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AVIGNON, FRANCE (January 6, 2025).
Hiphen and Aurea Imaging, two trailblazers in remote sensing, computer vision, and artificial intelligence for agriculture, have announced a major development: Hiphen is acquiring Aurea Imaging’s digital phenotyping activities. This strategic acquisition solidifies Hiphen’s status as the leading provider of advanced drone image analytics and predictive solutions for crop science organizations and agricultural enterprises worldwide.
Founded in 2014 and headquartered in Avignon, France, Hiphen has been delivering high-value data to seed breeders, product developers, food processors, vertical farms, and crop researchers across the globe. With expertise in image analysis and integrated solution systems, Hiphen has earned its reputation as a trusted partner in the agricultural industry. In 2023, Hiphen expanded its reach to the U.S. market with the acquisition of California-based SlantRange. Now, with the acquisition of Aurea Imaging’s phenotyping activities, Hiphen strengthens its position in Europe, a region renowned for its early adoption of digital plant phenotyping and innovative plant research ecosystem.
“The market for drone-based phenotyping in plant breeding and product development is evolving rapidly, and consolidation is essential to drive innovation and deliver maximum value to our clients,” said Dr. Alexis Comar, CEO of Hiphen. “By integrating Aurea’s resources and expertise, we can lower costs, accelerate trait and algorithm development, and deliver actionable insights faster, enabling optimal decision-making during the growing season. This acquisition reflects our commitment to advancing agricultural research globally, ensuring our clients gain deeper insights into their crops and extract greater value from their research efforts.”
On the other side, this divestment enables Aurea Imaging to focus on its orchard management strategy for fruit growers. Aurea has developed TreeScout, a hardware-software solution that provides tree-level insights to optimize orchard management by reducing inputs and enhancing yield.
“Since our founding, Aurea has been privileged to collaborate with leading seed breeders worldwide. While this divestment allows us to accelerate the roll-out of TreeScout, we are pleased that our customers will benefit from Hiphen’s state-of-the-art drone phenotyping platform and analytics to further their research initiatives,” said Joost Hazelhoff, CEO of Aurea Imaging. “We are committed to a robust handover process to ensure a seamless transition for our customers.”
Under this transition, Aurea's clients will benefit from Hiphen’s expertise through the Cloverfield Data Platform under the PhenoScale® product line, ensuring continuity and supporting their research endeavors. Both companies are confident this acquisition will spur the development of advanced tools to precisely measure and predict plant growth dynamics in response to genetics, management practices, and environmental factors.
By leveraging cutting-edge remote sensing and data science methods, Hiphen aims to enhance global agricultural efficiency, fostering a resilient and sustainable future for crop research and food production.
New features have been added to Cloverfield™, your plant phenotyping online platform. Embark on a smoother plant breeding adventure with Hiphen's latest addition: Dynamic Filtering.
Optimize your breeding scheme by effortlessly filtering your genetic material in a few clicks within Cloverfield™.
Interactively combine traits and set thresholds to filter out under-performing genotypes, or pinpoint the top-performing material you want to advance to the next stage.
Define more precisely your analytics profile. Specify more precisely the experiments, modalities and layers that you which to study and/or compare. You can create multiple profiles for the same trial site.
One step further in making your digital phenotyping experience frictionless with Hiphen, you're now able to make a detailed analysis of your data directly in Cloverfield™. You can easily combine traits and set for each one thresholding boundaries outside of which you wish to eliminate genetic material. For instance, one could set independent min-max thresholds for traits like plant biovolume, flower cover, stay green and lodging and see dynamically what share of the trial genetic material he/she is eliminating. This functionality will take you ever closer to reaching your ideotypes more efficiently than ever before!
User value: Apply multiple criteria filtering on your genetic material and dynamically eliminate under-performing genotypes based on your ideotype recipe.
Use Cases:
We invite you to explore the new dynamic filtering feature with the online INTERACTIVE TOUR. And please reach out to your Hiphen Campaign Manager should you have any question.
Stay tuned for the next Cloverfield™ features release update next month.
