Abstract :

Horticultural Seed Bags Play An Important Role In Agricultural Production, Storage, Transportation, And Distribution, Making Their Accurate Identification Essential For Efficient Seed Management And Qualitycontrol Operations. Conventional Inspection Methods Largely Depend On Manual Observation, Which Can Be Time-consuming And May Become Difficult When Large Numbers Of Seed Bags Are Handled. This Study Proposes A Generative AI-based Approach For The Detection And Classification Of Horticultural Seed Bags Using Image-based Analysis. The Proposed Framework Utilizes Generative AI To Learn The Visual Characteristics Of Seed-bag Images And Generate Diverse Synthetic Samples Representing Variations In Orientation, Lighting, Background, Packaging Appearance, Size, Scale, Partial Occlusion, And Viewing Angle. These Generated Samples Are Combined With Available Real Images To Develop A More Diverse Image Collection For Analysis And Classification. The Approach Aims To Improve The Ability Of The System To Recognize Seed Bags Under Different Visual And Environmental Conditions While Reducing Dependence On Large Volumes Of Manually Collected Images. The Generated Outputs Are Analyzed Based On Visual Quality, Diversity, And Their Usefulness For Seed-bag Identification. The Proposed Framework Can Support Automated Inspection, Seed Inventory Management, Packaging Verification, Agricultural Storage Facilities, And Horticultural Quality-control Operations. Overall, The Study Highlights The Potential Of Generative AI For Developing Flexible And Scalable Solutions For Automated Horticultural Seed-bag Detection And Classification.

Published:

20-8-2026

Issue:

Vol. 26 No. 8 (2026)

Page Nos:

1033-1045

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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