LiDAR and thermal images fusion for ground-based 3D characterisation of fruit trees

Narváez, Francisco J. Yandún; Pedregal, Jaime Salvo del; Prieto, Pablo; Torres-Torriti, Miguel; Cheein, Fernando Auat

doi:10.1016/j.biosystemseng.2016.10.012

Cited by 42 publications

(13 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Novelty and usefulness of the present fusion method emerge through comparison between the previous methods and the present one. Some of the previous fusion systems between the lidar and 2D images were designed and assembled for a specific 2D camera and lidar, where the positional relation of the 2D camera and lidar was fixed by frames or other members [26,27]. In these systems, other 2D cameras and lidars are difficult to use at the same fixed arrangement and settings because the positional relation needs to be changed depending on the size or form of the camera and lidar, and according to image properties, such as resolution, view angle, and distortion.…”

Section: Discussionmentioning

confidence: 99%

“…The fusion of an RGB camera and depth camera (one of the lidar instruments) was used for soybean canopy analysis [26]. The image fusion was conducted by a portable lidar and thermal camera to obtain plant spatial temperature distribution [27]. As shown in these cases, image fusion techniques allow the combination of lidars and various 2D imaging cameras, in addition to the optical cameras.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating 3D Chlorophyll Content Distribution of Trees Using an Image Fusion Method Between 2D Camera and 3D Portable Scanning Lidar

2019

View full text Add to dashboard Cite

An image fusion method has been proposed for plant images taken using a two-dimensional (2D) camera and three-dimensional (3D) portable lidar for obtaining a 3D distribution of physiological and biochemical plant properties. In this method, a 2D multispectral camera with five bands (475-840 nm) and a 3D high-resolution portable scanning lidar were applied to three sets of sample trees. After producing vegetation index (VI) images from multispectral images, 3D point cloud lidar data were projected onto the 2D plane based on perspective projection, keeping the depth information of each of the lidar points. The VI images were 2D registered to the lidar projected image based on the projective transformation and VI 3D point cloud images were reconstructed based on the depth information. Based on the relationship between the VI values and chlorophyll contents taken by a soil and plant analysis development (SPAD)-502 plus chlorophyll meter, 3D distribution images of the chlorophyll contents were produced. Similarly, a thermal 3D image for a sample was also produced. The resultant chlorophyll distribution images offered vertical and horizontal distributions, and those for each orientation for each sample, showing the spatial variability of the distribution and the difference between the samples.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating 3D Chlorophyll Content Distribution of Trees Using an Image Fusion Method Between 2D Camera and 3D Portable Scanning Lidar

2019

View full text Add to dashboard Cite

show abstract

“…Detailed 3D models were also used to extract woody structures of a pear tree 67 which can be useful for both plant phenotyping and field applications (e.g., pruning and similar operations). 3D point cloud from LiDAR, coupled with thermal imaging data, was also used in an avocado orchard to access plant physiological status 68 .…”

Section: High-resolution 3d Modeling Of Tree Cropsmentioning

confidence: 99%

Application of light detection and ranging and ultrasonic sensors to high-throughput phenotyping and precision horticulture: current status and challenges

et al. 2018

View full text Add to dashboard Cite

Ultrasonic and light detection and ranging (LiDAR) sensors have been some of the most deeply investigated sensing technologies within the scope of digital horticulture. They can accurately estimate geometrical and structural parameters of the tree canopies providing input information for high-throughput phenotyping and precision horticulture. A review was conducted in order to describe how these technologies evolved and identify the main investigated topics, applications, and key points for future investigations in horticulture science. Most research efforts have been focused on the development of data acquisition systems, data processing, and high-resolution 3D modeling to derive structural tree parameters such as canopy volume and leaf area. Reported applications of such sensors for precision horticulture were restricted to real-time variable-rate solutions where ultrasonic or LiDAR sensors were tested to adjust plant protection product or fertilizer dose rates according to the tree volume variability. More studies exploring other applications in site-specific management are encouraged; some that integrates canopy sensing data with other sources of information collected at the within-grove scale (e.g., digital elevation models, soil type maps, historical yield maps, etc.). Highly accurate 3D tree models derived from LiDAR scanning demonstrate their great potential for tree phenotyping. However, the technology has not been widely adopted by researchers to evaluate the performance of new plant varieties or the outcomes from different management practices. Commercial solutions for tree scanning of whole groves, orchards, and nurseries would promote such adoption and facilitate more applied research in plant phenotyping and precision horticulture.