Sincerely,
Your Hiphen Success Team.
We are committed to meeting the evolving needs of crop researchers, and reimagined the way we articulate our Cloverfield™ Glossary with this this purpose in mind. This new intuitive format helps to parse the 70+ phenotypic traits we process routinely every year. Traits are grouped in explicit agronomic themes, with definitions and visuals to be transparent with regards to methodology. The new version places a strong emphasis on five distinct trait categories:
These categories have been carefully curated to cater to the unique needs of agronomic researchers, providing you with a more precise and comprehensive understanding of phenotypic measures available off-the-shelf for your crops.
With this new version, we are also unveiling our revamped interface for an enhanced user experience. Dive into an enriched journey of knowledge with our detailed visualizations and graphs, providing transparency into our advanced traits processing methodology.
Additionally, a powerful search functionality is now at your disposal to easily navigate the content of the glossary. Try it now!
Feel free to share this open online resource, establishing the new standards in digital phenotyping methodology for Ag research, with your fellow researchers to foster discussions within the scientific community. Moreover, we invite you to regularly check out this resource to keep up to date with the latest traits available. We eagerly anticipate collecting your feedback on this new traits' glossary. Your insights are valuable as we continue to advance in the field of agricultural research.
Cloverfield's Glossary is designed to offer definitions for the traits computed by HIPHEN. Alexis Comar, PhD describes a phenotypic trait as: "A measurable characteristic of the plant or canopy. It is the result of combining raw data obtained from sensors such as cameras, lidar, spectrometers, etc., with an analytical method for interpretation. This process serves as the means to transform raw sensor data into meaningful metrics for agronomists."
We've shared previouosly on our blog a post about what is a phenotypic trait – Read it now.
Hiphen's roots are deep into crop science, which truly sets us apart in the digital phenotyping realm. We gather a decade of experience and an unwavering commitment to agricultural research to ensure that our solutions align with your objectives.
The new and improved Cloverfield Glossary is a testament to our dedication to empowering you with the tools you need to make informed decisions and optimize your agricultural practices.
We invite you to explore the new traits' glossary and ask your Hiphen Campaign Manager should you have any question.
Stay tuned for the next Cloverfield™ features release update next April.
Sincerely,
Your Hiphen Success Team.
As you are aware, Slantrange was acquired by Hiphen last May, and since then, our primary focus has been on ensuring a seamless transition for Slantrange clients to Hiphen, providing the best possible experience throughout the process.
With this transition in mind, we would like to inform you that the Slantview data platform will be discontinued. Instead, all data processing action and results will now be delivered through Cloverfield™, Hiphen's homegrown data platform. This decision has been made in the interest of practicality and to offer our clients the best user experience.**
The merging of Slantrange and Hiphen platforms has been a comprehensive process, allowing us to draw on the strengths of both. The insights gained from this integration have been instrumental in enhancing Cloverfield, making it an all-in-one online platform that you can trust for your phenotyping needs.
We understand that change can be challenging, but we believe that this transition will bring the best of both worlds to our clients. The improvements made to Cloverfield™, based on the lessons learned from the comparison of both platforms, will undoubtedly elevate your phenotyping experience.
This decision also aligns with our upcoming strategic developments at Hiphen. We are committed to maximizing your phenotyping experience by implementing improvements in internal tools, enhancing interconnectivity, ensuring transparency, and prioritizing data security. These developments are part of our broader strategy to provide you with a cutting-edge platform that meets and exceeds your expectations.
We appreciate your understanding and cooperation during this transition. If you have any questions or concerns, please do not hesitate to reach out to our Success Team using the button below.
We are dedicated to making this transition as smooth as possible for you. Thank you for your trust in Hiphen.
Your Hiphen Success Team.
Contact Success Team
Cloverfield v2.8.0 – October 04th, 2023
New features have recently been added to Cloverfield™, your plant phenotyping online platform, and in this blog post we are going to take a deeper dive into those 6 new features:
You can now autonomously manage the creation of new trial sites directly through Cloverfield™. Add a new location or change its name without having to ask your Hiphen Campaign Manager. Only accessible for users with 'Admin' rights assigned to the contract.
