A mobile sensor for leaf area index estimation from canopy light transmittance in wheat crops

Schirrmann, Michael; Hamdorf, André; Giebel, Antje; Dammer, Karl‐Heinz; Garz, Andreas

doi:10.1016/j.biosystemseng.2015.09.005

Cited by 14 publications

(9 citation statements)

References 30 publications

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“…The studies reported R 2 values for FBM between 0.77 and 0.99, for DBM between 0.72 and 0.99 and for LAI between 0.70 and 0.96 [22,54,55]. Sensing LAI was further investigated using a combined radiometer and ultra-sound sensing system yielding an R 2 value of 0.84 [24] and a mobile LAI system based on canopy light transmittance yielding R 2 values ranging from 0.73 to 0.86 [20] between the sensor and LAI reference measurements.…”

Section: Discussionmentioning

confidence: 99%

“…Most of these principles involve time-demanding measurements or sophisticated measurement protocols that can only be performed manually in a stop-and-go mode. Their use for online or high-throughput measurements was discussed only very recently in research, e.g., mobile LAI determination by sunlight interception [20]. For the real-time determination of biophysical parameters and N status, many active sensing approaches have been studied, such as LiDAR, optical sensors and ultra-sound sensors [21][22][23][24], in addition to more refined solutions, e.g., the pendulum operating measurement principle Crop-Meter [25,26].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

et al. 2016

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Abstract:Monitoring the dynamics in wheat crops requires near-term observations with high spatial resolution due to the complex factors influencing wheat growth variability. We studied the prospects for monitoring the biophysical parameters and nitrogen status in wheat crops with low-cost imagery acquired from unmanned aerial vehicles (UAV) over an 11 ha field. Flight missions were conducted at approximately 50 m in altitude with a commercial copter and camera system-three missions were performed between booting and maturing of the wheat plants and one mission after tillage. Ultra-high resolution orthoimages of 1.2 cm·px −1 and surface models were generated for each mission from the standard red, green and blue (RGB) aerial images. The image variables were extracted from image tone and surface models, e.g., RGB ratios, crop coverage and plant height. During each mission, 20 plots within the wheat canopy with 1 × 1 m 2 sample support were selected in the field, and the leaf area index, plant height, fresh and dry biomass and nitrogen concentrations were measured. From the generated UAV imagery, we were able to follow the changes in early senescence at the individual plant level in the wheat crops. Changes in the pattern of the wheat canopy varied drastically from one mission to the next, which supported the need for instantaneous observations, as delivered by UAV imagery. The correlations between the biophysical parameters and image variables were highly significant during each mission, and the regression models calculated with the principal components of the image variables yielded R 2 values between 0.70 and 0.97. In contrast, the models of the nitrogen concentrations yielded low R 2 values with the best model obtained at flowering (R 2 = 0.65). The nitrogen nutrition index was calculated with an accuracy of 0.10 to 0.11 NNI for each mission. For all models, information about the surface models and image tone was important. We conclude that low-cost RGB UAV imagery will strongly aid farmers in observing biophysical characteristics, but it is limited for observing the nitrogen status within wheat crops.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

et al. 2016

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“…This could be achieved by combining the coverage level with crop height, which would require, for example, an ultrasonic sensor in addition to the camera‐sensor (sensor‐fusion). A new optoelectronic vertical sensor (canopy‐meter), which operates inside the wheat canopy, has been developed to measure the LAI directly …”

Section: Discussionmentioning

confidence: 99%

“…A new optoelectronic vertical sensor (canopy-meter), which operates inside the wheat canopy, has been developed to measure the LAI directly. 27…”

Section: Discussionmentioning

confidence: 99%

Impact of sensor‐controlled variable‐rate fungicide application on yield, senescence and disease occurrence in winter wheat fields

Tackenberg¹,

Volkmar

Schirrmann

et al. 2018

Pest Management Science

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“…The LAI measurements were repeated weekly and biweekly in the same measurement points. This indirect LAI method was validated because first time SunScan was used in woody SRC poplar plantation/referenced (r 2 = 0.82, y = 1.05 − 0.7, p < 0.001) for SRC poplar [47], and for AFCs it followed the calibration from earlier studies in different crops such as soybean, maize, and wheat [48][49][50][51]. For daily LAI, the weekly and biweekly LAI data were interpolated using a regression equation with the cumulative mean daily air temperature sum (>5 • C) [52].…”

Section: Leaf Area Development Measurementsmentioning

confidence: 99%

The Evaluation of Radiation Use Efficiency and Leaf Area Index Development for the Estimation of Biomass Accumulation in Short Rotation Poplar and Annual Field Crops

Tripathi,

Pohanková,

Fischer

et al. 2018

Forests

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Abstract:We evaluated the long-term pattern of leaf area index (LAI) dynamics and radiation use efficiency (RUE) in short rotation poplar in uncoppice (single stem) and coppice (multi-stem) plantations, and compared them to annual field crops (AFCs) as an alternative for bioenergy production while being more sensitive to weather fluctuation and climate change. The aim of this study was to evaluate the potential of LAI and RUE as indicators for bioenergy production and indicators of response to changing environmental conditions. For this study, we selected poplar clone J-105 (Populus nigra L. × P. maximowiczii A. Henry) and AFCs such as barley (Hordeum vulgare L.), wheat (Triticum aestivum L.), maize (Zea mays L.), and oilseed rape (Brassica napus L.), and compared their aboveground dry mass (AGDM) production in relation to their LAI development and RUE. The results of the study showed the long-term maximum LAI (LAI max ) to be 9.5 in coppice poplar when compared to AFCs, where LAI max did not exceed the value 6. The RUE varied between 1.02 and 1.48 g MJ −1 in short rotation poplar and between 0.72 and 2.06 g MJ −1 in AFCs. We found both LAI and RUE contributed to AGDM production in short rotation poplar and RUE only contributed in AFCs. The study confirms that RUE may be considered an AGDM predictor of short rotation poplar and AFCs. This may be utilized for empirical estimates of yields and also contribute to improve the models of short rotation poplar and AFCs for the precise prediction of biomass accumulation in different environmental conditions.

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A mobile sensor for leaf area index estimation from canopy light transmittance in wheat crops

Cited by 14 publications

References 30 publications

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

Impact of sensor‐controlled variable‐rate fungicide application on yield, senescence and disease occurrence in winter wheat fields

The Evaluation of Radiation Use Efficiency and Leaf Area Index Development for the Estimation of Biomass Accumulation in Short Rotation Poplar and Annual Field Crops

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