Unlocking New Possibilities in Crop Research
Literal for
Biomass & Yield
Advanced phenotyping data about your trials’ varieties to identify the most subtle differences in plant performance between genotypes – Helping you link phenotypic traits to genetic markers, to speed up breeding cycles.
Literal for
Deficiencies & Plant Health
Standardized phenotyping information about your trial modalities to evaluate plant response to multiple stresses and build strong dose response curves, with greater precision than manual methods.
Use Cases For Field Experimentation
Use cases for Plant Breeding
Assessing the % of green plant pixels through multiple traits, providing a valuable indicator about crop deficiencies while breeding for stress resilience.
For plant breeders, resilience begins with observation — the ability to detect how each genotype responds to stress before yield or growth are visibly affected. But traditional health assessments rely on subjective scoring and late-stage symptoms, making it difficult to capture subtle yet critical differences among varieties.
Using AI-powered imagery and digital phenotyping, Literal quantifies the percentage of green plant pixels in each image across all your experimental plots. Through a set of multiple digital traits, it delivers a reliable indicator for crop deficiences or stress intensity — detecting early signs of decline and distinguishing them from advanced symptoms with unmatched repeatability. And this is just the beginning since this approach is paving the way to real and tailor-made disease quantification digital traits. Hiphen keeps on supporting research projects worldwide to accelerate the developments of these high-value traits for plant breeders.
In the meantime, these objective digital traits already enable you to rank genotypes by their physiological resilience to help identify genetic markers linked to key traits driving your breeding program.
Beyond the measurements, Literal’s centralized analytics platform gathers imagery, traits, and analytics, empowering breeders to make data-driven decisions with confidence.
Segmenting and assessing the % of healthy organs (leaves & heads) for rice and wheat through RGB & NIR imagery to breed for stress resilience.
Pushing the boundaries of plant health assessments.
When breeding for stress resilience, every detail matters. Measuring plant health is essential — but understanding the health of each organ takes phenotyping to a whole new level. With Literal, breeders can now segment and assess the percentage of healthy organs, such as heads and leaves, in crops like rice and wheat, using RGB and NIR imagery enhanced by deep learning detection models.
This approach reveals what the human eye can’t: subtle variations in color, texture, and vigor that distinguish early symptoms from advanced stress responses on each organs of each plant within each image. By combining multiple digital traits — from global plant health to fine-grained organ color — breeders gain a multidimensional view of resilience, performance, and genetic potential of their varieties.
Through these digital assessments, each organ becomes a data point. The result? Objective and quantifiable insights that help identify stress-tolerant genotypes with greater precision and confidence than current methods.
Backed by cutting-edge research, including the paper published by Riley et al. and other global collaborations with research initiatives worldwide, like the Global Wheat Dataset, Hiphen continues to build robust datasets and Deep Learning models to support the next generation of breeding applications, not only on cereals but also on other field crops and vegetables.
Estimating yield on cereal crops through organ density digital trait, to rank genotypes and improve selection based on yield potential.
Turn yield estimation into genetic insight.
For plant breeders, yield is the ultimate measure of success — the trait that brings research to life in the field and seeds to market for companies. But measuring yield potential in experimental plots is no simple task. Manual organ counting is tedious, time-consuming, often variable and thus rarely scalable to the hundreds of genotypes and trial locations that must be evaluated each season. Literal offers a new path forward.
Through advanced imaging and AI-driven analysis, Literal enables breeders to estimate yield potential non-destructively by measuring organ density — a digital trait directly linked to the number and distribution of yield components such as ears in wheat, but it is also transferable to pods in canola for instance, or heads in sunflower, to some extent.
The wheat head density trait has been validated against ground-truth data to ensure repeatability across multiple experiment configurations, making it reliable and research-ready for cereal breeders. Integrated into your breeding analysis, it helps you rank genotypes objectively, identify genetic markers linked to yield, and strengthen your selection decisions with confidence.
Beyond the measurements, Literal connects you to a centralized phenotyping analytics platform, seamlessly compatible with your breeding software — unifying data, images, and insights in one place.
This use case has been produced from Arvalis data collected across multiple research stations.
Estimating Biomass through plant height, biovolume and green cover traits, to rank genotypes and improve selection based on biomass production.
Quantify biomass, boost genetic gain with Literal.
In modern breeding programs, understanding biomass production is fundamental. It’s not just a measure of growth — it’s a window into plant vigor, resource efficiency, and ultimately, yield potential. Yet, measuring biomass precisely in the field remains one of the toughest challenges for breeders. Manual methods are slow, destructive, and difficult to scale across hundreds or thousands of plots. Literal changes that.
With Literal, you can now quantify biomass-related traits accurately, consistently, and non-destructively through advanced digital phenotyping. Using standardized imaging protocols and AI-powered analysis, Literal delivers key digital traits such as Plant Height, Plant Green Cover, but also Biovolume, LAI and Ear Volume (for wheat) that are under development. Each trait can be validated with ground-truth measurements to ensure reliability and research-grade precision at scale.
These traits integrate seamlessly into your breeding workflows, helping you sort, rank, and select genotypes based on objective, reproducible data. More importantly, they support the identification of genetic markers associated with key traits, strengthening your predictive breeding models.
And beyond the measurements, Literal connects you to a centralized phenotyping analytics platform, compatible with your existing information systems — empowering faster, data-driven selection decisions.