show abstract

“…Nguyen et al (2016) used an RGB-D(red, green, blue and depth) camera to design image reconstruction that includes color information and 3D shape information [9], and based on the apple colors and shapes, the fruits on the canopy were accurately detected and located; this algorithm is suitable for robot picking and can estimate the output yield of an orchard. Narváez (2016) used a drone to capture large-area orchards' thermal infrared images, which were merged with laser LIDAR data for 3D reconstruction [10], providing large-area 3D visual images in the Global Navigation Satellite System (GNSS) environment for multiple orchards. Torres-Sánchez (2018) proposed an OBIA algorithm for measuring the 3D geometric features of apricot trees, in which a low-cost RGB camera carried in a drone was used to generate a measured point cloud followed by technical analysis of the image [11].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Morphological Measurement Method for a Fruit Tree Canopy Based on Kinect Sensor Self-Calibration

2019

View full text Add to dashboard Cite

Perception of the fruit tree canopy is a vital technology for the intelligent control of a modern standardized orchard. Due to the complex three-dimensional (3D) structure of the fruit tree canopy, morphological parameters extracted from two-dimensional (2D) or single-perspective 3D images are not comprehensive enough. Three-dimensional information from different perspectives must be combined in order to perceive the canopy information efficiently and accurately in complex orchard field environment. The algorithms used for the registration and fusion of data from different perspectives and the subsequent extraction of fruit tree canopy related parameters are the keys to the problem. This study proposed a 3D morphological measurement method for a fruit tree canopy based on Kinect sensor self-calibration, including 3D point cloud generation, point cloud registration and canopy information extraction of apple tree canopy. Using 32 apple trees (Yanfu 3 variety) morphological parameters of the height (H), maximum canopy width (W) and canopy thickness (D) were calculated. The accuracy and applicability of this method for extraction of morphological parameters were statistically analyzed. The results showed that, on both sides of the fruit trees, the average relative error (ARE) values of the morphological parameters including the fruit tree height (H), maximum tree width (W) and canopy thickness (D) between the calculated values and measured values were 3.8%, 12.7% and 5.0%, respectively, under the V1 mode; the ARE values under the V2 mode were 3.3%, 9.5% and 4.9%, respectively; and the ARE values under the V1 and V2 merged mode were 2.5%, 3.6% and 3.2%, respectively. The measurement accuracy of the tree width (W) under the double visual angle mode had a significant advantage over that under the single visual angle mode. The 3D point cloud reconstruction method based on Kinect self-calibration proposed in this study has high precision and stable performance, and the auxiliary calibration objects are readily portable and easy to install. It can be applied to different experimental scenes to extract 3D information of fruit tree canopies and has important implications to achieve the intelligent control of standardized orchards.

show abstract

LiDAR and thermal images fusion for ground-based 3D characterisation of fruit trees

Cited by 42 publications

References 37 publications

Estimating 3D Chlorophyll Content Distribution of Trees Using an Image Fusion Method Between 2D Camera and 3D Portable Scanning Lidar

Estimating 3D Chlorophyll Content Distribution of Trees Using an Image Fusion Method Between 2D Camera and 3D Portable Scanning Lidar

Application of light detection and ranging and ultrasonic sensors to high-throughput phenotyping and precision horticulture: current status and challenges

Three-Dimensional Morphological Measurement Method for a Fruit Tree Canopy Based on Kinect Sensor Self-Calibration

Contact Info

Product

Resources

About