Unmanned drone multispectral imaging for assessment of wheat and oilseed rape habitus (original) (raw)
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ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016
New sensors mounted on UAV and optimal procedures for survey, data acquisition and analysis are continuously developed and tested for applications in precision farming. Procedures to integrate multispectral aerial data about soil and crop and ground-based proximal geophysical data are a recent research topic aimed to delineate homogeneous zones for the management of agricultural inputs (i.e., water, nutrients). Multispectral and multitemporal orthomosaics were produced over a test field (a 100 m x 200 m plot within a maize field), to map vegetation and soil indices, as well as crop heights, with suitable ground resolution. UAV flights were performed in two moments during the crop season, before sowing on bare soil, and just before flowering when maize was nearly at the maximum height. Two cameras, for color (RGB) and false color (NIR-RG) images, were used. <br><br> The images were processed in Agisoft Photoscan to produce Digital Surface Model (DSM) of bare soil and crop...
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—Agriculture is one of the main sources of income for emerging economies like Guatemala's. Yet, only about 35% of the total arable land is used properly. Most of the farmers depend from subsistence-agriculture, nearly 80% of them live under poverty conditions and are affected by malnutrition. Information about their crop's health would be valuable to considerably increase yields, reduce the use of fertilizer and reduce losses by early detecting diseases. Satellite images and unmanned autonomous vehicles (UAVs) are often used in conjunction with multi-spectral cameras to calculate a normalized difference vegetation index (NDVI), which provides information about crops' health. Commercial NDVI solutions are extremely expensive for Guatemalan farmers, which restricts them from the benefits of the technology. In this paper, we describe the prototype development of a low-cost autonomous unmanned aerial vehicle (UAV) platform for crop analysis via NDVI. To reduce costs, a modified webcam was used for NDVI calculation. Off-the-shelf components were used to facilitate replication by other parties and a quadcopter topology was chosen given the irregularities of Guatemala's geography, Test flights were performed in sugar cane fields to evaluate the platform's autonomy and the NDVI calculation. The integration between subsystems and reliability of the NDVI results are discussed.
Image analysis aplications in precision agriculture
Revista Visión electrónica, 2017
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Unmanned aerial vehicles (UAVs) equipped with spectral sensors have become useful in the fast and non-destructive assessment of crop growth, endurance and resource dynamics. This study is intended to inspect the capabilities of UAV-onboard multispectral sensors for non-destructive phenotype variables, including leaf area index (LAI), leaf mass per area (LMA) and specific leaf area (SLA) of rapeseed oil at different growth stages. In addition, the raw image data with high ground resolution (20 cm) were resampled to 30, 50 and 100 cm to determine the influence of resolution on the estimation of phenotype variables by using vegetation indices (VIs). Quadratic polynomial regression was applied to the quantitative analysis at different resolutions and growth stages. The coefficient of determination (R2) and root mean square error results indicated the significant accuracy of the LAI estimation, wherein the highest R2 values were attained by RVI = 0.93 and MTVI2 = 0.89 at the elongation s...