Candidate processing workflow
Instance segmentation → dimensions and Lab color → texture/defect classification → multiview association
JIVISION offers project development and technical assessment for Fruit, Vegetable, Grain and Agricultural Product Grading. Scope: Size, color, ripeness appearance, visible lesions, damage and produce counts.
Size, color, ripeness appearance, visible lesions, damage and produce counts
Sample grade criteria across varieties and seasons; apparent ripeness does not directly establish sugar content or internal quality.
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.
Instance segmentation → dimensions and Lab color → texture/defect classification → multiview association
Provide product variants, defect definitions and reference parts, acceptable and borderline samples, field of view, takt time, cameras/lighting and trigger/reject interfaces.
Grading model, specification rules, counting and sorting interfaces
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.
Grade confusion matrix; dimensional error; lesion-area error; count error
Separate test data by product, batch and defect class. Report misses, false calls, minimum visible conditions and line cycle time; test physical rejection separately.
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.
Agree targets, stations, inputs and responsibilities in a statement of work, separating required and excluded conditions.
Use representative samples to test critical risks, document feasibility/failures and scope the next-stage estimate.
Implement agreed functions and interfaces with configuration/change records; use offline replay before authorized device or site integration.
Retest against individual measures and hand over contracted artifacts with known limits; manage maintenance, expansion and changeovers as subsequent work packages.
Image decisions cover visible features under agreed conditions; acceptable appearance does not establish material, strength, sealing or electrical conformity.
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.
Size, color, ripeness appearance, visible lesions, damage and produce counts. Deliverables: Grading model, specification rules, counting and sorting interfaces
Sample grade criteria across varieties and seasons; apparent ripeness does not directly establish sugar content or internal quality.
Task-specific measures: Grade confusion matrix; dimensional error; lesion-area error; count error. Separate test data by product, batch and defect class. Report misses, false calls, minimum visible conditions and line cycle time; test physical rejection separately.
After reviewing samples for size, color, ripeness appearance, visible lesions, damage and produce counts, 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.
Service ID: A18 · Fruit, Vegetable, Grain and Agricultural Product Grading
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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