A Brief History of The Emergence Of Digital Phenotyping
Plant phenotyping is a brick in the wall of agricultural research and plant breeding. It helps understanding how a plant is behaving in its environment, by assessing key information related to plant morphology, physiology, biochemistry, yield, and responses to biotic and abiotic stresses. Having access to such information enables us to understand plant dynamics to be able to predict how a variety will perform and produce in a specific environment. Phenotypic assessments, which are accessible by combining Genotypic information x Environment data, are mostly used to improve varietal selection and make it completer and more precise than the genomic approach, which relies on on the study of gene presence and performance through genotypic assessments.
Digital phenotyping is quite a new realm, companies like Hiphen were founded less than 10 years ago. Thus, such technology needs some time to be well-adopted and improved to unleash its full potential. Nonetheless plant phenotyping by itself is not something new, it has existed for ages since agronomists got eyes and can measure plants manually. But most of those manual techniques are either often destructive or time-consuming. However, the rising of digital phenotyping by leveraging new technologies has seen the use of sensors and imaging platforms emerging as a fast and efficient approach to quantitatively assess plant characteristics i.e., phenotypes, in a non-destructive way. And at Hiphen we believe that the use of those sensors to create data fusion enables more accurate and repeatable assessments, in a high-throughput fashion, based on the theory of the 6 dimensions of phenotyping.
Different imaging platforms can be used for field and indoor phenotyping to process data about those 6 dimensions, like Hiphen’s Cloverfield, and those platforms are using data collected from various sensors. Commonly used imaging technologies include visible light imaging, thermal imaging, 3D imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging. So, let’s take a deeper dive into the sensor technologies accessible for agricultural imaging nowadays.
Imaging Platforms and Sensor Technologies for Plant Phenotyping
Several imaging platforms are utilized for both field-focused and indoor plant phenotyping. These platforms gather data from various sensor types to obtain a comprehensive understanding of plant traits. Some of the commonly used imaging techniques include:
- Visible Light Imaging: Visible light sensors detect light within a spectrum of wavelengths from 400 to 700 nm and provide values for red, green, and blue (RGB) colors. High-resolution RGB images allow accurate measurements of plant biomass, root architecture, growth rate, germination rate, yield, disease detection, and quantification of abiotic stress. While this method is cost-effective and easy to access, slight color variations between the plant and its background, as well as lighting conditions, can affect automated image processing.
- Thermal Imaging: Thermal infrared imaging enables the visualization and distribution of infrared radiation on plant surfaces. A thermal camera converts the emitted infrared radiation (heat) from the plant into visible images that display the spatial temperature distribution. This imaging technique is used to assess the physiological state of the plant in response to biotic and abiotic stresses, such as canopy or leaf temperature, transpiration, stomatal conductance, and overall plant water status. It also finds applications in precision agriculture for water management and irrigation.
- 3D Imaging (like LiDAR technology) : LiDAR or LADAR, an acronym of "light detection and ranging" or "laser imaging, detection, and ranging" is a method for determining ranges by targeting an object or a surface with a laser and measuring the time for the reflected light to return to the receiver. LIDAR can operate in a fixed direction (e.g., vertical) or it may scan multiple directions, in which case it is known as LIDAR scanning or 3D laser scanning. LIDAR has terrestrial, airborne, and mobile applications and is thus compatible with most phenotyping applications and systems. It is most suitable for seeking structural and architectural information like plant height and biovolume but also enables to access counting and classification assessments by applying specific color maps on the 3D dense clouds to identify plant organs.
- Multispectral/Hyperspectral Imaging: Multispectral/hyperspectral imaging captures electromagnetic spectra (λ) and spatial data (x, y) at each pixel of an image, reconstructing a 3D data matrix known as a hypercube. The hypercube contains thousands of images within the spectral range of 250 to 2500 nm, including UV, visible, near infrared, and mid-infrared regions. This approach provides an abundance of information, enabling the extraction of a wide range of phenotypic traits, such as nutrient content estimation, disease detection, fruit maturity and ripening, and other physiological and biochemical traits related to plant growth, development, and yield.
- Fluorescence Imaging: Fluorescence imaging involves measuring the light energy emitted when the plant absorbs shorter wavelength radiation, mainly through the chlorophyll complex. The emitted fluorescence is a tiny fraction (<3%) of the total radiation emitted by the light source to the object. The amount of re-emitted light (fluorescence) is a reliable indicator of the plant's ability to use the absorbed light and is used to estimate the overall health status of the plant. Fluorescence imaging is utilized to estimate photosynthetic efficiency and other associated metabolic processes affected by biotic and abiotic stresses. However, this technique does not specify the cause of variations in the plant signal, such as light, temperature, or other environmental factors.
- Tomographic Imaging: Other imaging techniques, such as Magnetic Resonance Imaging (MRI), X-ray Computed Tomography (CT), and Positron Emission Tomography (PET), provide high-resolution 3D images of single plants or plant parts. MRI captures 3D images of internal structures, allowing non-invasive quantification of static and dynamic traits, such as structural, biochemical, and temporal changes inside the plant. X-ray CT visualizes the 3D structures of both internal and external plant features at the micro or macro level. These imaging techniques are time-consuming and not suitable for processing substantial amounts of data. Additionally, their large size and weight prevent their use on aerial imaging platforms.
Choosing the Right Sensors for Non-destructive Phenotypic Traits Assessments
The selection of sensor technologies depends on the specific assessments’ researchers aim to perform. Companies like Hiphen are proficient in guiding researchers in identifying the most appropriate sensors and traits for their needs. Data-driven decisions and insightful research based on phenotypic data assessments are key to enhancing agricultural practices, improving crop yields, and ensuring food security for the growing global population.
In conclusion, imaging technologies for plant phenotyping have made significant strides in recent years, and their integration with digital phenotyping has brought new opportunities for advancing agricultural research and plant breeding. As these technologies continue to evolve, we can expect even more sophisticated and efficient ways to understand plant dynamics and harness their full potential to address the challenges of modern agriculture. The future of plant phenotyping is promising, and it holds the key to sustainable and efficient food production for the years to come.
Your Hiphen Team.
Topic brought to you by Marc LABADIE - Project Leader at Hiphen.