💎 User value: Save time and be autonomous with your campaign management.
⚙️ Use Cases:
Clicking on a plot in the map view will give you access to a new window that displays next to each other the plot images from each flight. You can now efficiently visualize a single plot’s evolution through time in one click.
The new window also provides the trait results computed for the selected plot at the selected date. Finally, a new spider chart helps to efficiently assess how the plot performed versus the trial’s mean.
💎 User value: Access plot information and history at once for faster inspection and decision-making during the campaign.
⚙️ Use Cases:
For future multispectral flights, RGB plot clips will be generated from the orthomosaic and posted into the analytics tool. Just as it is the case for RGB flights, you’ll then be able to inspect multispectral plot imagery in more details directly from Cloverfield™.
**💎 User value:**Check the raw image of your plot acquired with a MS sensor.
⚙️ Use Cases:
Download graphs displayed in the Analytics tool and any plot clips to insert them into your presentations and internal communication. The file format will be an image (.webp) which will following the following file naming convention: site name_date_plot name.webp.
**💎 User value:**Boost your internal communication about the results of your trial programs.
⚙️ Use Cases:
We have refreshed the design of the monitoring tool that keeps you informed of the data processing progress for each flight you upload. The page ergonomics has been improved and we added new filters based on your feedback. You can now group specific filters to get a more granular and custom progress tracking update. Filters include campaign, region, country, crop and sensor type.
You can also filter flags and comments posted by your Hiphen Campaign Manager in a more intuitive manner. This allows you to very efficiently focus on datasets that require your attention.
💎 User value: Get all the transparency you need about data processing progress, pinpoint flights that require attention and use this tracking tool for your own internal reporting.
⚙️ Use Cases:
In the map and analytics pages, at the top right corner of the map, enter a specific plot name in the search bar to pinpoint its position on the plot map and select it.
Once the search bar is used, we will automatically suggest you to add the plot of interest to the 'plot exploration' section of the analytics page.
**💎 User value:**Save time exploring specific plots of interest.
⚙️ Use Cases:
For more information, reach out to your Hiphen Campaign Manager. Stay tuned for the next Cloverfield™ features release update next month.
Sincerely, Your Hiphen Success Team.
In modern agriculture, the utilization of advanced technologies has transformed the way researchers and producers monitor and manage their crops. Drones and satellites have emerged as two powerful devices, offering numerous advantages to agri-businesses, ranging from territory surveillance to mapping and crop health assessment.
Aerial imagery collected through these systems provides valuable insights by capturing data from the sky, enabling reserachers and farmers to make informed decisions and optimize their agricultural production. In the last decades, drone and satellite imagery has brought about significant improvements in large-scale crop monitoring across the agricultural sector.
In this post, we will explore and contrast the use of drone and satellite image analytics for monitoring agricultural production, highlighting their respective strengths and differences, and showing how they can combine together.
Drone and Sentinel 2 Satellite flying above agricultural land.
Drones, on the first hand, also known as Unmanned Aerial Vehicles (UAVs), are small remote-controlled aircraft equipped with high-resolution cameras and other sensors like multispectral cameras or 3D sensor (LiDAR). They can fly at various altitudes and capture detailed imagery of agricultural fields and research trials. Drones offer flexibility and precision, as they can be deployed on-demand and they can fly over specific areas of interest in a fairly short period of time thanks to their portability and lightness.
Table comparing the imaging specs of each system for phenotyping applications.
Satellites, on the other hand, are orbiting spacecraft that continuously capture images of the Earth's surface. Satellites are equipped with powerful sensors too, even if they usually are compatible with fewer sensor types (no 3D sensors yet for instance). The high-resolution equipment enable satellites to provide wide-scale coverage and monitor large agricultural regions from high altitudes. Satellites offer the advantage of regular and systematic data acquisition, allowing for consistent monitoring of crops over time.
