Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor

Krienke, Brian; Ferguson, Richard B.; Schlemmer, Michael R.; Holland, Kyle; Marx, David B.; Eskridge, Kent M.

doi:10.1007/s11119-017-9534-5

Cited by 21 publications

(21 citation statements)

References 27 publications

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“…Compared with other field-based HTPPs (e.g., unmanned ground vehicle), they provide more effective and simultaneous measurements of all plots in a relative large field (Holman et al, 2016 ; Liu et al, 2017b ). Thus, the emerging UAV-based HTPPs have been increasingly used to evaluate plant water stress (Sullivan et al, 2007 ; Baluja et al, 2012 ; Gonzalez-Dugo et al, 2013 ; Ludovisi et al, 2017 ), nitrogen content (Kefauver et al, 2017 ; Krienke et al, 2017 ), and growth parameters (Brede et al, 2017 ; Jin et al, 2017 ; Yue et al, 2017 ) at field scale. During the FBP of a crop's entire growth period, there is a strong interest in evaluating its growth traits such as canopy cover (Irmak et al, 2000 ; Breckenridge et al, 2011 ; Córcoles et al, 2013 ) and plant height (Bendig et al, 2015 ; Holman et al, 2016 ; Schirrmann et al, 2017 ; Watanabe et al, 2017 ; Yue et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

et al. 2018

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The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%. Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages.

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Section: Introductionmentioning

confidence: 99%

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

et al. 2018

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“…However, considering previous studies with manned aircraft (Lamb et al, 2009, 2014) and unmanned aerial vehicles (Krienke et al, 2017), still, little attention has been paid to investigate the possibility of applying active sensors on ultra low-altitude aerial vehicles. A potential issue for these studies was the effective sensing distance to accurately collect crop canopy reflectance.…”

Section: Introductionmentioning

confidence: 99%

Potential of UAV-Based Active Sensing for Monitoring Rice Leaf Nitrogen Status

Ding

Kuang

et al. 2018

Front. Plant Sci.

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Unmanned aerial vehicle (UAV) based active canopy sensors can serve as a promising sensing solution for the estimation of crop nitrogen (N) status with great applicability and flexibility. This study was endeavored to determine the feasibility of UAV-based active sensing to monitor the leaf N status of rice (Oryza sativa L.) and to examine the transferability of handheld-based predictive models to UAV-based active sensing. In this 3-year multi-locational study, varied N-rates (0–405 kg N ha−1) field experiments were conducted using five rice varieties. Plant samples and sensing data were collected at critical growth stages for growth analysis and monitoring. The portable active canopy sensor RapidSCAN CS-45 with red, red edge, and near infrared wavebands was used in handheld mode and aerial mode on a gimbal under a multi-rotor UAV. The results showed the great potential of UAV-based active sensing for monitoring rice leaf N status. The vegetation index-based regression models were built and evaluated based on Akaike information criterion and independent validation to predict rice leaf dry matter, leaf area index, and leaf N accumulation. Vegetation indices composed of near-infrared and red edge bands (NDRE or RERVI) acquired at a 1.5 m aviation height had a good performance for the practical application. Future studies are needed on the proper operation mode and means for precision N management with this system.

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“…This approach has been applied to measure the canopy temperatures and lodging situations in thousands of field plots as well as canopy structure at various test sites [87,88]. UAVs or drones are now used to carry multispectral cameras (such as the Mini-MCA6), hyperspectral cameras (such as the Cubert UHD185) and active non-imaging sensors (such as the RapidSCAN) that enables the derivation of the LAI, aboveground biomass, and nitrogen nutrient status for crops such as wheat, rice, cotton, and corn [89][90][91]. For example, in Japan, TM-based RS data has been used to monitor nitrogen, amylose, amylopectin, and other quality indicators in rice, which has been used to guide the application of nitrogen fertilizer [92].…”

Section: Real-time Variable Fertilization Systems Based On Crop Phenomentioning

confidence: 99%

Progress and development on biological information of crop phenotype research applied to real-time variable-rate fertilization

et al. 2020

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Background: Variable-rate fertilization is crucial in the implementation of precision agriculture and for ensuring reasonable and efficient fertilizer application and nutrient management that is tailored to local conditions. The overall goal of these technologies is to maximize grain output and minimize fertilizer input and, thus, achieve the optimal input-output production ratio. As the main form of variable-rate fertilization, real-time variable-rate control technology adjusts fertilizer application according to the growth status and nutrient information of crops and, as such, its effective application relies on the stable and accurate acquisition of crop phenotypic information. Results: Due to the relationship between crop phenotype and real-time fertilizer demand, phenotypic information has been increasingly applied in these contexts in recent years. Here, the establishment and characteristics of inversion models between crop phenotypic information and nutritional status are reviewed. The principles of real-time monitoring applications, the key technologies relating to crop phenotypic biological parameters, and the existing challenges for real-time variable-rate fertilization technology are also evaluated. Future research directions are then discussed in the specific context of the need for sustainable development of modern agriculture in China. Conclusion: This paper provides a theoretical reference for the construction of scientific management technology systems aimed at reducing fertilizer application and maximizing output, and for the development of relevant technologies in the specific context of China.

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Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor

Cited by 21 publications

References 27 publications

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

Potential of UAV-Based Active Sensing for Monitoring Rice Leaf Nitrogen Status

Progress and development on biological information of crop phenotype research applied to real-time variable-rate fertilization

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