Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects

Jin, Shichao; Sun, Xiuhong; Wu, Fangfang; Su, Yanjun; Li, Yumei; Song, Shiling; Xu, Kuiying; Ma, Qin; Baret, Frédéric; Jiang, Dong; Ding, Yanfeng; Guo, Qinghua

doi:10.1016/j.isprsjprs.2020.11.006

Cited by 95 publications

(65 citation statements)

References 371 publications

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“…LiDAR is still an emerging technology for use on UAS, and with further research, its usefulness may increase to phenotype additional traits. An in-depth review of LiDAR for plant phenotyping uses was provided by [68]. Table 2 lays out the main types of sensors used as UAV payload.…”

Section: Lidar (Light Detection and Ranging)mentioning

confidence: 99%

UAS-Based Plant Phenotyping for Research and Breeding Applications

et al. 2021

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Unmanned aircraft system (UAS) is a particularly powerful tool for plant phenotyping, due to reasonable cost of procurement and deployment, ease and flexibility for control and operation, ability to reconfigure sensor payloads to diversify sensing, and the ability to seamlessly fit into a larger connected phenotyping network. These advantages have expanded the use of UAS-based plant phenotyping approach in research and breeding applications. This paper reviews the state of the art in the deployment, collection, curation, storage, and analysis of data from UAS-based phenotyping platforms. We discuss pressing technical challenges, identify future trends in UAS-based phenotyping that the plant research community should be aware of, and pinpoint key plant science and agronomic questions that can be resolved with the next generation of UAS-based imaging modalities and associated data analysis pipelines. This review provides a broad account of the state of the art in UAS-based phenotyping to reduce the barrier to entry to plant science practitioners interested in deploying this imaging modality for phenotyping in plant breeding and research areas.

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Section: Lidar (Light Detection and Ranging)mentioning

confidence: 99%

UAS-Based Plant Phenotyping for Research and Breeding Applications

et al. 2021

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“…High throughput and high accuracy are the two most important prerequisites for studying the seasonal and circadian rhythms of structural traits of maize plants and leaves. Previous efforts have demonstrated that TLS can provide high point density, making accurate phenotyping applicable at multiple biological levels [ 14 ]. At the plant level, numerous traits have been accurately extracted from TLS data, such as crop height [ 33 , 37 , 38 ], crown size [ 35 ], and PAI [ 24 , 39 ].…”

Section: Discussionmentioning

confidence: 99%

“…Light detection and ranging (LiDAR), an emerging active sensing technology less influenced by the natural illumination conditions, measures the three-dimensional (3D) structure of a plant with high precision and accuracy. LiDAR has been recognized as a cutting-edge tool in quantifying plant structure across different environments [13,14]. During the last several decades, LiDAR has been extensively used to extract tree height [15][16][17][18], leaf area index [19], and plant volume [20].…”

Section: Introductionmentioning

confidence: 99%

Exploring Seasonal and Circadian Rhythms in Structural Traits of Field Maize from LiDAR Time Series

Jin

Zhang

et al. 2021

Plant Phenomics

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Plant growth rhythm in structural traits is important for better understanding plant response to the ever-changing environment. Terrestrial laser scanning (TLS) is a well-suited tool to study structural rhythm under field conditions. Recent studies have used TLS to describe the structural rhythm of trees, but no consistent patterns have been drawn. Meanwhile, whether TLS can capture structural rhythm in crops is unclear. Here, we aim to explore the seasonal and circadian rhythms in maize structural traits at both the plant and leaf levels from time-series TLS. The seasonal rhythm was studied using TLS data collected at four key growth periods, including jointing, bell-mouthed, heading, and maturity periods. Circadian rhythms were explored by using TLS data acquired around every 2 hours in a whole day under standard and cold stress conditions. Results showed that TLS can quantify the seasonal and circadian rhythm in structural traits at both plant and leaf levels. (1) Leaf inclination angle decreased significantly between the jointing stage and bell-mouthed stage. Leaf azimuth was stable after the jointing stage. (2) Some individual-level structural rhythms (e.g., azimuth and projected leaf area/PLA) were consistent with leaf-level structural rhythms. (3) The circadian rhythms of some traits (e.g., PLA) were not consistent under standard and cold stress conditions. (4) Environmental factors showed better correlations with leaf traits under cold stress than standard conditions. Temperature was the most important factor that significantly correlated with all leaf traits except leaf azimuth. This study highlights the potential of time-series TLS in studying outdoor agricultural chronobiology.

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“…Furthermore, the experiment was performed indoors and mounted on a tripod. Highthroughput plant phenotyping platforms aimed at improving the data collection efficiency and accuracy, thus the efficiency of crop breeding, involves various proximal sensing platforms and remote sensing platforms [1,62]. Lidar sensors can be boarded on proximal sensing planforms (e.g., gantry, vehicle, tripod, backpack, and handheld platforms) both indoors and outdoors [62], while the efficiency of some field-based platforms is limited when applied to large plot areas [63].…”

Section: Discussionmentioning

confidence: 99%

“…Highthroughput plant phenotyping platforms aimed at improving the data collection efficiency and accuracy, thus the efficiency of crop breeding, involves various proximal sensing platforms and remote sensing platforms [1,62]. Lidar sensors can be boarded on proximal sensing planforms (e.g., gantry, vehicle, tripod, backpack, and handheld platforms) both indoors and outdoors [62], while the efficiency of some field-based platforms is limited when applied to large plot areas [63]. For remote sensing platforms, laser sensors can couple with unmanned aerial vehicles, manned aircraft, and satellites for efficient highthroughput monitoring of plant phenotyping at the plot, landscape, or region level [64,65].…”

Section: Discussionmentioning

confidence: 99%

Estimation of Maize Photosynthesis Traits Using Hyperspectral Lidar Backscattered Intensity

Niu

Xiao

et al. 2021

Remote Sensing

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High-throughput measurement of plant photosynthesis ability presents a challenge for the breeding process aimed to improve crop yield. As a novel technique, hyperspectral lidar (HSL) has the potential to characterize the spatial distribution of plant photosynthesis traits under less confounding factors. In this paper, HSL reflectance spectra of maize leaves were utilized for estimating the maximal velocity of Rubisco carboxylation (Vcmax) and maximum rate of electron transport at a specific light intensity (J) based on both reflectance-based and trait-based methods, and the results were compared with the commercial Analytical Spectral Devices (ASD) system. A linear combination of the Lambertian model and the Beckmann law was conducted to eliminate the angle effect of the maize point cloud. The results showed that the reflectance-based method (R2 ≥ 0.42, RMSE ≤ 28.1 for J and ≤4.32 for Vcmax) performed better than the trait-based method (R2 ≥ 0.31, RMSE ≤ 33.7 for J and ≤5.17 for Vcmax), where the estimating accuracy of ASD was higher than that of HSL. The Lambertian–Beckmann model performed well (R2 ranging from 0.74 to 0.92) for correcting the incident angle at different wavelength bands, so the spatial distribution of photosynthesis traits of two maize plants was visually displayed. This study provides the basis for the further application of HSL in high-throughput measurements of plant photosynthesis.

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Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects

Cited by 95 publications

References 371 publications

UAS-Based Plant Phenotyping for Research and Breeding Applications

UAS-Based Plant Phenotyping for Research and Breeding Applications

Exploring Seasonal and Circadian Rhythms in Structural Traits of Field Maize from LiDAR Time Series

Estimation of Maize Photosynthesis Traits Using Hyperspectral Lidar Backscattered Intensity

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