Imaging from manned ultra-light and unmanned aerial vehicles for estimating properties of spring wheat

Mozgeris, Gintautas; Jonikavičius, Donatas; Jovarauskas, Darius; Zinkevičius, Remigijus; Petkevičius, Sigitas; Steponavičius, Dainius

doi:10.1007/s11119-018-9562-9

Cited by 25 publications

(22 citation statements)

References 63 publications

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“…Vegetation Indices (VIs) have been widely used in remote sensing applications for Precision Agriculture. They are considered to be very effective for monitoring the growth and health of crops in qualitative and quantitative vegetation analysis [25,29,30]. Vegetation Indices are based on the absorption of electromagnetic radiation by the vegetation.…”

Section: Vegetation Indicesmentioning

confidence: 99%

A Review on UAV-Based Applications for Precision Agriculture

2019

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Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

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Section: Vegetation Indicesmentioning

confidence: 99%

A Review on UAV-Based Applications for Precision Agriculture

2019

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“…extensively investigated with hyperspectral remote sensing on the field [22,27,28,31,32] and airplane level [25,27,30], while derivation of CHL from UAV-based hyperspectral data is relatively rare due to it being fairly new technology. Reference [33] acquired hyperspectral snapshot data from spring wheat and predicted chlorophyll contents with a least-squares regression approach, resulting in models of medium quality. They also compared these results with predictions from regular RGB imagery and could not find a large advantage for the use of hyperspectral data.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction

et al. 2018

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The efficient use of nitrogen fertilizer is a crucial problem in modern agriculture. Fertilization has to be minimized to reduce environmental impacts but done so optimally without negatively affecting yield. In June 2017, a controlled experiment with eight different nitrogen treatments was applied to winter wheat plants and investigated with the UAV-based hyperspectral pushbroom camera Resonon Pika-L (400-1000 nm). The system, in combination with an accurate inertial measurement unit (IMU) and precise gimbal, was very stable and capable of acquiring hyperspectral imagery of high spectral and spatial quality. Additionally, in situ measurements of 48 samples (leaf area index (LAI), chlorophyll (CHL), and reflectance spectra) were taken in the field, which were equally distributed across the different nitrogen treatments. These measurements were used to predict grain yield, since the parameter itself had no direct effect on the spectral reflection of plants. Therefore, we present an indirect approach based on LAI and chlorophyll estimations from the acquired hyperspectral image data using partial least-squares regression (PLSR). The resulting models showed a reliable predictability for these parameters (R 2 LAI = 0.79, RMSE LAI [m 2 m −2 ] = 0.18, R 2 CHL = 0.77, RMSE CHL [µg cm −2 ] = 7.02). The LAI and CHL predictions were used afterwards to calibrate a multiple linear regression model to estimate grain yield (R 2 yield = 0.88, RMSE yield [dt ha −1 ] = 4.18). With this model, a pixel-wise prediction of the hyperspectral image was performed. The resulting yield estimates were validated and opposed to the different nitrogen treatments, which revealed that, above a certain amount of applied nitrogen, further fertilization does not necessarily lead to larger yield.From the farmer's perspective, the most important economic parameter is achieved yields. An overdose of N fertilizer, within the legal limits, results higher costs without adding value in terms of additional yield. Further possible regulations for the application of fertilizers should only have a limited negative impact on yields. With controlled experiments, directly comparing the harvested yield resulting from different N applications, one can identify the effects of reduced fertilization. Moreover, new concepts of monitoring these effects during vegetative growth enables the development of precision farming applications, especially created for efficient N fertilization [3].Remote sensing technology at various scales has often proved to be a suitable tool for agricultural crop monitoring [4]. In particular, UAV-supported remote sensing enables very precise monitoring of individual areas through lower flight altitudes and high-resolution data [5]. In recent years, the development of UAV-based hyperspectral recording systems has made rapid progress [6]. In comparison to manned aircraft based systems, the sensors are smaller, lighter, and less costly during acquisition and processing [7]. The great potential of this technology has been demonstrated [8].Hyper...

