Oil palm unstripped bunch detector using modified faster regional convolutional neural network

Aji, Wahyu Sapto; Ghazali, Kamarul Hawari; Akbar, Son Ali

doi:10.11591/ijai.v11.i1.pp189-200

Cited by 3 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Palm oil is widely available in Southeast Asia countries and extensive investigations have been conducted to determine whether it is a viable substitute for dielectric insulating fluid for transformers [1]- [4]. Different types of studies are conducted on palm oil to explore more about its applications [5]- [7]. One of the common types of palm oil known as refined, bleached and deodorised palm oil (RBDPO) is able to meet with the dielectric properties requirement for transformer application [2].…”

Section: Introductionmentioning

confidence: 99%

Impact of low molecular weight acid and moisture on the thermal ageing properties of palm oil

Muzamil Mustam,

Azis,

Jasni

et al. 2024

IJEECS

View full text Add to dashboard Cite

This study examines the effect of low molecular weight acid (LMA), moisture and oxygen on the thermal ageing characteristics of refined, bleached, and deodorised palm oil (RBDPO). The paper moisture was varied between 0.5% and 3.5%. The oil was initially subjected to 0.2 g of LMA and 20 mbar of oxygen pressure. The thermal ageing experiment was performed at 120 °C and 140 °C for 28 days. Several dielectric and physiochemical parameters were measured which included dielectric dissipation factor, relative permittivity, resistivity, moisture in oil, acidity, and thermogravimetric analysis (TGA). It is found that LMA and moisture in paper do not affect the relative permittivity of RBDPO and mineral oil (MO). The dielectric dissipation factor of RBDPO and MO reveals slight increment trends within the ageing time. The decrements of resistivities occur after 7 days of ageing for both RBDPO and MO while only RBDPO shows decrement trend of moisture in oil. The ageing patterns of relative permittivities, dielectric dissipation factors and resistivities for RBDPO are similar to MO. The increment of acidity for RBDPO is more apparent that MO throughout the ageing time. All RBDPOs are more resistant to ageing than MO based on the TGA.

show abstract

Section: Introductionmentioning

confidence: 99%

Impact of low molecular weight acid and moisture on the thermal ageing properties of palm oil

Muzamil Mustam,

Azis,

Jasni

et al. 2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…They have led to a rapid development of different fields of studies (identification [3], [4], medical [5], [6], automotive [7], and [8]). In the field of object detection, the most recent cutting-edge algorithms are faster region based CNN (Faster R-CNN) [3], [9]- [12], single shot multibox detector (SSD) [11], [13], [14] and you only look once (YOLO) [10], [11], [15]. This last one, as a deep learning-based model, have been considered to be the most efficient approach to detecting objects and characteristics such as surface cracks [16], but also a strong and competitive approach for detecting potholes [11].…”

Section: Introductionmentioning

confidence: 99%

An image-based convolutional neural network system for road defects detection

Benallal

Tayeb

2023

IJ-AI

View full text Add to dashboard Cite

An application of convolutional neural network (CNN) technique for road surface defects detection is presented in this paper. You only look ones (YOLO) algorithm showed its capabilities as an effective object detection technique in many previous works for different problems. Road damages detection and classification is one of the most challenging problems faced by public and private road management agencies. We present here results for a first attempt on applying YOLO to detect cracks and potholes, the most common defects encountered in surface roadways. Image database of the Brazilian highways were used to prepare input data, train the model and test it. Despite considering different types of cracks in one class and a less amount of potholes images, results show that the YOLO algorithm performs well with a global rate of 91% of defect detection. Output results analysis induce us to work on providing a local database for Algerian roadways with a large number of defect images/videos, as well as producing an automatic road-dedicated defects detector device.

show abstract

“…In Indonesia, another significant palm oil producer, the loss due to USB is 2.2% [3]. If the USB is not appropriately managed, losses from the USB may reach up to 40% in certain circumstances [4]. Sadly, manual USB monitoring is still practiced today, which has to change [2].…”

Section: Introductionmentioning

confidence: 99%

Oil Palm USB (Unstripped Bunch) Detector Trained on Synthetic Images Generated by PGGAN

Aji,

Bin Ghazali,

Akbar

2023

Journal of Robotics and Control

View full text Add to dashboard Cite

Identifying Unstriped Bunches (USB) is a pivotal challenge in palm oil production, contributing to reduced mill efficiency. Existing manual detection methods are proven time-consuming and prone to inaccuracies. Therefore, we propose an innovative solution harnessing computer vision technology. Specifically, we leverage the Faster R-CNN (Region-based Convolution Neural Network), a robust object detection algorithm, and complement it with Progressive Growing Generative Adversarial Networks (PGGAN) for synthetic image generation. Nevertheless, a scarcity of authentic USB images may hinder the application of Faster R-CNN. Herein, PGGAN is assumed to be pivotal in generating synthetic images of Empty Fruit Bunches (EFB) and USB. Our approach pairs synthetic images with authentic ones to train the Faster R-CNN. The VGG16 feature generator serves as the architectural backbone, fostering enhanced learning. According to our experimental results, USB detectors that were trained solely with authentic images resulted in an accuracy of 77.1%, which highlights the potential of this methodology. However, employing solely synthetic images leads to a slightly reduced accuracy of 75.3%. Strikingly, the fusion of authentic and synthetic images in a balanced ratio of 1:1 fuels a remarkable accuracy surge to 87.9%, signifying a 10.1% improvement. This innovative amalgamation underscores the potential of synthetic data augmentation in refining detection systems. By amalgamating authentic and synthetic data, we unlock a novel dimension of accuracy in USB detection, which was previously unattainable. This contribution holds significant implications for the industry, ensuring further exploration into advanced data synthesis techniques and refining detection models.

show abstract

Oil palm unstripped bunch detector using modified faster regional convolutional neural network

Cited by 3 publications

References 22 publications

Impact of low molecular weight acid and moisture on the thermal ageing properties of palm oil

Impact of low molecular weight acid and moisture on the thermal ageing properties of palm oil

An image-based convolutional neural network system for road defects detection

Oil Palm USB (Unstripped Bunch) Detector Trained on Synthetic Images Generated by PGGAN

Contact Info

Product

Resources

About