Comparison of Feature Extraction and Auto-Preprocessing for Chili Pepper (Capsicum Frutescens) Quality Classification - A Machine Learning Study

Aug 3, 2026

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Low-cost webcams used in machine vision still suffer from limited resolution and noise, reducing the reliability of grading systems built directly on captured images. This study compares three feature extraction strategies—color features, ORB features, and a combined color+ORB approach—along with an auto-preprocessing step to classify chili pepper (Capsicum frutescens) quality using machine learning. From 525 images collected via Logitech C170, the proposed auto-preprocessing improves model performance across extractors.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Background and motivation for low-cost camera grading

Proposed auto-preprocessing approach

Method

Data collection and feature extraction

IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 13, No. 1, March 2024, pp. 319~ 328 ISSN: 2252-8938, DOI: 10. 11591/ijai.v13 .i1 .pp319-328 􀂈 319 | Comparison of feature extraction and auto-preprocessing for chili pepper (Capsicum Frutescens) quality classification using
machine learning
Jelita Asian1, Nunik Destria Arianti2, Ariefin3, Muhamad Muslih2
1Department of Informatic Magister, Nusa Putra University, Sukabumi, Indonesia
2Department of Information System, Nusa Putra University, Sukabumi, Indonesia 3Department of Mechanical Engineering, Lhokseumawe State Polytechnic, Lhokseumawe, Indonesia | | | --- | --- | | Article Info
Article history:
Received Mar 26, 2023 Revised May 26, 2023 Accepted Jun 3, 2023
Keywords:
Auto-preprocessing
Color feature extractor Machine vision
ORB feature extractor Supervised image processing
Corresponding Author: | The low-cost camera for machine vision, such as a webcam, still has a problem with resolution noise. Therefore, it is important to learn strategies to reduce noise from low-cost camera images so that they can be widely used for grading machines in the future. This paper aims to compare three feature extraction methods with auto-preprocessing to classify chili pepper (Capsicum Frutescens) quality using a machine learning algorithm. Three extraction methods were used, including the color feature, oriented FAST and rotated BRIEF (ORB), and the combination color feature and ORB. A total of 525 image data for quality chili pepper were collected using the webcam. The auto-preprocessing strategy to classify chili peppers can improve the performance of machine-learning algorithms for all data generated by the feature extractor. The performance of the chili paper quality classification model with auto-preprocessing of the variable color feature can improve the performance of machine learning algorithms by up to 64.21% . The performance improvement of the classification model using the ORB feature variable and the auto-preprocessing of up to 4.41% . The performance improvement of the classification model using machine learning algorithms is 11.27% when using the combination color feature and ORB feature and autopreprocessing.
This is an open access article under the CC BY-SA license.

ABSTRACT | | Jelita Asian
Department of Informatic Magister, Nusa Putra University St. Cibolang 21, Sukabumi, Indonesia
Email: [jelita.asian@nusaputra.ac.id](jelita.asian@nusaputra.ac.id) | | 1. INTRODUCTION Grading in the agricultural production process is needed to separate products based on quality. Generally, grading quality indicators are shape, size, color, maturity, and defection [1]–[4] . Grading can be done manually, but it is prone to inconsistencies and requires more time. An automatic grading process was developed using computer vision and machine learning to overcome this problem. In addition to speeding up the process, automatic grading with computers allows for further fruit processing utilizing an automation system. But until now, devices to support it so it can have high performance are still not affordable, especially for sensors such as cameras. The camera is the most important part of grading machine development [5]–[7]. This part first acquire data before proceeding with processing. On the one hand, many cameras have been developed for computer vision purposes, especially for machine grading. However, most cameras are still expensive and unaffordable if they want to be used for creating a grading machine on a small industrial scale, such as for fruit and vegetable horticultural products. On the other hand, low-cost cameras (such as web cameras) can be used for machine grading but provide low-quality images. This will impact the performance of the developed grading machine if it is to be used directly. Therefore, creating a classification modeling strategy that uses source images from low-cost cameras is necessa

FAQs

Why is auto-preprocessing needed for chili pepper quality classification with low-cost cameras?

Low-cost cameras produce low-resolution images with noise, which degrades the extracted features. Auto-preprocessing is introduced to improve feature quality before modeling.

Which feature extraction methods are compared in the study?

The study compares color features, ORB (oriented FAST and rotated BRIEF) features, and a combined color+ORB feature approach.

How was the dataset collected for training and evaluation?

A total of 525 chili images were collected using a low-cost web camera (Logitech C170), including 420 good-quality samples and 105 degraded samples.

Aug 3, 2026

Upload by Levi