Xipotech
Vision-guided robotics• Crop health analytics• Selective harvesting• Edge AI

Robotic Crop Inspection & Harvesting

Autonomous field robots combine computer vision, sensor fusion, and robotic manipulation to monitor plant health and perform precision picking. From early stress detection to yield-aware, fruit-level harvesting, our platform turns real-time insights into efficient action, with geo-anchored records for farm management systems.

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End-to-end autonomy
Inspect, decide, and harvest in a single pass, from plant-level analytics to precision picking.
GIS-ready outputs
Geo-anchor detections and harvest events for seamless integration with farm management tools.
Row navigation
Robust visual-inertial SLAM for GPS-challenged orchards and greenhouses.
Privacy-first
On-device processing and selective redaction for people and property.

End-to-End Robotic Harvesting & Crop Inspection

We design and deliver complete agri-robotics solutions — from custom hardware to production-grade software. Our systems combine computer vision, sensor fusion, SLAM-based navigation, and compliant manipulation to perform autonomous crop inspectionand selective harvesting at scale.

  • Hardware: camera stacks, lighting, embedded GPUs, custom end-effectors & grippers
  • Perception: plant/fruit detection, ripeness & defect scoring, multispectral options
  • Navigation: row-level VIO/SLAM for GPS-challenged orchards and glasshouses
  • Manipulation: grasp planning, force/trajectory control, damage-aware picking
  • Edge AI: low-latency inference, on-device privacy controls
  • Data & Integration: geo-anchored logs, yield analytics, FMIS/GIS export
From prototype to field deployment — built and validated with commercial growers

Prototype demo: selective cucumber picking in a glasshouse — vision-guided approach, gentle grasp, per-plant logging.

How it works

  1. Sense: multi-camera rig captures RGB/depth; optional multispectral for canopy insights.
  2. Localize: visual-inertial SLAM fuses IMU and odometry for row-accurate navigation.
  3. Analyze: AI models score ripeness, disease risk, pests, and nutrient stress per plant.
  4. Act: robotic end-effectors perform selective harvesting with grasp planning and force control.
  5. Record: every action is geo-anchored with timestamps for traceability and yield analytics.
Capabilities at a glance
  • Plant-level inspection
  • Ripeness estimation
  • Early disease alerts
  • Selective picking
  • Yield heatmaps
  • Edge inference

Why it matters

Growers face labour shortages, variable weather, and narrow picking windows. Our system scales plant-level decision-making with consistent quality and full traceability — improving yields, reducing waste, and lowering operating costs.

Less waste
Pick at peak ripeness, skip under- or over-mature fruit.
Fewer passes
Combine inspection and harvest to cut field time.
Quality at scale
Consistent standards across blocks and seasons.
Safer ops
Automate repetitive, high-risk tasks in dense canopies.

Trust & integration

GIS-ready

Export geo-anchored detections and harvest logs to your FMIS or GIS pipeline.

Privacy-first

On-device inference and selective redaction for bystanders and neighbouring properties.