Use of digital images to disclose canopy architecture in olive tree

Moriondo, Marco; Leolini, Luisa; Staglianò, N.; Argenti, Giovanni; Trombi, Giacomo; Brilli, Lorenzo; Dibari, Camilla; Leolini, Claudio; Bindi, Marco

doi:10.1016/j.scienta.2016.05.021

Cited by 26 publications

(14 citation statements)

References 35 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is typically accomplished with structure-from-motion (SfM) photogrammetry [25,26], which is a process for extracting geometric structures from camera images taken from different camera stations. General steps for the 3D reconstruction are as follows: (1) feature point extraction using the scale-invariant feature transform (SIFT) algorithm [27]; (2) feature matching [28]; (3) applying the SfM algorithm [29] and a bundle block adjustment [30] to recover the image poses [31] and build sparse 3D feature points; (4) building dense 3D point clouds from camera poses estimated from Step (3) and sparse 3D feature points using the multi-view stereo (MVS) algorithm [31]; and (5) building a 3D polygonal mesh of the object surface based on the dense cloud. Good results from the SfM algorithm rely significantly on the quality (proper image texture, image block geometry, divergence, etc.)…”

Section: Introductionmentioning

confidence: 99%

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

Yang

Wang

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:Hyperspectral remote sensing is used in precision agriculture to remotely and quickly acquire crop phenotype information. This paper describes the generation of a digital orthophoto map (DOM) and radiometric calibration for images taken by a miniaturized snapshot hyperspectral camera mounted on a lightweight unmanned aerial vehicle (UAV). The snapshot camera is a relatively new type of hyperspectral sensor that can acquire an image cube with one spectral and two spatial dimensions at one exposure. The images acquired by the hyperspectral snapshot camera need to be mosaicked together to produce a DOM and radiometrically calibrated before analysis. However, the spatial resolution of hyperspectral cubes is too low to mosaic the images together. Furthermore, there are no systematic radiometric calibration methods or procedures for snapshot hyperspectral images acquired from low-altitude carrier platforms. In this study, we obtained hyperspectral imagery using a snapshot hyperspectral sensor mounted on a UAV. We quantitatively evaluated the radiometric response linearity (RRL) and radiometric response variation (RRV) and proposed a method to correct the RRV effect. We then introduced a method to interpolate position and orientation system (POS) information and generate a DOM with low spatial resolution and a digital elevation model (DEM) using a 3D mesh model built from panchromatic images with high spatial resolution. The relative horizontal geometric precision of the DOM was validated by comparison with a DOM generated from a digital RGB camera. A surface crop model (CSM) was produced from the DEM, and crop height for 48 sampling plots was extracted and compared with the corresponding field-measured crop height to verify the relative precision of the DEM. Finally, we applied two absolute radiometric calibration methods to the generated DOM and verified their accuracy via comparison with spectra measured with an ASD Field Spec Pro spectrometer (Analytical Spectral Devices, Boulder, CO, USA). The DOM had high relative horizontal accuracy, and compared with the digital camera-derived DOM, spatial differences were below 0.05 m (RMSE = 0.035). The determination coefficient for a regression between DEM-derived and field-measured crop height was 0.680. The radiometric precision was 5% for bands between 500 and 945 nm, and the reflectance curve in the infrared spectral region did not decrease as in previous research. The pixel and data sizes for the DOM corresponding to a field area of approximately 85 m × 34 m were small (0.67 m and approximately 13.1 megabytes, respectively), which is convenient for data transmission, preprocessing and analysis. The proposed Remote Sens. 2017, 9, 642; doi:10.3390/rs9070642 www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 642 2 of 21 method for radiometric calibration and DOM generation from hyperspectral cubes can be used to yield hyperspectral imagery products for various applications, particularly precision agriculture.

show abstract

Section: Introductionmentioning

confidence: 99%

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

Yang

Wang

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…BI were obtained considering the vertical angle between a single petiole/branch and stem [32]. We measured LI as the angle between the leaves' surface normal and the zenith, following the methodology used in previous studies [26,43,44]. LA was calculated by color-thresholding the gridded background and setting the surface included within the leaf boundaries [26,45,46].…”

Section: Image Processingmentioning

confidence: 99%

“…Among these techniques, structure for motion (SfM) represents a widely-used approach for plant phenotyping from both proximal and remote platforms [25][26][27][28]. SfM is based on the overlap of a set of two-dimensional (2D) images acquired by digital camera(s) from multiple viewing angles for 3D-object reconstruction [29].…”