Drone and satellite imagery has brought about significant improvements in large-scale crop monitoring across the agricultural sector; however, they differ in several key aspects:
These devices are different in their specifications but also in the assessment that they can give access to. Indeed, the sensors from a Satellite cannot capture the same thing as a Drone camera would since the proximity with the canopy and the sensor resolution are not the same, and in digital phenotyping, those parameters prevail while looking for research-grade plant assessments with deep granularity (like organ counting and classification or disease assessments for instance). But there are some traits that both devices can capture, like NDVI for example which is a vegetation index useful to estimate Vigor and Biomass, and in that case, creating data fusion with both devices and sensors helps accessing precise assessments that will give insights at different scales such as the field scale, the plot scale and even the plant scale when using high-resolution sensors.
NDVI assessment from Satellite and Drone at different scales, giving access to various granularity of information.
You can browse your plant traits values easily and intuitively in Hiphen's Cloverfield™.
In conclusion, drones and satellites have become indispensable tools for monitoring agricultural production. While drones excel in providing high-resolution, near real-time data for small to large plot trials assessments, satellites offer broader coverage and long-term monitoring capabilities for large-scale agricultural regions. Both options have unique value for plant phenotyping and as usual, the way you should pick either one or the other or both, should be driven by the assessments and outputs you wish to get, and by your field trial and project specifications as mentioned in the text above. By leveraging the strengths of both drone and satellite image analytics, agricultural researchers can make improved data-driven decisions, based on valuable phenotypic data and analytics, to optimize crop management practices and improve seed breeding and agricultural production globally.
Grab a time from one of our experts' Calendar to discover more.
Sincerely,
Your Hiphen Team.
Topic delivered by Patrizia ZAMBERLETTI – Imaging Solutions Specialist @Hiphen.
In the digital agriculture realm, generating accurate plot patterns of your field trial is crucial for optimal crop monitoring to understand plant dynamics through time. To achieve this, the accurate and precise mapping of your plots, also known as plot mapping or parcellaire generation, plays a pivotal role. In recent years, the integration of deep learning techniques has revolutionized the process, enabled automated plot map generation, and offered a myriad of benefits for agricultural research and production. In this blog post, we will explore the importance of plot mapping, the advantages of using deep learning for automation, and how it impacts the agricultural ecosystem.
Traditionally, creating plot maps was a labor-intensive task involving manual measurement and mapping techniques. However, with the advent of deep learning algorithms, this process has been significantly streamlined. Deep learning models can automatically segment and delineate agricultural land into distinct plots, saving valuable time and effort for farmers, agronomists, and researchers. By automating this process, researchers can allocate their resources more efficiently and focus on other critical aspects of field trial management.
Big plots trial – Automating plot mapping with digital tools is highly helpful.
Small plots trial – Plot map still could be done manually but automation make it even more easy.
One of the remarkable advantages of using deep learning algorithms for plot map generation is the ability to achieve higher precision compared to manual mapping methods. Deep learning models can analyze aerial or satellite imagery, topographic data, and other relevant information to accurately identify plot boundaries.
Example of automated plot mapping of salad field from UAV imagery.
Accurate plot maps generated through deep learning algorithms have a direct impact on the processing of phenotypic data collected for each plot. Phenotypic data is the result of plant assessments like canopy development, stress and disease resilience, yield predictions and more, is essential for agricultural research and breeding programs. By having precise plot boundaries, researchers can associate specific phenotypic data with corresponding plots, enabling more accurate analysis and interpretation of plant assessment distribution within the entire field trial. This granular information facilitates the identification of patterns, trends, and correlations, ultimately leading to informed decision-making for crop research and production.
To achieve reliable and versatile deep learning models for plot map generation, robust training datasets are paramount. High-quality datasets that encompass diverse geographical regions, crop types, and plot patterns variations and orientation are essential for training models to identify plot boundaries effectively within images captured in various environments. This emphasizes the need for collaboration and data sharing within the agricultural community to develop comprehensive datasets that can improve the accuracy and applicability of deep learning models for plot map generation.
Set of images of plots in various conditions used for training DL models.