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“…This procedure involves dealing with non-aligned spectral bands due to the time-sequential imaging principle, i.e., each band of the data cube has a different position and orientation, and, assuming 10 ms exposure time for each band used, the shift between the 1st and 64th bands may amount to 40 m on the ground. Special treatments to obtain the co-registered Rikola bands have been suggested in other studies, like independent photogrammetric processing of all bands and co-registration of mosaics [58]; however, we have observed that with relatively low aircraft speeds and high imaging altitudes the Coregistering v1.1 software works well in forested and urban areas with dense vegetation. Nevertheless, the co-registration quality was manually checked on all images and bands beforehand to proceed with further processing.…”

Section: Imaging Missions and Image Processingmentioning

confidence: 77%

“…To compare alternative imaging equipment and flight settings, we simulated the image acquisition for the same area using EnsoMosaic FlyPlan v7 software in ArcGIS v10.6 and a calculator of basic aerial photography parameters in MS Excel. Only platforms that we had experience using with the Rikola hyperspectral camera were considered, i.e., we discussed the performance of an originally developed eight rotor wing UAV [58] and a Cessna 172 aircraft [63]. Assuming numerous potential options to compare, we restricted the discussion to the most relevant imaging alternatives for HSI and CIR applications in inventories of urban green spaces.…”

Section: Imaging Missions and Image Processingmentioning

confidence: 99%

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Ultra-Light Aircraft-Based Hyperspectral and Colour-Infrared Imaging to Identify Deciduous Tree Species in an Urban Environment

et al. 2018

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One may consider the application of remote sensing as a trade-off between the imaging platforms, sensors, and data gathering and processing techniques. This study addresses the potential of hyperspectral imaging using ultra-light aircraft for vegetation species mapping in an urban environment, exploring both the engineering and scientific aspects related to imaging platform design and image classification methods. An imaging system based on simultaneous use of Rikola frame format hyperspectral and Nikon D800E adopted colour infrared cameras installed onboard a Bekas X32 manned ultra-light aircraft is introduced. Two test imaging flight missions were conducted in July of 2015 and September of 2016 over a 4000 ha area in Kaunas City, Lithuania. Sixteen and 64 spectral bands in 2015 and 2016, respectively, in a spectral range of 500–900 nm were recorded with colour infrared images. Three research questions were explored assessing the identification of six deciduous tree species: (1) Pre-treatment of spectral features for classification, (2) testing five conventional machine learning classifiers, and (3) fusion of hyperspectral and colour infrared images. Classification performance was assessed by applying leave-one-out cross-validation at the individual crown level and using as a reference at least 100 field inventoried trees for each species. The best-performing classification algorithm—multilayer perceptron, using all spectral properties extracted from the hyperspectral images—resulted in a moderate classification accuracy. The overall classification accuracy was 63%, Cohen’s Kappa was 0.54, and the species-specific classification accuracies were in the range of 51–72%. Hyperspectral images resulted in significantly better tree species classification ability than the colour infrared images and simultaneous use of spectral properties extracted from hyperspectral and colour infrared images improved slightly the accuracy over the 2015 image. Even though classifications using hyperspectral data cubes of 64 bands resulted in relatively larger accuracies than with 16 bands, classification error matrices were not statistically different. Alternative imaging platforms (like an unmanned aerial vehicle and a Cessna 172 aircraft) and settings of the flights were discussed using simulated imaging projects assuming the same study area and field of application. Ultra-light aircraft-based hyperspectral and colour-infrared imaging was considered to be a technically and economically sound solution for urban green space inventories to facilitate tree mapping, characterization, and monitoring.

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Imaging from manned ultra-light and unmanned aerial vehicles for estimating properties of spring wheat

Cited by 25 publications

References 63 publications

A Review on UAV-Based Applications for Precision Agriculture

A Review on UAV-Based Applications for Precision Agriculture

High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction

Ultra-Light Aircraft-Based Hyperspectral and Colour-Infrared Imaging to Identify Deciduous Tree Species in an Urban Environment

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