Non-destructive & high-throughput
Scans large plant populations without requiring manual measurement.
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Accreditations
Case Study · Plant Phenomics Centre, IARI
Hyperspectral image processing at the Plant Phenomics Centre, IARI — enabling accurate, early-stage, non-destructive crop-yield estimation for wheat and rice by correlating spectral signatures (photosynthetic efficiency, chlorophyll, water stress) with final yield.
Industry
Agriculture
Region
IN
Year
2023
Featured
Yes
Artificial Intelligence, Machine Learning & IoT
At the Plant Phenomics Centre, IARI, a major challenge was the lack of accurate, non-destructive, early-stage methods for estimating crop yield under different environmental and genetic conditions. Traditional approaches — conventional field trials, visual assessment and manual measurement — were labour-intensive, time-consuming, hard to scale, and often unable to capture subtle physiological changes affecting final yield, especially under stress. The project used hyperspectral image processing together with controlled environmental conditions to estimate the yield potential of wheat and rice.
Business requirements & challenges
The underlying requirement was to develop a method that could provide early, accurate and non-destructive crop-yield estimation.
Major challenges
Solution
Xpertnest implemented a hyperspectral image processing solution at the Plant Phenomics Centre, IARI. The system captures detailed spectral signatures from plants, identifies physiological characteristics associated with crop performance, and correlates them with eventual yield through data-driven models — enabling real-time monitoring and yield prediction across genetic and environmental scenarios.
Stack
How it works
What's new
Scans large plant populations without requiring manual measurement.
Can potentially be extended to UAV/drone and satellite imaging in the future.
Works across different crops and stress conditions with minimal retraining.
Gives breeders and agronomists real-time information to support plant selection, crop management and research decisions.
Business benefits
At a glance
| Crop | Wheat and Rice |
|---|---|
| Analysis | Plant Phenotyping |
| Imaging | Hyperspectral Imaging |
| Prediction | Early Crop Yield Potential |
| Measurement | Non-destructive |
| Processing | High-throughput |
| Conditions | Diverse environmental & genetic conditions |
| Physiological Parameters | Photosynthetic efficiency, chlorophyll, water stress and other traits |
| Scalability | UAV/drone and satellite imaging potential |
| Adaptability | Different crops and stress conditions with minimal retraining |
| Users | Breeders and Agronomists |
| Applications | Crop breeding, crop management, research, policy and precision agriculture |
Why it matters
India's need for food security, sustainable agriculture, faster breeding programmes and optimised field practices creates demand for technologies that improve crop research and agricultural decision-making. Accurate early-stage yield prediction helps researchers and policymakers make better decisions around genotype selection, crop management and resource allocation — establishing a stronger connection between phenotypic traits and actual yield potential through advanced technology.
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