AI-Enabled Sensing Systems for Agriculture and Quality Assessment
Practical hardware and data analytics designed for real-world field and post-harvest conditions.
Key Challenges in Agricultural Quality Assessment
- High post-harvest losses due to manual and subjective quality inspection
- Inconsistent grading and sorting outcomes across locations and operators
- Limited availability of affordable, field-deployable sensing systems
- Lack of actionable, data-driven insights at farm, aggregation, and storage levels
Our Solutions
SenseQube develops modular sensing and analytics systems designed for agricultural quality assessment and field-level decision support.
FruitsQube
A multi-sensor fruit quality inspection system combining vision and spectral data to assist in grading, sorting, and quality assessment during post-harvest handling.
GrainsQube
A non-destructive grain analysis system designed to assess quality parameters and detect defects at procurement, storage, and aggregation points.
Soil Trident
Modular sensing platforms for monitoring soil and operational parameters, enabling data-driven insights for farm-level and supply-chain decision-making.
Why SenseQube
SenseQube is focused on building practical, deployable sensing systems grounded in real field conditions rather than laboratory-only prototypes.
Execution-Focused Approach
Our systems are designed with an emphasis on robustness, repeatability, and ease of deployment across farms, aggregation centers, and storage environments.
Built for Field Reality
Product development is informed by on-ground agricultural workflows, with attention to environmental variability, operator constraints, and data reliability.
Current Status & Next Steps
SenseQube is currently in the prototype development and validation phase, with functional systems being tested across controlled and field-representative conditions.
- Continued field validation and performance benchmarking
- Pilot deployments with select partners across agriculture and post-harvest workflows
- Iterative refinement of hardware design and data analytics pipelines
- Preparation for scaled pilots and institutional collaborations