Analyzing growing plants from 4D point cloud data

Li, Yangyan; Fan, Xiaochen; Mitra, Niloy J.; Chamovitz, Daniel; Cohen–Or, Daniel; Chen, Baoquan

doi:10.1145/2508363.2508368

Cited by 87 publications

(82 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…LiDAR technologies consist of an active laser sensor providing direct measurements of canopy architecture and organ distribution [25] for the estimation of plant volume, LAI, and biomass [25,35,37,38], thus allowing plant growth analyses from the vegetative to reproductive stages [26,39,40]. Other techniques include time-of-flight cameras and ultrasonic sensors, reviewed in [14].…”

Section: Radiation Interceptionmentioning

confidence: 99%

“…In fact, reflectance, absorbance, and transmittance of electromagnetic radiation are influenced by plant tissue morphology and elemental and molecular composition [15]. Digital cameras, as previously described, can be adopted to capture images in the VIS range, providing useful information on plant color, for the detection of plant stress and plant classification, in addition to structural and biometric characteristics [38][39][40][46][47][48][49][50]. However, PRSs based on reflectance measurement can provide more useful information in plant physiology studies.…”

Section: Radiation Reflectance Absorbance and Transmittancementioning

confidence: 99%

See 1 more Smart Citation

Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges

Tripodi¹,

Massa²,

Venezia³

et al. 2018

Agronomy

View full text Add to dashboard Cite

Abstract:Increasing the ability to investigate plant functions and structure through non-invasive methods with high accuracy has become a major target in plant breeding and precision agriculture. Emerging approaches in plant phenotyping play a key role in unraveling quantitative traits responsible for growth, production, quality, and resistance to various stresses. Beyond fully automatic phenotyping systems, several promising technologies can help accurately characterize a wide range of plant traits at affordable costs and with high-throughput. In this review, we revisit the principles of proximal and remote sensing, describing the application of non-invasive devices for precision phenotyping applied to the protected horticulture. Potentiality and constraints of big data management and integration with "omics" disciplines will also be discussed.

show abstract

Section: Radiation Interceptionmentioning

confidence: 99%

Section: Radiation Reflectance Absorbance and Transmittancementioning

confidence: 99%

Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges

Tripodi¹,

Massa²,

Venezia³

et al. 2018

Agronomy

View full text Add to dashboard Cite

show abstract

“…In this experiment, the measurement area was scanned with intervals of 1 h or less, regardless of external lighting status and the increased scan frequency revealed some interesting developments. In imaging, longer term time-series studies have recently been carried out both in the field 61 and in the laboratory, 62 but they are inherently limited to light hours or when using external light sources. Additionally, measurement radiometry and its calibration are complicated.…”

Section: Temporal Variation Of Spectral Response In Target Point Cloudsmentioning

confidence: 99%

Artificial target detection with a hyperspectral LiDAR over 26-h measurement

et al. 2015

View full text Add to dashboard Cite

Abstract. Laser scanning systems that simultaneously measure multiple wavelength reflectances integrate the strengths of active spectral imaging and accurate range measuring. The Finnish Geodetic Institute hyperspectral lidar system is one of these. The system was tested in an outdoor experiment for detecting man-made targets from natural ones based on their spectral response. The targets were three camouflage nets with different structures and coloring. Their spectral responses were compared against those of a Silver birch (Betula pendula), Scots pine shoots (Pinus sylvestris L.), and a goat willow (Salix caprea). Responses from an aggregate clay block and a plastic chair were used as man-made comparison targets. The novelty component of the experiment was the 26-h-long measurement that covered both day and night times. The targets were classified with 80.9% overall accuracy in a dataset collected during dark. Reflectances of four wavelengths located around the 700 nm, the so-called red edge, were used as classification features. The addition of spatial aggregation within a 5-cm neighborhood improved the accuracy to 92.3%. Similar results were obtained using a set of four vegetation indices (78.9% and 91.0%, respectively). The temporal variation of vegetation classes was detected to differ from those in man-made classes.

show abstract

“…Dornbusch et al used structured light technique to scan barley plant for graphic 3D reconstruction [25]. Li et al used structured light systems to scan spatial-temporal point cloud data of growing plants, and successfully detected growth events such as budding and bifurcation [26].…”

Section: Introductionmentioning

confidence: 99%

3D Imaging of Greenhouse Plants with an Inexpensive Binocular Stereo Vision System

Tang

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Nowadays, 3D imaging of plants not only contributes to monitoring and managing plant growth, but is also becoming an essential part of high-throughput plant phenotyping. In this paper, an inexpensive (less than 70 USD) and portable platform with binocular stereo vision is established, which can be controlled by a laptop. In the stereo matching step, an efficient cost calculating measure-AD-Census-is integrated with the adaptive support-weight (ASW) approach to improve the ASW's performance on real plant images. In the quantitative assessment, our stereo algorithm reaches an average error rate of 6.63% on the Middlebury datasets, which is lower than the error rates of the original ASW approach and several other popular algorithms. The imaging experiments using the proposed stereo system are carried out in three different environments including an indoor lab, an open field with grass, and a multi-span glass greenhouse. Six types of greenhouse plants are used in experiments; half of them are ornamentals and the others are greenhouse crops. The imaging accuracy of the proposed method at different baseline settings is investigated, and the results show that the optimal length of the baseline (distance between the two cameras of the stereo system) is around 80 mm for reaching a good trade-off between the depth accuracy and the mismatch rate for a plant that is placed within 1 m of the cameras. Error analysis from both theoretical and experimental sides show that for an object that is approximately 800 mm away from the stereo platform, the measured depth error of a single point is no higher than 5 mm, which is tolerable considering the dimensions of greenhouse plants. By applying disparity refinement, the proposed methodology generates dense and accurate point clouds of crops in different environments including an indoor lab, an outdoor field, and a greenhouse. Our approach also shows invariance against changing illumination in a real greenhouse, as well as the capability of recovering 3D surfaces of highlighted leaf regions. The method not only works on a binocular stereo system, but is also potentially applicable to a SFM-MVS (structure-from-motion and multiple-view stereo) system or any multi-view imaging system that uses stereo matching.

show abstract

Analyzing growing plants from 4D point cloud data

Cited by 87 publications

References 55 publications

Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges

Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges

Artificial target detection with a hyperspectral LiDAR over 26-h measurement

3D Imaging of Greenhouse Plants with an Inexpensive Binocular Stereo Vision System

Contact Info

Product

Resources

About