Analysis of cotton height spatial variability based on UAV-LiDAR

Liu, Kuan; Dong, Xiaopeng; Qiu, Baijing

doi:10.33440/j.ijpaa.20200303.79

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…Though more expensive than image-based 3D reconstructions such as structure-from-motion (SFM) [19][20][21], LiDAR systems have provided more accurate measurements of plant phenotypes such as height and biomass as a result of their finer spatial resolution compared to SFM [22]. LiDAR systems were utilized to estimate structural traits including height [21,23,24], leaf area index [25][26][27], biomass [19,20] and biotic or abiotic stresses that are expressed mostly via a change in the structural traits [15,[28][29][30]. LiDAR-based high-throughput phenotyping systems have been deployed for situations where the stress is expressed via the change in canopy color and have primarily been limited to applications in controlled environments.…”

Section: Introductionmentioning

confidence: 99%

Soybean Canopy Stress Classification Using 3D Point Cloud Data

Young,

Chiranjeevi,

Elango

et al. 2024

Agronomy

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Automated canopy stress classification for field crops has traditionally relied on single-perspective, two-dimensional (2D) photographs, usually obtained through top-view imaging using unmanned aerial vehicles (UAVs). However, this approach may fail to capture the full extent of plant stress symptoms, which can manifest throughout the canopy. Recent advancements in LiDAR technologies have enabled the acquisition of high-resolution 3D point cloud data for the entire canopy, offering new possibilities for more accurate plant stress identification and rating. This study explores the potential of leveraging 3D point cloud data for improved plant stress assessment. We utilized a dataset of RGB 3D point clouds of 700 soybean plants from a diversity panel exposed to iron deficiency chlorosis (IDC) stress. From this unique set of 700 canopies exhibiting varying levels of IDC, we extracted several representations, including (a) handcrafted IDC symptom-specific features, (b) canopy fingerprints, and (c) latent feature-based features. Subsequently, we trained several classification models to predict plant stress severity using these representations. We exhaustively investigated several stress representations and model combinations for the 3-D data. We also compared the performance of these classification models against similar models that are only trained using the associated top-view 2D RGB image for each plant. Among the feature-model combinations tested, the 3D canopy fingerprint features trained with a support vector machine yielded the best performance, achieving higher classification accuracy than the best-performing model based on 2D data built using convolutional neural networks. Our findings demonstrate the utility of color canopy fingerprinting and underscore the importance of considering 3D data to assess plant stress in agricultural applications.

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Section: Introductionmentioning

confidence: 99%

Soybean Canopy Stress Classification Using 3D Point Cloud Data

Young,

Chiranjeevi,

Elango

et al. 2024

Agronomy

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“…It has been reported that crop-specific CHM perform better than generalized crop models [11]. Studies were focused on segmentation of individual plants as is typical for maize [12,13] or on segmentation of entire field-trial plots in case of other cereals, sorghum or cotton [14][15][16][17]. The conversion of point-clouds to CHM can be done manually using software for digital terrain model processing [16], but specialized algorithms have also been reported [18].…”

Section: Introductionmentioning

confidence: 99%

Crop growth dynamics: Fast automatic analysis of LiDAR images in field-plot experiments by specialized software ALFA

Fryčák,

Fürst,

Koprna

et al. 2024

PLoS ONE

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Repeated measurements of crop height to observe plant growth dynamics in real field conditions represent a challenging task. Although there are ways to collect data using sensors on UAV systems, proper data processing and analysis are the key to reliable results. As there is need for specialized software solutions for agricultural research and breeding purposes, we present here a fast algorithm ALFA for the processing of UAV LiDAR derived point-clouds to extract the information on crop height at many individual cereal field-plots at multiple time points. Seven scanning flights were performed over 3 blocks of experimental barley field plots between April and June 2021. Resulting point-clouds were processed by the new algorithm ALFA. The software converts point-cloud data into a digital image and extracts the traits of interest–the median crop height at individual field plots. The entire analysis of 144 field plots of dimension 80 x 33 meters measured at 7 time points (approx. 100 million LiDAR points) takes about 3 minutes at a standard PC. The Root Mean Square Deviation of the software-computed crop height from the manual measurement is 5.7 cm. Logistic growth model is fitted to the measured data by means of nonlinear regression. Three different ways of crop-height data visualization are provided by the software to enable further analysis of the variability in growth parameters. We show that the presented software solution is a fast and reliable tool for automatic extraction of plant height from LiDAR images of individual field-plots. We offer this tool freely to the scientific community for non-commercial use.

