Rapid Mapping of the Leaf Area Index in Agricultural Crops

Gebbers, Robin; Ehlert, Detlef; Adamek, Rolf

doi:10.2134/agronj2011.0201

Cited by 58 publications

(31 citation statements)

References 44 publications

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“…In-situ studies confirmed the potential of ground-based LIDAR methods, also known as terrestrial laser scanning (TLS), for the assessment of plant parameters in agricultural applications. Previous studies focused on the acquisition of plant height [25], post-harvest growth [26], leaf area index [27], crop density [28,29], nitrogen status [30], or the detection of individual plants [31,32]. Moreover, the potential of TLS for estimating the biomass of small-grain cereals was emphasized [33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Transferability of Models for Estimating Paddy Rice Biomass from Spatial Plant Height Data

et al. 2015

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It is known that plant height is a suitable parameter for estimating crop biomass. The aim of this study was to confirm the validity of spatial plant height data, which is derived from terrestrial laser scanning (TLS), as a non-destructive estimator for biomass of paddy rice on the field scale. Beyond that, the spatial and temporal transferability of established biomass regression models were investigated to prove the robustness of the method and evaluate the suitability of linear and exponential functions. In each growing season of two years, three campaigns were carried out on a field experiment and on a farmer's conventionally managed field. Crop surface models (CSMs) were generated from the TLS-derived point clouds for calculating plant height with a very high spatial resolution of 1 cm. High coefficients of determination between CSM-derived and manually measured plant heights (R 2 : 0.72 to 0.91) confirm the applicability of the approach. Yearly averaged differences between the measurements were ~7% and ~9%. Biomass regression models were established from the field experiment data sets, based on strong coefficients of determination between plant height and dry biomass (R : 0.60 to 0.90 and 0.56 to 0.85 for linear and exponential models, respectively). Hence, the suitability of TLS-derived spatial plant height as a non-destructive estimator for biomass of paddy rice on the field scale was verified and the transferability demonstrated.

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Section: Introductionmentioning

confidence: 99%

Transferability of Models for Estimating Paddy Rice Biomass from Spatial Plant Height Data

et al. 2015

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“…Several crops were already investigated with ground-based LIDAR approaches for various purposes like determining plant height (Zhang and Grift, 2012) and estimating biomass (Keightley and Bawden, 2010;Ehlert et al, 2009;, crop density (Hosoi and Omasa, 2009;Saeys et al, 2009), or leaf area index (Gebbers et al, 2011). Furthermore, analysis of the measured intensity values can lead to the detection of single plants of maize (Höfle, 2013) or sugar beet .…”

Section: Introductionmentioning

confidence: 99%

Precise plant height monitoring and biomass estimation with Terrestrial Laser Scanning in paddy rice

Tilly

Hoffmeister

Cao

et al. 2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Optimizing crop management is a major topic in the field of precision agriculture as the growing world population puts pressure on the efficiency of field production. Accordingly, methods to measure plant parameters with the needed precision and within-field resolution are required. Studies show that Terrestrial Laser Scanning (TLS) is a suitable method to capture small objects like crop plants. In this contribution, the results of multi-temporal surveys on paddy rice fields with the TLS system Riegl LMS-Z420i are presented. Three campaigns were carried out during the key vegetative stage of rice plants in the growing period 2012 to monitor the plant height. The TLS-derived point clouds are interpolated to visualize plant height above ground as crop surface models (CSMs) with a high resolution of 0.01 m. Spatio-temporal differences within the data of one campaign and between consecutive campaigns can be detected. The results were validated against manually measured plant heights with a high correlation (R² = 0.71). Furthermore, the dependence of actual biomass from plant height was evaluated. To the present, no method for the non-destructive determination of biomass is found yet. Thus, plant parameters, like the height, have to be used for biomass estimations. The good correlation (R² = 0.66) leads to the assumption that biomass can be estimated from plant height measurements. The results show that TLS can be considered as a very promising tool for precision agriculture.

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“…This assumption is realistic since HUR1 and HUR2 are continuously measured by soil water content micro-sensors. Up to now, LAI is obtained by reference measurements but automatic methods are in progress (Gebbers et al, 2011;Leemans et al, 2012;Sakamoto et al, 2012). …”

Section: Problem Formulationmentioning

confidence: 99%

“…More recently, methods have been proposed to obtain real-time data processing of LAI. Gebbers et al (2011) developed a rapid mapping of the LAI on the basis of ground-based laser rangefinders mounted on a vehicle. Sakamoto et al (2012) applied a camera-based observation system using vegetation indices for monitoring of seasonal changes in several biophysical parameters of maize such as LAI.…”

Section: Introductionmentioning

confidence: 99%

Bayesian methods for predicting LAI and soil water content

et al. 2013

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LAI of winter wheat (Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was assumed to progress according to a Bayesian probabilistic state space model, in which real values of LAI and soil water content were daily introduced in order to correct the model trajectory and reach better future evolution. The chosen crop model was mini STICS which can reduce the computing and execution times while ensuring the robustness of data processing and estimation. To predict simultaneously state variables and model parameters in this non-linear environment, three techniques were used: Extended Kalman Filtering (EKF), Particle Filtering (PF), and Variational Filtering (VF). The significantly improved performance of the VF method when compared to EKF and PF is demonstrated. The variational filter has a low computational complexity and the convergence speed of states and parameters estimation can be adjusted independently. Detailed case studies demonstrated that the root mean square error (RMSE) of the three estimated states (LAI and soil water content of two soil layers) was smaller and that the convergence of all considered parameters was ensured when using VF. The interest of assimilating measurements in a crop model allows accurate prediction of LAI and soil water content at a local scale. As these biophysical properties are key parameters in the cropplant system characterization, the system has the potential to be used in precision farming to aid farmers and decision makers in developing strategies for site-specific management of inputs, such as fertilizers and water irrigation.

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Rapid Mapping of the Leaf Area Index in Agricultural Crops

Cited by 58 publications

References 44 publications

Transferability of Models for Estimating Paddy Rice Biomass from Spatial Plant Height Data

Transferability of Models for Estimating Paddy Rice Biomass from Spatial Plant Height Data

Precise plant height monitoring and biomass estimation with Terrestrial Laser Scanning in paddy rice

Bayesian methods for predicting LAI and soil water content

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