Robot Vision & Automation Localization · D26

Fruit-Picking Point Localization under Foliage Occlusion

JIVISION offers project development and technical assessment for Fruit-Picking Point Localization under Foliage Occlusion. Scope: Fruit, stems, leaves, apparent ripeness, picking points and depth.

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Scope and project-specific requirements

Task / output

Fruit, stems, leaves, apparent ripeness, picking points and depth

Critical decision conditions

Stratify foliage occlusion by visibility and validate stem points together with feasible picking directions.

Candidate technical approach

These are candidate implementation paths. Establish a baseline on real samples, then select or combine methods for imaging, speed and deployment constraints. Model names do not imply measured project results.

Object detection and confidence gating

RT-DETR, RTMDet, YOLOX, PP-YOLOE or rotated-box detection → non-maximum suppression and confidence thresholds → task rules

Region segmentation and quantification

U-Net, DeepLabV3+, SegFormer or Mask2Former → connected components and morphology → region measurements

Stereo depth estimation

Stereo calibration and rectification → BM, SGBM or RAFT-Stereo → consistency checks and hole handling

Object pose estimation

Markers or 2D-to-3D correspondences → EPnP, IPPE or PnP-RANSAC → nonlinear refinement → optional ICP

Inputs for assessment

Provide robot/controller models, grippers/tools, parts or CAD, coordinate frames, working volume, camera mounts, control periods and interlock interfaces.

Deliverables and interface agreement

Result schema, algorithm or processing configuration, example outputs and evaluation records for: Fruit, stems, leaves, apparent ripeness, picking points and depth. Software format and source-code scope are agreed in the contract.

Specify input formats, output fields, coordinates/units, error states, versions and invocation methods. Define review and failure handling. The statement of work determines the exact scope of source code, executables, model files or analysis reports.

Acceptance method and measures

Task-specific acceptance measures

Fruit recall; picking-point error; occlusion-stratified success

Test visual localization error, calibration error, grasp/alignment success and end-to-end cycle time separately, including occlusion, empty scenes, changeovers and communication loss.

These are measures to agree and test, not achieved-performance claims. Freeze samples, reference truth, thresholds and hardware/software versions before acceptance; report subgroup results and failures, identifying under-sampled conditions as uncovered.

Implementation and procurement stages

Define scope
Agree targets, stations, inputs and responsibilities in a statement of work, separating required and excluded conditions.
Sample validation
Use representative samples to test critical risks, document feasibility/failures and scope the next-stage estimate.
Development and integration
Implement agreed functions and interfaces with configuration/change records; use offline replay before authorized device or site integration.
Acceptance and handover
Retest against individual measures and hand over contracted artifacts with known limits; manage maintenance, expansion and changeovers as subsequent work packages.

Scope limits and licensing

Vision output does not replace independent safety control. Responsible teams must verify robot motion, tooling and line interlocks before integrated testing.

Candidate technologies are not license clearance. Check code, model weights, training data and dependency versions separately. Replace, license or exclude components unsuitable for the intended commercial delivery. Customer data is not used for public training by default.

Project FAQs

What does this service produce?

Fruit, stems, leaves, apparent ripeness, picking points and depth. Deliverables: Result schema, algorithm or processing configuration, example outputs and evaluation records for: Fruit, stems, leaves, apparent ripeness, picking points and depth. Software format and source-code scope are agreed in the contract.

What needs to be confirmed first?

Stratify foliage occlusion by visibility and validate stem points together with feasible picking directions.

How is acceptance defined beyond a demonstration?

Task-specific measures: Fruit recall; picking-point error; occlusion-stratified success. Test visual localization error, calibration error, grasp/alignment success and end-to-end cycle time separately, including occlusion, empty scenes, changeovers and communication loss.

How are cost and schedule assessed?

After reviewing samples for fruit, stems, leaves, apparent ripeness, picking points and depth, equipment conditions and interfaces, scope validation, development, deployment and acceptance separately. Data coverage, site changes and delivery rights affect the estimate; no fixed performance or schedule is promised before assessment.

PROJECT INQUIRY

Discuss this service for your project

Service ID: D26 · Fruit-Picking Point Localization under Foliage Occlusion

Describe available samples, equipment and target measures. Agree confidentiality and permissions before transferring sensitive or personal data through an approved channel.

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