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“…Multiple examples of accurate plant height estimates using UAV imagery have been shown in crops such as barley ( Hordeum vulgare L.) (Bendig et al ., 2014; Herzig et al ., 2021), cabbage ( Brassica oleracea var. capitata L.) (Moeckel et al ., 2018), cotton ( Gossypium hirsutum L.) (Feng et al ., 2019; Liu et al ., 2020), eggplant ( Solanum melongena L.) (Moeckel et al ., 2018), faba bean ( Vicia faba L.) (Ji et al ., 2022), maize (Anthony et al ., 2014; Geipel et al ., 2014; Grenzdörffer, 2014; Su et al ., 2019; Varela et al ., 2017; Anderson et al ., 2019; Malambo et al ., 2018; Shi et al ., 2016; Tirado et al ., 2020; Adak, Murray, Božinović, et al ., 2021; Letsoin et al ., 2023), potato ( Solanum tuberosum L.) (de Jesus Colwell et al ., 2021; Xie et al ., 2022; Njane et al ., 2023), rapeseed ( Brassica napus L.) (Xie et al ., 2021), sorghum ( Sorghum bicolor L.) (Chang et al ., 2017; Shi et al ., 2016; Watanabe et al ., 2017; Gano et al ., 2021), soybean ( Glycine max L.) (Li et al ., 2022), tomato ( Solanum lycopersicum L.) (Moeckel et al ., 2018), and wheat ( Triticum aestivum L.) (Holman et al ., 2016; Madec et al ., 2017; Michalski et al ., 2018; Volpato et al ., 2021). Studies on plant height using UAVs have shown variable levels of success when compared to manual measurements due to plant structure, field layout, and improvements in best practices as more research was completed (Holman et al ., 2016; Sweet et al ., 2022).…”

Section: Introductionmentioning

confidence: 99%

Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits

Sweet,

Tirado,

Cooper

et al. 2024

Preprint

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Plant height can be an indicator of plant health across environments and used to identify superior genotypes or evaluate abiotic stress factors. Typically plant height is measured at a single time point when plants have reached terminal height for the season. Evaluating plant height using unoccupied aerial vehicles (UAVs) is faster, allowing for measurements throughout the growing season, which facilitates a better understanding of plant-environment interactions and the genetic basis of this complex trait. To assess variation throughout development, plant height data was collected weekly for a panel of ∼500 diverse maize inbred lines over four growing seasons. The variation in plant height throughout the season was significantly explained by genotype, year, and genotype-by-year interactions to varying extents throughout development. Genome-wide association studies revealed significant SNPs associated with plant height and growth rate at different parts of the growing season specific to certain phases of vegetative growth that would not be identified by terminal height associations alone. When plant height growth rates were compared to growth rates estimated from canopy cover, greater Fréchet distance stability was observed in plant height growth curves than for canopy cover. This indicated canopy cover may be more useful for understanding environmental modulation of overall plant growth and plant height better for understanding genotypic modulation of overall plant growth. This study demonstrated that substantial information can be gained from high temporal resolution data to understand how plants differentially interact with the environment and can enhance our understanding of the genetic basis of complex polygenic traits.

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Analysis of cotton height spatial variability based on UAV-LiDAR

Cited by 6 publications

References 26 publications

Soybean Canopy Stress Classification Using 3D Point Cloud Data

Soybean Canopy Stress Classification Using 3D Point Cloud Data

Crop growth dynamics: Fast automatic analysis of LiDAR images in field-plot experiments by specialized software ALFA

Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits

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