[PDF][PDF] Characterising Colour Feature Descriptors for Ficus carica L. Ripeness Classification Based on Artificial Neural Network (ANN).

IA Mazni, S Setumin, MS Osman… - Pertanika Journal of …, 2023 - 119.40.116.186
Excessive feature dimensions impact the effectiveness of machine learning, computationally
expensive and the analysis of feature correlations in the engineering area. This paper uses …

Fig Fruit Image Segmentation using Threshold, K-means Clustering, and Sharp U-Net Techniques

NEM Rosli, S Setumin, A Nugroho… - … on Emerging Smart …, 2022 - ieeexplore.ieee.org
In this study, image segmentation on Ficus Carica (fig) was developed. Fig fruit image
segmentation separates fruit objects by removing the background in the image, including …

Development of fig fruit ripeness classification using convolutional neural network

SJ Abu Bakar, HR Musa, MS Osman… - ESTEEM Academic …, 2024 - ir.uitm.edu.my
This study presents the design and evaluation of a deep convolutional neural network
(CNN) model for accurately classifying fig ripeness stages. Traditionally, fruit ripeness …

Development of a Non-Destructive Fruit Quality Predictor Using Convolutional Neural Network Regression Model on Raspberry Pi

IA Mazni, S Setumin, AAS Joseph… - … on Emerging Smart …, 2022 - ieeexplore.ieee.org
The rapid progress in agriculture continually increases the demand for high-quality fruits.
However, the quality of the fruits was usually assessed by a destructive method that normally …

[PDF][PDF] Development of fig fruit ripeness classification using convolutional neural network.

SJA Bakar, HR Musa, MS Osman, MMM Abdul Kader… - ESTEEM, 2024 - researchgate.net
This study presents the design and evaluation of a deep convolutional neural network
(CNN) model for accurately classifying fig ripeness stages. Traditionally, fruit ripeness …

PERTANIKA JOURNAL OF SOCIAL SCIENCES AND HUMANITIES

IA Mazni, S Setumin, MS Osman, MK Osman, MS Tahir - pertanika.upm.edu.my
Excessive feature dimensions impact the effectiveness of machine learning, computationally
expensive and the analysis of feature correlations in the engineering area. This paper uses …

UiTM Press Pulau Pinang

SJA Bakar, HR Musa, MS Osman, MMMA Kader - uppp.uitm.edu.my
This study presents the design and evaluation of a deep convolutional neural network
(CNN) model for accurately classifying fig ripeness stages. Traditionally, fruit ripeness …

[PDF][PDF] Systematic Literature Review Approach on the Fruit Quality Assessment Based on Fruit Imaging Techniques

IA Mazni, S Setumin, MS Osman, K Osman, M Subri - jeesr.uitm.edu.my
Non-destructive quality assessment is one of the methods in image processing used to
evaluate the qualities of fruits without destroying the internal structure and external …

PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

IA Mazni, S Setumin, MS Osman, MK Osman, MS Tahir - pertanika.upm.edu.my
Excessive feature dimensions impact the effectiveness of machine learning, computationally
expensive and the analysis of feature correlations in the engineering area. This paper uses …