Estimation of leaf area index using inclined smartphone camera

Qu, Yonghua; Wang, Zixin; Shang, Jiali; Liu, Jiangui; Zou, Jie

doi:10.1016/j.compag.2021.106514

Cited by 12 publications

(3 citation statements)

References 42 publications

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“…The commonly used instruments are LAI-2200 canopy analyzer, fisheye camera, etc. [12]. However, traditional optical sensor has light saturation effect, which is easy to underestimate high LAI value.…”

Section: Introductionmentioning

confidence: 99%

Study on the LAI Single Tree Model Based on Terrestrial Laser Scanning

Pan¹,

Fu²

2023

OJG

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Leaf area index (LAI) is a key parameter for studying global terrestrial ecology and environment and has great ecological significance. How to accurately measure and calculate structural parameters of trees has become an urgent matter. This paper reports the use of terrestrial laser scanning (TLS) as a measurement tool to achieve accurate LAI estimation through point cloud preprocessing measures, the LeWos algorithm, and voxel methods. The accuracy and feasibility of this indirect measurement method were explored. It is found that the single wood structure parameters extracted from TLS have a good linear relationship with manual measurement, and the extraction errors meet the requirements of real-scene conversion. The study also found when the voxel size is consistent with the minimum distance of the point cloud set by TLS instrument, it has a strong correlation with the measured value of canopy analyser. These results lay the foundation for conveniently and quickly obtaining structural parameters of trees, tree growth state detection, and canopy ecological benefit assessment.

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Section: Introductionmentioning

confidence: 99%

Study on the LAI Single Tree Model Based on Terrestrial Laser Scanning

Pan¹,

Fu²

2023

OJG

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“…The leaf area index (LAI), defined as the total one-sided leaf area per unit of the surface area of vegetation [1], is a key parameter for the crop management and is closely related to photosynthesis, respiration, and transpiration of crop canopies [2,3]. The accurate and timely monitoring of the LAI is of great importance for efficient agricultural management [4].…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Winter Wheat Leaf Area Index Fitting Model Using Spectral and Canopy Height Model Data from Unmanned Aerial Vehicle Imagery

Zhang

et al. 2022

Remote Sensing

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The leaf area index (LAI) is critical for the respiration, transpiration, and photosynthesis of crops. Color indices (CIs) and vegetation indices (VIs) extracted from unmanned aerial vehicle (UAV) imagery have been widely applied to the monitoring of the crop LAI. However, when the coverage of the crop canopy is large and only spectral data are used to monitor the LAI of the crop, the LAI tends to be underestimated. The canopy height model (CHM) data obtained from UAV-based point clouds can represent the height and canopy structure of the plant. However, few studies have been conducted on the use of the CHM data in the LAI modelling. Thus, in this study, the feasibility of combining the CHM data and CIs and VIs, respectively, to establish LAI fitting models for winter wheat in four growth stages was investigated, and the impact of image resolution on the extraction of remote sensing variables (the CHM data, CIs, and VIs) and on the accuracy of the LAI models was evaluated. Experiments for acquiring remote sensing images of wheat canopies during the four growth stages from the RGB and multispectral sensors carried by a UAV were carried out. The partial least squares regression (PLSR), random forest regression (RFR), and support vector machine regression (SVR) were used to develop the LAI fitting models. Results showed that the accuracy of the wheat LAI models can be improved in the entire growth stages by the use of the additional CHM data with the increment of 0.020–0.268 in R2 for three regression methods. In addition, the improvement from the Cis-based models was more noticeable than the Vis-based ones. Furthermore, the higher the spatial resolution of the CHM data, the better the improvement made by the use of the additional CHM data. This result provides valuable insights and references for UAV-based LAI monitoring.

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“…Jorge Mendes et al designed a low-cost smartphone application to estimate the LAI that uses an ambient light sensor inside the smartphone [10]. Qu et al designed and verified the effective-ness of LAISmart by measuring the LAI of four vegetation types: evergreen coniferous forest (ECF), deciduous broadleaf forest (DBF), deciduous coniferous forest (DNF), and broadleaf crops [11]. Brown et al described the condition of ground vegetation using a low-cost UAV-based DHP system Qiang Ni is with the InfoLab21, School of Computing and Communic ations, Lancaster University, Lancaster, LA1 4WA, UK (e-mail: q.ni@lancaster.ac.uk).…”

Section: Introductionmentioning

confidence: 99%

A Novel Wireless Leaf Area Index Sensor Based on a Combined U-Net Deep Learning Model

Wang

Yin

et al. 2022

IEEE Sensors J.

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Leaf area index (LAI) is an important parameter for forestry vegetation canopy structure investigation and ecological environment model study. Traditional ground direct measuring method is too time and labor consuming, while the remote sensing technique lacks of adequate validation and comparative analysis. Here, a novel wireless LAI sensor based on a lightweight deep learning model (LAINET) has been designed with a Raspberry Pi microcomputer and a LoRa transceiver. The mainly metering pattern of sensor system is the digital hemispherical photography (DHP) methodology based on Beer-Lambert law: firstly, the crown canopy's image is captured and segmented by LAINET, then the vegetation gap fraction can be extracted to calculate the LAI value. Our proposed LAINET consists of a lightweight convolutional neural network (CNN) and a generative adversarial network (GAN). The average accuracy of semantic segmentation (i.e. CNN part) could reach 0.978, and the combination of GAN for image super-resolution reconstruction can improve the accuracy of gap fraction measurement more by 5.5%. In addition, LAINET effectively solves the problem of low segmentation accuracy brought by environmental effects, the separation accuracy in direct sunlight or clear weather has been improved significantly. So the ultimate LAI value can be calculated precisely and stably. Experiment results show that the proposed sensor obtains a fine measuring error of less than 4% when comparing with the commercial plant canopy analyzer HM-G20. Combined with Uninterruptible Power Supply module of 5200 mAh, the sensor can work effectively for about 8 months, principally meeting the deployment and measurement criteria of forestry LAI. Therefore, the wireless sensor presented in this paper has a great application prospect.

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Estimation of leaf area index using inclined smartphone camera

Cited by 12 publications

References 42 publications

Study on the LAI Single Tree Model Based on Terrestrial Laser Scanning

Study on the LAI Single Tree Model Based on Terrestrial Laser Scanning

An Investigation of Winter Wheat Leaf Area Index Fitting Model Using Spectral and Canopy Height Model Data from Unmanned Aerial Vehicle Imagery

A Novel Wireless Leaf Area Index Sensor Based on a Combined U-Net Deep Learning Model

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