At Hiphen, we specialize in developing digital plant assessment solutions for agricultural research and production. We understand the significance of plot map generation and the transformative potential of deep learning in the agricultural sector. Our expertise lies in leveraging cutting-edge technologies to empower our clients with accurate and automated plot mapping alongside data acquisition and processing, enabling them to make better decisions and adapt to ever-changing environmental conditions. Through our tailored AI solutions, we strive to revolutionize the agriculture industry, promote sustainable practices, and enhance crop researchers productivity.
View of Hiphen's Cloverfield™ Data Platform.
Plot map generation holds immense importance for agricultural research and production. By utilizing deep learning algorithms, we can automate the process, saving time and improving precision. Accurate plot maps enable efficient resource allocation and precise data processing. However, it is vital to build robust training datasets to ensure the reliability and versatility of deep learning models. With Hiphen's AI methodologies, the agricultural industry can embrace innovation, make informed decisions, and adapt to the evolving needs.
Sincerely,
Your Hiphen Team.
Topic brought to you by Rhianna MCANENY – Image Processing Specialist @Hiphen.
In the realm of phenotyping and agricultural research, image analytics has emerged as a powerful tool for extracting valuable insights. However, beneath the surface lies a crucial element that often goes unnoticed but plays a pivotal role in ensuring accurate analysis and interpretation georeferencing plots. Georeferencing involves assigning geographic coordinates to specific locations within field trials, enabling the reconstruction of Orthomosaic and facilitating precise data processing. In this blog post, we will explore the significance of georeferencing plots and shed light on the importance of coordinate systems, file formats, and advancements in technology for harnessing the full potential of image analytics in phenotyping.
Contrary to popular belief, the Earth is not a perfect sphere. It is slightly flattened at the poles and bulged at the equator, resulting in an irregular shape known as the geoid. Recognizing this, coordinate systems have been designed to account for the specific geodetic deformations in different regions, minimizing errors in positioning and representation. By aligning our data with accurate coordinate systems, we can ensure precise georeferencing and eliminate discrepancies that may arise during image analysis.
Geoid representing the Earth
Georeferencing plots within field trials allow to reconstruct Orthomosaic, a bird's-eye view of the field. An Orthomosaic comprises an RGB image superimposed with the associated plot map, which delineates the division of plots within the entire field trial. With a well-constructed Orthomosaic, we can accurately process data and deliver results through interactive and visual maps that showcase calculated traits for each individual plot. This approach enhances the clarity and interpretability of the obtained data, enabling data-driven decision-making. To ensure optimal plot patterns reconstruction we generally use Ground Control Points (GCP) that are positioned in the field and help identifying the orientation of the field, the origin of plot X1Y1 and make good alignments during the photogrammetry process.
Simplified process of creconstructing the orthomosaic of a field trial with GCPs.
Multiple coordinate systems exist to represent and locate points on the Earth's surface. Two widely used systems are WGS 84 (World Geodetic System 1984) and UTM (Universal Transverse Mercator). WGS 84 is a geodetic coordinate system that provides geographic coordinates—latitude, longitude, and altitude—based on a mathematical model describing the Earth's shape. UTM, on the other hand, is a projected coordinate system that divides the Earth into zones, allowing for more precise measurements within each zone. The choice of coordinate system depends on the specific requirements of the research, but in our experience, UTM coordinate systems have proven to be more efficient and precise as we can see with the example below:
The importance of choosing the right coordinate system: on the left, the length of the plot is 10.79m, while on the right, it is 8.81m.
In addition to coordinate systems, the file format used for storing GPS coordinates is crucial for preserving geographic information. At Hiphen, we recommend specific formats such as shapefile, GeoJSON, geopackage, and KML. These formats ensure that the geographic information contained in the data is preserved and accessible during analysis. Choosing alternative formats like CSV can lead to the loss of vital information regarding the coordinate system used during data acquisition, hindering proper interpretation and analysis.
