Innovative LIDAR 3D Dynamic Measurement System to Estimate Fruit-Tree Leaf Area

Sanz, Ricardo; Llorens-Calveras, Jordi; Escolà, Alexandre; Arnò, Jaume; Ribes-Dasi, Manel; Masip, Joan; Camp, Ferran; Gràcia-Aguilá, Felip; Solanelles, F.; Planas-DeMartí, Santiago; Pallejà-Cabré, Tomàs; Palacin-Roca, Jordi; Gregorio-Lopez, Eduard; del-Moral-Martínez, Ignacio; Rosell-Polo, Joan R.

doi:10.3390/s110605769

Cited by 94 publications

(55 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Irregularities of leaf surfaces and shapes, overlapping between neighboring plants and mechanical movements produced by wind are among the factors that make accurate 3D reconstructions challenging. In the past years few methodologies, including structured light approaches [36], stereo imaging [37,38] and laser scanning techniques [39,40], have become available to estimate plant structural traits including leaf angle distribution. In particular, stereo imaging has been referred as a rapid and easy approach to characterize the outer canopy leaves, providing a good compromise between accuracy and versatility in the field [38,41].…”

Section: Introductionmentioning

confidence: 99%

Multiangular Observation of Canopy Sun-Induced Chlorophyll Fluorescence by Combining Imaging Spectroscopy and Stereoscopy

et al. 2017

View full text Add to dashboard Cite

Abstract:The effect that the canopy structure and the viewing geometry have on the intensity and the spatial distribution of passively measured sun-induced chlorophyll fluorescence at canopy scale is still not well understood. These uncertainties constrain the potential use of fluorescence to quantify photosynthesis at this level. Using a novel technique, we evaluated the diurnal changes in the spatial distribution of sun-induced fluorescence at 760 nm (F 760 ) within the canopy as a consequence of the spatial disposition of the leaves and the viewing angle of the sensor. High resolution spectral and stereo images of a full sugar beet canopy were recorded simultaneously in the field to estimate maps of F 760 and the surface angle distribution, respectively. A dedicated algorithm was used to align both maps in the post-processing and its accuracy was evaluated using a sensitivity test. The relative angle between sun and the leaf surfaces primarily determined the amount of incident Photosynthetic Active Radiation (PAR), which in turn was reflected in different values of F 760 , with the highest values occurring in leaf surfaces that are perpendicularly oriented to the sun. The viewing angle of the sensor also had an impact in the intensity of the recorded F 760 . Higher viewing angles generally resulted in higher values of F 760 . We attribute these changes to a direct effect of the vegetation directional reflectance response on fluorescence retrieval. Consequently, at leaf surface level, the spatio-temporal variations of F 760 were mainly explained by the sun-leaf-sensor geometry rather than directionality of the fluorescence emission. At canopy scale, the diurnal patterns of F 760 observed on the top-of-canopy were attributed to the complex interplay between the light penetration into the canopy as a function of the display of the various leaves and the fluorescence emission of each leaf which is modulated by the exposure of the individual leaf patch to the incoming light and the functional status of photosynthesis. We expect that forward modeling can help derive analytical simplified skeleton assumptions to scale canopy measurements to the leaf functional properties.

show abstract

Section: Introductionmentioning

confidence: 99%

Multiangular Observation of Canopy Sun-Induced Chlorophyll Fluorescence by Combining Imaging Spectroscopy and Stereoscopy

et al. 2017

View full text Add to dashboard Cite

show abstract

“…In previous studies, most of the presented imaging techniques used to derive the morphological traits of plants were comparatively cost-intensive and focused on either stereo vision [7,9,[11][12][13][14] or were laser scanner-based [15][16][17][18][19] approaches, which required a high level of methodological effort during field measurements and, therefore, resulted in reduced field capability [20,21].…”

Section: Introductionmentioning

confidence: 99%

Imagine All the Plants: Evaluation of a Light-Field Camera for On-Site Crop Growth Monitoring

et al. 2016

View full text Add to dashboard Cite

Abstract:The desire to obtain a better understanding of ecosystems and process dynamics in nature accentuates the need for observing these processes in higher temporal and spatial resolutions. Linked to this, the measurement of changes in the external structure and phytomorphology of plants is of particular interest. In the fields of environmental research and agriculture, an inexpensive and field-applicable on-site imaging technique to derive three-dimensional information about plants and vegetation would represent a considerable improvement upon existing monitoring strategies. This is particularly true for the monitoring of plant growth dynamics, due to the often cited lack of morphological information. To this end, an innovative low-cost light-field camera, the Lytro LF (Light-Field), was evaluated in a long-term field experiment. The experiment showed that the camera is suitable for monitoring plant growth dynamics and plant traits while being immune to ambient conditions. This represents a decisive contribution for a variety of monitoring and modeling applications, as well as for the validation of remote sensing data. This strongly confirms and endorses the assumption that the light-field camera presented in this study has the potential to be a light-weight and easy to use measurement tool for on-site environmental monitoring and remote sensing purposes.

show abstract

“…For the worst case, the volume was about 70% of the value estimated by fully scanned canopies. Other methods for measuring tree canopy structure information using LiDAR were discussed by Pforte et al [33] and Sanz-Cortiella et al [34]. Shi et al [35] introduced a corn plant locations and stalk numbers measurement system using a LiDAR with an encoder mounted on a modified golf cart.…”

Section: Introductionmentioning

confidence: 99%

In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR

Sun

Paterson

2017

Remote Sensing

View full text Add to dashboard Cite

A LiDAR-based high-throughput phenotyping (HTP) system was developed for cotton plant phenotyping in the field. The HTP system consists of a 2D LiDAR and an RTK-GPS mounted on a high clearance tractor. The LiDAR scanned three rows of cotton plots simultaneously from the top and the RTK-GPS was used to provide the spatial coordinates of the point cloud during data collection. Configuration parameters of the system were optimized to ensure the best data quality. A height profile for each plot was extracted from the dense three dimensional point clouds; then the maximum height and height distribution of each plot were derived. In lab tests, single plants were scanned by LiDAR using 0.5 • angular resolution and results showed an R 2 value of 1.00 (RMSE = 3.46 mm) in comparison to manual measurements. In field tests using the same angular resolution; the LiDAR-based HTP system achieved average R 2 values of 0.98 (RMSE = 65 mm) for cotton plot height estimation; compared to manual measurements. This HTP system is particularly useful for large field application because it provides highly accurate measurements; and the efficiency is greatly improved compared to similar studies using the side view scan.

show abstract

Innovative LIDAR 3D Dynamic Measurement System to Estimate Fruit-Tree Leaf Area

Cited by 94 publications

References 22 publications

Multiangular Observation of Canopy Sun-Induced Chlorophyll Fluorescence by Combining Imaging Spectroscopy and Stereoscopy

Multiangular Observation of Canopy Sun-Induced Chlorophyll Fluorescence by Combining Imaging Spectroscopy and Stereoscopy

Imagine All the Plants: Evaluation of a Light-Field Camera for On-Site Crop Growth Monitoring

In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR

Contact Info

Product

Resources

About