Author Card:
R&D Software Engineer

Matthew Cassidy

An R&D engineer with expertise in 3D data processing for agricultural imaging. My background includes software development, AI module creation, and computer vision. My responsibilities primarily involve creating, maintaining, and enhancing AI-powered processing modules and algorithms to support the data processing team at Hiphen, contributing to technological advancements in the field of digital phenotyping.

In the agricultural imaging realm, working with multiple sensors at a time is often common since each sensor give access to a specific plant information, either related to the architectural, structural, biophysical, or reflectance properties of the plant. And interpreting the data collected from these sensors is required to compute what we usually call traits (i.e., plant assessments).

Now if we want to access more granular assessments, one way of doing it is to combine data from multiple sensors to superpose them and access more insightful and accurate information for agricultural research.

Enter the fascinating world of the pinhole camera model – a simplified yet powerful concept that forms the bedrock of computer vision. In this blog post, we'll explore the wonders of the pinhole camera model and its indispensable role in simulating the behavior of a camera for deeper granularity of phenotypic assessments in agricultural research and production.

At Hiphen we have experienced working with an array of cutting-edge industrial sensors, from RGB cameras to LiDAR, multispectral, thermal sensors and more, each providing valuable data to compute essential traits. Therefore, combining the data from these multiple sensors is crucial for gaining a comprehensive understanding of how plants are behaving in their environment. However, to achieve this, it is essential to be able to simulate and understand the geometric representation of a camera.


Understanding the Pinhole Camera Model

Early diagram of the pinhole camera model.


The pinhole camera model is a mathematical representation that describes how light from a three-dimensional scene interacts with an ideal pinhole camera to form a two-dimensional image. In this model, the camera aperture is represented as a point, and light travels in straight lines through the tiny hole before reaching the image sensor or film.


Key Elements of the Pinhole Camera Model

  1. Pinhole: The small aperture through which light enters the camera, acting as a lens to capture light rays from different points in the scene.
  2. Image Plane: The surface (film or image sensor) where the two-dimensional image is formed, located at a fixed distance behind the pinhole.
  3. Optical Axis: An imaginary line passing through the center of the aperture and perpendicular to the image plane, representing the path of light from the scene to the camera.
  4. Focal Length: The distance between the pinhole and the image plane, determining the field of view and size of the projected image.
  5. Perspective Projection: Light rays from each point in the scene travel in straight lines and converge at the pinhole, resulting in a 2D representation of the 3D scene with perspective distortion.


Data Fusion (from various sensors) To Access Deeper Granularity of Assessments


In agricultural imaging projects, researchers employ various sensors simultaneously to access diverse plant information. For example, RGB cameras provide valuable colour data, while 3D sensors allow for structural property assessments such as biovolume and height. By combining data from these sensors, researchers can achieve a more accurate and comprehensive understanding of plant traits. For instance, while LiDAR provides an excellent source of information for structural and architectural plant properties, it cannot provide data on leaf colour or temperature amongst other. However, by integrating LiDAR data with thermal data, researchers can precisely measure leaf temperature in a high-throughput fashion and with excellent repeatability. This level of data fusion enhances the accuracy and efficiency of plant trait assessments, revolutionizing digital phenotyping in agriculture and boosting agricultural research worldwide.


Digital phenotyping Data Fusion in practice. Leaf temperature can now be calculated precisely from imagery.


Putting the Pinhole Camera Model into Practice


To superimpose data from different sensors, it's crucial to understand the geometric representation of the camera and ensure perfect alignment. The pinhole camera model allows researchers to put the 2D scene, captured by a thermal camera for example, into perspective to merge it with a 3D point cloud data for instance. To apply this methodology to phenotyping applications, researchers need to consider the sensors' positions on a 3-dimensional plane in comparison to the plant or tree being measured. Validating the alignment is a crucial step and involves extracting and comparing points from each image to ensure accurate layering.


Diagram illustrating the accurate superposition of 3D (point cloud) data with 2D (Thermal) data.


The pinhole camera model has played a crucial role in digital phenotyping evolution, enabling plant phenotyping experts to combine data from different sensors to achieve a deeper granularity of plant trait assessment. By fusing information from various sensors like LiDAR and thermal cameras, researchers can gain a comprehensive understanding of plant health, stress tolerance and resilience, and understand genotypes behavior in their environment globally. It also gives access to traits assessments that we can’t imagine having access to before. Such traits and mathematical calculations can be easily implemented into Hiphen’s PhenoStation® for phenotyping in controlled conditions but could also be adapted to PhenoMobile® for field-focused phenotyping projects. So, with the latest advancements in data fusion and sensor technology, digital phenotyping is poised to lead the way in shaping tomorrow’s agriculture.


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Topic brought to you by Matthew Cassidy - R&D Engineer @Hiphen.