and a RMSE of 0.15 m2/m2. Through the whole growth period, the three phenotypes of all 17 maize varieties showed a pattern of increasing first and then decreasing. In the heading and ripening stages, maize varieties with high drought tolerance tended to keep a low plant height and PAI without reducing PLA, which may help to both reduce the demand of water resources and ensure the photosynthesis rate. The relative low plant height and PAI at the tasseling stage would be useful indicators to identify maize varieties with high drought tolerance level during the growth period. Moreover, maize plants with high drought tolerance tended to keep lower upper level PAD than maize plants with low drought tolerance so that they could reduce the transpiration more efficiently.
YS, FW, FQ and QG designed the work; FW, SP, SJ, TH, and BL performed the experiments; FW and SJ processed the experiment data; YS, FW and QG wrote the manuscript; LL, QF, QG, TH, and BL helped to revise the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Availability of data and materials
The scripts and datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Consent for publication
This work was support by the National Key R&D Program of China (Grant Nos. 2017YFC0503905 and 2016YFC0500202), the National Natural Science Foundation of China (Grant Nos. 41471363 and 31741016), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA08040107), the CAS Pioneer Hundred Talents Program.
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