Section: Introductionmentioning

confidence: 99%

“…A camera-based SfM approach has been used for a low-cost and detailed reconstruction of herbaceous and tree crops, such as soybean [32], paprika [9] and olive tree [26]. For instance, Liu et al [33] built a 3D representation of a manually-rotated single rosette plant, acquiring 1000 low-resolution images (7.96 megapixels), which is too labor-intensive for a multi-plant application.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping

Rossi

Leolini²,

Costafreda-Aumedes

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

This study aims to test the performances of a low-cost and automatic phenotyping platform, consisting of a Red-Green-Blue (RGB) commercial camera scanning objects on rotating plates and the reconstruction of main plant phenotypic traits via the structure for motion approach (SfM). The precision of this platform was tested in relation to three-dimensional (3D) models generated from images of potted maize, tomato and olive tree, acquired at a different frequency (steps of 4°, 8° and 12°) and quality (4.88, 6.52 and 9.77 µm/pixel). Plant and organs heights, angles and areas were extracted from the 3D models generated for each combination of these factors. Coefficient of determination (R2), relative Root Mean Square Error (rRMSE) and Akaike Information Criterion (AIC) were used as goodness-of-fit indexes to compare the simulated to the observed data. The results indicated that while the best performances in reproducing plant traits were obtained using 90 images at 4.88 µm/pixel (R2 = 0.81, rRMSE = 9.49% and AIC = 35.78), this corresponded to an unviable processing time (from 2.46 h to 28.25 h for herbaceous plants and olive trees, respectively). Conversely, 30 images at 4.88 µm/pixel resulted in a good compromise between a reliable reconstruction of considered traits (R2 = 0.72, rRMSE = 11.92% and AIC = 42.59) and processing time (from 0.50 h to 2.05 h for herbaceous plants and olive trees, respectively). In any case, the results pointed out that this input combination may vary based on the trait under analysis, which can be more or less demanding in terms of input images and time according to the complexity of its shape (R2 = 0.83, rRSME = 10.15% and AIC = 38.78). These findings highlight the reliability of the developed low-cost platform for plant phenotyping, further indicating the best combination of factors to speed up the acquisition and elaboration process, at the same time minimizing the bias between observed and simulated data.

show abstract

“…However, technologies such as LiDAR and TLS can be cost prohibitive [38], and are therefore limited in their application. Recent studies have demonstrated the value of more cost-effective methods of capturing information describing vegetation (fuel) structure and health using image-based point clouds [39,40,41,42,43,44,45]. Image based point clouds are constructed using photogrammetric principles from highly overlapping photography, and the reader is directed to Westoby et al [46] and Snavely et al [47] for further details on the methods.…”

Section: Introductionmentioning

confidence: 99%

Investigating Surface and Near-Surface Bushfire Fuel Attributes: A Comparison between Visual Assessments and Image-Based Point Clouds

Spits

Wallace

Reinke

2017

Sensors

View full text Add to dashboard Cite

Visual assessment, following guides such as the Overall Fuel Hazard Assessment Guide (OFHAG), is a common approach for assessing the structure and hazard of varying bushfire fuel layers. Visual assessments can be vulnerable to imprecision due to subjectivity between assessors, while emerging techniques such as image-based point clouds can offer land managers potentially more repeatable descriptions of fuel structure. This study compared the variability of estimates of surface and near-surface fuel attributes generated by eight assessment teams using the OFHAG and Fuels3D, a smartphone method utilising image-based point clouds, within three assessment plots in an Australian lowland forest. Surface fuel hazard scores derived from underpinning attributes were also assessed. Overall, this study found considerable variability between teams on most visually assessed variables, resulting in inconsistent hazard scores. Variability was observed within point cloud estimates but was, however, on average two to eight times less than that seen in visual estimates, indicating greater consistency and repeatability of this method. It is proposed that while variability within the Fuels3D method may be overcome through improved methods and equipment, inconsistencies in the OFHAG are likely due to the inherent subjectivity between assessors, which may be more difficult to overcome. This study demonstrates the capability of the Fuels3D method to efficiently and consistently collect data on fuel hazard and structure, and, as such, this method shows potential for use in fire management practices where accurate and reliable data is essential.

show abstract

Use of digital images to disclose canopy architecture in olive tree

Cited by 26 publications

References 35 publications

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping

Investigating Surface and Near-Surface Bushfire Fuel Attributes: A Comparison between Visual Assessments and Image-Based Point Clouds

Contact Info

Product

Resources

About