Transparent Produce Grading Through Mobile Image Analysis

Julien Moreau

Transparent Produce Grading Through Mobile Image Analysis

Author(s)	Prof. Julien Moreau
Country	United States
Abstract	Produce grading influences market access, price formation, buyer confidence, postharvest handling, and the allocation of fruits and vegetables into different commercial channels. Conventional visual grading can be practical but may vary among inspectors because assessments of color, shape, size, maturity, and visible defects contain subjective elements. Computer vision offers a mechanism for converting observable produce characteristics into reproducible measurements, while smartphone cameras create the possibility of moving image-based grading from centralized packing facilities to farms, aggregation centers, local markets, and small-scale supply chains. This study develops a simulation-based framework for transparent produce grading through mobile image analysis without presenting hypothetical images or grading trials as empirical observations. Five grading configurations are compared: manual visual assessment, uncalibrated mobile analysis, standardized mobile image capture, calibrated mobile grading, and explainable mobile grading with human review. A Mobile Grading Transparency Index integrates measurement consistency, grading-rule visibility, confidence communication, reproducibility, and contestability. Simulated agreement with a reference grading protocol increases from 76% for conventional visual assessment to 96% for an explainable calibrated mobile system supported by human review. The analysis demonstrates that algorithmic grading becomes transparent only when users can understand which visible attributes influenced the grade, how confident the system is, whether image quality was adequate, and how an uncertain or disputed result can be reviewed. Camera differences, automatic white balance, illumination, background, viewing angle, occlusion, and training-data representativeness remain important sources of error. Mobile image analysis should therefore be positioned primarily as a standardized assessment tool for externally visible quality attributes rather than as a substitute for measurements of internal composition, food safety, or hidden defects. The proposed framework demonstrates how affordable computer vision can support more consistent produce transactions while preserving human oversight and traceable grading logic.
Keywords	produce grading, mobile image analysis, computer vision, smartphone agriculture, grading transparency, fruit quality, machine vision, postharvest technology
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-03-10

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American Journal of Advanced Multidisciplinary Innovation and Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Transparent Produce Grading Through Mobile Image Analysis

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