Thanks to advancements in drone, robotics, and GPS technologies, collecting GPS data has become increasingly automated and efficient. Metadata recorded directly in each image simplifies the process of gathering accurate georeferencing information. By leveraging these technological improvements, researchers can streamline their data collection processes and enhance the accuracy and reliability of plant assessments. This is mostly possible currently thanks to RTK technology. RTK means Real Time Kinematic, and this geolocation technology allows a centimeter-level positioning of the measured object, with real-time synchronization. Such technology is now implemented as standard in most of the latest generation of portable drones with GPS modules mounted on top of the devices, but at Hiphen we also use it as an independent module mounted on a stick, for georeferencing GCPs position, or even mounted on our autonomous PhenoMobile robots.
Field operator collecting GPS coordinates of ground control points with RTK precision device.
At Hiphen, we understand the importance of research-grade data collection and processing. Our experts work closely with researchers to define the best protocols for ensuring high-quality data collection. We navigate through various projected coordinate systems, adapting to the location of the field, to ensure the measurements of plots align accurately with real-world observations. By partnering with Hiphen, researchers can leverage our expertise in georeferencing plots and unlock the full potential of image analytics for phenotyping.
Georeferencing plots for image analytics is an essential but often overlooked aspect of phenotyping. The process of assigning geographic coordinates to field trial locations enables the reconstruction of Orthomosaic, leading to accurate data processing and visualization. Choosing appropriate coordinate systems, file formats, and harnessing advancements in technology empowers researchers to extract meaningful insights from their data. Collaborating with experts like Hiphen ensures that research-grade data collection and processing are achieved, ultimately advancing agricultural research, and propelling the field of phenotyping to new frontiers.
Sincerely,
Your Hiphen Team.
Topic brought to you by Adrien Vielix – Field Acquisition Manager @Hiphen.
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.
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:
RGB image of plots with emergence issues.
Segmented and annotated RGB image of wheat heads.
Thermal assessment of trees in greenhouse – Here we can see Data fusion in action combining RGB imagery with thermal to estimate precisely leaf temperature.
3D sensing equipment helps accessing information about crops at all plant levels.
NDVI image captured from UAV.
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.
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.
Sincerely,
Your Hiphen Team.
Topic brought to you by Marc LABADIE – Project Leader at Hiphen.
AVIGNON, FRANCE (June 5, 2023).
Hiphen and SlantRange, two pioneers in the application of remote sensing, computer vision, and artificial intelligence for agriculture, today announced they are joining forces. Hiphen is acquiring SlantRange to form the leading source of advanced crop measurement and prediction solutions to crop science companies and agricultural enterprises worldwide.
Hiphen SA, of Avignon, France, has been delivering high value data to seed breeders, product developers, food processors, vertical farms, and crop researchers across the globe since 2014. Its core competencies in image analysis and solution systems have positioned it as a trusted partner in the industry. However, it only started exploring the North American market in 2020.
"We are excited about the synergy created by SlantRange's extensive patent portfolio and growing US customer base, which perfectly complements Hiphen's analytics capability and strong European presence," stated Alexis Comar, CEO of Hiphen. "This merger will provide us with a broader capability to make impactful analytical tools accessible to the global agriculture market."
Hiphen and Slantrange are joining forces to deliver insightful drone and satellite data to plant breeders in North America.
SlantRange, Inc., of San Diego, California, USA, has built a significant presence in the North American crop input market through its innovative methods for quantifying and predicting crop performance. It brings an extensive patent portfolio, deep engineering and data sciences skill base, and industry relationships to the partnership.
Michael Ritter, CEO of SlantRange, added, "We’re thrilled to complement Hiphen’s capabilities with our own. Through this combination we’ll be able to deliver an expanded, more responsive service portfolio across a broader global footprint."
The combined company will operate globally under the Hiphen brand and will accelerate new tools to market that accurately and efficiently quantify and predict plant development in response to management, genetics, and environment. The company aims for its remote sensing and data science methods to underpin future improvements to global food production efficiency and sustainability.
"We are committed to supplying agricultural researchers and producers with innovative solutions to produce more food, feed, fuel, and fiber on fewer acres to meet the demands of the 21st century," emphasized Hiphen’s business development director Lee West.
Media Contacts:
Lee West
lwest@hiphen-plant.com
+1 540 309 3353