Registration is even more complex when involving three-dimensional (3D) multispectral images of plants at close range. Unfortunately, the process of combining plant images acquired using multiple sensors, called 'registration,' can be notoriously complex. Different sensors are optimized for different frequency ranges and provide useful information about the plant. A major issue faced during image-sensing is the difficulty of combining data from the same plant gathered from multiple image sensors, also known as 'multispectral' or 'multimodal' imaging. However, some obstacles must be overcome before these visions become a reality. In the near future, farmers and scientists will be able to quantify various plant traits by simply pointing special imaging devices at plants. Modern cameras and sensors, together with image processing algorithms and artificial intelligence (AI), are ushering in a new era of precision agriculture and plant breeding. Here, di denotes the distance from the point to the light source, dv denotes the distance from the point to the camera optical center, θi denotes the angle between the direction vector of the incident light and the normal vector of the surface where the point is located, θv denotes the angle between the observation direction vector and the normal vector of the surface where the point is located, and θ denotes the angle between the projection vector of the incident light direction vector and the projection vector of the observation direction vector on the surface where the point is located. In this case, the plant and the reference at the tangent point share the same 3D light field features. (C) Simulation of the plant leaf and the hemisphere reference tangent to each other and the relevant 3D light field features. (B) Schematic diagram of the raw image acquisition system for the plant. (A) Schematic diagram of the raw image acquisition system for the hemisphere references.
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