This use case has been produced from Arvalis data collected across multiple research stations, and from the work of Jimenez-Berni et al., 2018.
Use cases for Agro-inputs Testing
Wheat head density digital trait, measured with Literal, for plant protection product certification in regulatory trials.
In agricultural innovation, precision means everything. When developing a new fertilizer or crop nutrition product, proof of performance is non-negotiable. And yet, one of the simplest metrics — wheat head density — has long been one of the hardest to measure. Manual counting is slow, subjective, and nearly impossible to scale. Literal by Hiphen changes that.
With advanced imaging and AI-powered analytics, Literal captures every plot in high-resolution detail, turning images into science-grade measurements. Among its validated digital traits, wheat head density delivers accuracy that’s fully aligned with the latest EPPO guidelines — generating over 60 data points per trial, across 3+ trial sites, with a correlation of 0.85 and in the end the value is that it’s three times more repeatable than manual assessments.
The result? Stronger regulatory dossiers. Clearer product differentiation. All backed by real images and traceable data that regulators and scientists can trust. Literal transforms tedious counting into objective evidence — helping you certify your product’s impact with precision, confidence, and scientific integrity.
With Literal, you can:
- Extract multiple traits simultaneously;
- Access more accurate and repeatable measurements than manual methods;
- Apply a standardized methodology across all your trials;
- Use advanced analytics to detect differences between treatments in just a few clicks;
- Rely on non-destructive, evolutive, and on-demand digital traits;
- Visualize tangible images highlighting product impact for certification reports.
Note: Hiphen validated the Wheat Head Density digital trait through ground-truth comparisons during a global testing campaign in 2025. The same validation framework can be applied to other crops and also to other traits, including all currently available off-the-shelf traits presented below, to support product certification in regulatory trials.
Multi-environment analysis with high assessment repeatability for crop nutrition performance evaluation.
Proving that a crop nutrition product works isn’t just about one trial, one variety, or one environment. It’s about demonstrating consistent performance — across genetics, soils, climates, and management practices. Because in agriculture, impact is never shaped by a single factor, but by the complex interaction of Genetics × Environment × Management × Product. That’s why multi-environment testing is essential. It’s how you build regulatory confidence, scientific credibility, and market trust.
With Literal by Hiphen, this process becomes simpler, standardized, and scalable. Literal brings every research site, every partner, and every dataset together under one unified methodology. Through advanced imaging and AI-driven analytics, you can measure plant performance precisely, compare results across locations, and prove that your product delivers — anywhere it’s tested.
The platform connects the full value chain: image-based traits, ground-truth correlations, built-in analytics, and even a visual ratings feature — all easily accessible and centralized.
One methodology. One platform. Endless consistency. Literal by Hiphen empowers your product development and regulatory teams to make data-driven decisions — and confidently prove your product works within a real-world global testing program.
Assessing the % of green plant pixel through multiple traits, providing a valuable indicator about crop deficiencies for Biostimulant and Biocontrol efficacy testing.
In Biostimulant and Biocontrol efficacy testing, proving your product’s protective power depends on one thing: how healthy your plants remain under stress. But manual assessments are slow, subjective, and often blind to subtle differences that truly define product performance. Literal by Hiphen changes that.
Literal captures high-resolution canopy images directly in the field and its algorithms then precisely quantify the percentage of green plant pixels in each image — providing a precise, objective and valuable indicator about crop deficiencies. This allows you to move beyond simple presence or absence of symptoms in your modalities. With Literal, you can detect subtle variations, distinguishing early signs of stress or infection from advanced symptoms. You can visualize, measure, and quantify those differences — turning nuanced crop responses into clear, comparable metrics, from non-destructive assessments.
All data, images, and analytics are centralized in a single platform (Cloverfield) giving you objective, traceable evidence to strengthen regulatory dossiers and demonstrate true biological efficacy.
Use Literal and reveal what the eye can’t see and transform plant deficiencies into a measurable proof of product performance.
How CROs can take advantage of digital phenotyping to turn data into a competitive edge.
In a world where credibility is measured in data, Literal by Hiphen empowers CROs to lead with precision, transparency, and innovation — transforming every trial into a story of proven performance.
For every CRO, data is the ultimate proof of excellence. Each trial, each measurement, and each report tells a story — one that product developers rely on to make critical decisions. Accuracy, traceability, and insight define the difference between a standard service and a trusted research partner. That’s where Literal by Hiphen brings real competitive advantage.
Literal offers CROs a versatile phenotyping system that elevates the quality of experimentation. Through advanced imaging and AI-powered analysis, it captures precise indicators of plant response and overall trial performance — providing insights that go far beyond manual observation.
With Literal, CROs can demonstrate not only that trials were conducted rigorously, but that they generated science-grade, traceable, and repeatable data proving product performance to help product development teams make informed decisions for their programs. From crop health to stress proxies and yield-related traits, the system delivers a holistic view of plant characteristics — helping your teams prove value, not just report numbers.
Traits With Literal®
Making High-resolution Phenotyping Finally Accessible
Reliable Product For Ag Research
<30s
Time / plot
(average)
150+
Plots screened / hour
(up to)*
x3
Trait repeatability vs manual methods**
x10
Image resolution
vs drone imagery***
*Depending on plot size and protocol. **Comparison between manual methods vs literal on multiple trait assessments. ***Compared to Mavic 3M integrated RGB sensor
With Literal®
Easy To Integrate Into Your Workflow
