Coconut Classification Using Python with Machine Learning

This project, Coconut Classification Using Machine Learning in Python, focuses on automating the classification of coconuts based on their visual features. By leveraging computer vision and machine learning techniques, the system aims differentiate coconuts based ripeness, quality and other key attributes. implementation training machine learning models, including K-Nearest Neighbors (KNN), Convolutional Neural Net (CNN), Logistic Regression, using image datasets of coconuts. Data augmentation techniques are applied to improve model accuracy and generalization. The project’s goal is to provide an efficient and reliable solution for industries such as agriculture and food processing, reducing manual labor and enhancing quality control. user-friendly interface is also integrated to enable easy classification for end users.

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Publication Details

Journal
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Published
2025-07-30
DOI
https://doi.org/10.32628/cseit25111397
Primary Topic
Coconut Research and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Coconut Classification Using Python with Machine Learning

Gina Kusuma
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Coconut Research and Applications
article

Coconut Classification Using Python with Machine Learning

Gina Kusuma
article en

Abstract

This project, Coconut Classification Using Machine Learning in Python, focuses on automating the classification of coconuts based on their visual features. By leveraging computer vision and machine learning techniques, the system aims differentiate coconuts based ripeness, quality and other key attributes. implementation training machine learning models, including K-Nearest Neighbors (KNN), Convolutional Neural Net (CNN), Logistic Regression, using image datasets of coconuts. Data augmentation techniques are applied to improve model accuracy and generalization. The project’s goal is to provide an efficient and reliable solution for industries such as agriculture and food processing, reducing manual labor and enhancing quality control. user-friendly interface is also integrated to enable easy classification for end users.

International Journal of Scientific Research in Computer Science Engineering and Information TechnologyVol. 11(4)
PES University (IN)
Openalex Percentile: Top 84%
Coconut Research and Applications
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