Utilizing RGB imaging and machine learning for freshness level determination of green bell pepper (Capsicum annuum L.) throughout its shelf-life

Aug 5, 2026

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This study investigates sensory qualities, weight loss, and texture changes in green bell peppers as indicators of freshness during storage. Machine learning feasibility is evaluated for monitoring freshness changes in the commercial variety ‘Kyohikari’ over 16 days at +5 °C and 95% relative humidity. RGB images are captured with a DSLR camera, while sensory panels and texture, mass loss, chlorophyll, and carotenoid measurements are taken at multiple intervals. Logistic regression, neural networks, random forests, k-nearest neighbors, and support vector machines classify and predict freshness, with hue angle and chlorophyll staying stable. After 10 days, about 3% weight loss coincides with off-odors and off-flavors, marking loss of marketability. Neural networks achieve 100% accuracy for freshness classification.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Rationale for non-destructive freshness assessment

Methods (experimental design and modeling)

RGB image acquisition during storage

Machine learning models for classification and prediction

Results (freshness indicators and model performance)

Stability of hue angle and chlorophyll

Weight loss, off-odors, off-flavors after 10 days

Accuracy of freshness classification and prediction

Postharvest Biology and Technology 222 (2025) 113359 Contents lists available at ScienceDirect Postharvest Biology and Technology journal [homepage:](homepage: www.elsevier.com/locate/postharvbio)[ www.elsevier.com/locate/postharvbio](homepage: www.elsevier.com/locate/postharvbio) | Utilizing RGB imaging and machine learning for freshness level determination of green bell pepper (Capsicum annuum L.) throughout its shelf-life
Danial Fatchurrahmana,*, Maulidia Hilailib, Nurwahyuningsihc, Lucia Russo a, Mahirah Binti Jaharid, Ayoub Fathi-Najafabadia
a Dipartimento di Scienze Agrarie, Alimenti, Risorse Naturali e Ingegneria (DAFNE), Universit`a di Foggia, Via Napoli 25, Foggia 71122, Italy b Laboratory of Bio-Sensing Engineering, Graduate School of Agriculture, Kyoto University, Kyoto 606-8502, Japan
c Department of Agricultural Technology, politeknik Negeri Jember, Jember 68121, Indonesia
d Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Malaysia | | | | --- | --- | --- | | A R T I C L E I N F O
Keywords: Algorithms Storage Fruit quality
Food waste Taste | A B S T R A C T
This study investigates the sensory qualities, weight loss and texture changes in green bell peppers as indicator of freshness during storage. It assesses the feasibility of using machine learning methods to monitor freshness changes in the commercial variety ‘Kyohikari’ over a 16-d storage period at + 5 ◦ C and 95 % relative humidity (RH). Throughout the storage period, the commercial variety of green bell peppers were stored, and RGB images were captured using a DSLR camera. Sensory assessments and measurements of texture, weight loss, chlorophyll, and carotenoids were conducted at various inteval. Several machine learning approaches- including logistic regression, neural networks, random forests, k-nearest neighbors, and support vector machines, were employed to develop classification and prediction models for fruit freshness during storage intervals of 0, 4, 6, 8, 10, 12, 14, and 16 d. The results indicate that hue angle and chlorophyll content remained unchanged throughout the experiment. However, after 10 d of storage, a 3 % weight loss was observed, accompanied by the detection of offodors and off-flavors, marking the limit of marketability. The models demonstrated exceptional accuracy in classifying and predicting the freshness of green bell peppers on the storage day, achieving 100 % accuracy with the neural network algorithm. | | 1. Introduction Green bell pepper is non-climacteric fruit and is an important horticultural crop cultivate in temperate, tropical and subtropical regions (Singh et al., 2014; Blanke and Holthe, 1997). This vegetable has a limited shelf-life and is prone to deterioration due to water loss stored at low relative humidity, necessitating proper handling to maintain its quality and longevity (Marinov et al., 2023; Althaus and Blanke, 2021; 2020; Ullah et al., 2017). The optimal storage conditions for green bell peppers are temperatures between 7.5 and 13 ºC with 95–98 % relative humidity (RH), which can extend their shelf life to 2 – 3 weeks. However, they can also be stored at + 5 ºC and 90 RH for approximately 2 weeks to minimize water loss, enhancing marketability in certain supply chains (Lim et al., 2007; Mercado et al., 1995). Quality assessment of green bell peppers typically involves evaluating texture, color, and sensory attributes (Rodoni et al., 2015; Kader and Holcroft., 1999; Blanke and Holthe, 1997). Unfortunately, these traditional methods are often destructive, time-consuming, and costly. In recent years, there has been increasing interest in non-destructive techniques for assessing fruit quality. Spectroscopic and imaging technologies have proven to be effective tools for inspecting the quality and safety of fresh produce (Fathi-Najafabadi et al., 2021; Fatchurrahman et al., 2020b; Amodio et al., 2017). Non-destructive f

FAQs

What freshness indicators were evaluated during storage of green bell peppers?

The study evaluated sensory qualities, weight loss, texture changes, and associated biochemical measures including chlorophyll and carotenoids.

How were RGB images used in the machine learning workflow?

RGB images of the green bell peppers were captured using a DSLR camera at multiple storage intervals, and these images were used to train classification and prediction models.

Which machine learning method achieved the best freshness classification accuracy?

The neural network model achieved 100% accuracy for classifying the freshness of green bell peppers on the storage day.

Aug 5, 2026

Upload by Ezra