Classification Quality of Tobacco Leaves as Cigarette Raw Material Based on Artificial Neural Networks

Sari, Yuslena; Pramunendar, Ricardus Anggi

doi:10.14445/22312803/ijctt-v50p126

Cited by 7 publications

(9 citation statements)

References 5 publications

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“…Reference [8] employed Backpropagation Neural Network (BNN) in the classification of noncured tobacco leaves as cigarette raw material. A total of 200 images from various grades of tobacco leaves served as the dataset of the study.…”

Section: Related Workmentioning

confidence: 99%

“…The presented studies [7], [8] in tobacco grading did feature extraction to train the GRNN and BNN models. It is in contrast with the CNN used by [9] which does not take features as input.…”

Section: Related Workmentioning

confidence: 99%

“…They proposed algorithms to extract the leaves' color, shape, texture, injury and waste tolerance, stalk position, and maturity. Relative to classification, machine learning algorithms like Fuzzy [4], Support Vector Machine [5], K-Nearest Neighbor [6], Generalized Regression Neural Network [7], and Backpropagation Neural Network [8] were used to classify tobacco leaves. Because the classification features were selected based on human experience, these approaches improved the accuracy of tobacco grading.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated Tobacco Grading Using Image Processing Techniques and a Convolutional Neural Network

Marzan¹,

Ruiz²

2019

IJMLC

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Tobacco grading is very important for crop market price determination. It is beneficial for graders who need to manually classify tobacco leaves according to their grades. As such, a grading system using image processing techniques and a Convolutional Neural Network (CNN) is proposed in this study which aims to surpass existing results in tobacco grading. The system consists of image acquisition, pre-processing, leaf detection, segmentation, and classification. Tobacco leaf images were directly taken at the tobacco grading room and pre-processed for subsequent tasks. Through a Haar cascade classifier and applying image processing techniques, air-cured tobacco leaves are automatically detected and extracted in images. This method produced satisfactory results as it can successfully detect single and multiple tobacco leaves taken under different positions and scale. All detected tobacco leaves underwent various image processing to precisely segment leaves from the rest of the image. The experimental results also reveal that using segmented and nonsegmented images, CNN classifier can effectively grade tobacco leaves as high as 96.25% accuracy rate and on average, took 7.43 ms to classify a single tobacco leaf. This approach outperforms current methods in grading tobacco leaves.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Tobacco Grading Using Image Processing Techniques and a Convolutional Neural Network

Marzan¹,

Ruiz²

2019

IJMLC

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“…It is necessary to develop classification automation tools to minimize human errors and to determine the quality of tobacco leaves with high accuracy. 9 Recently, artificial intelligence applications such as image processing, machine vision, pattern recognition, and deep learning have shown rapid development. The automatic classification method of color-based tobacco leaves is likely to be enabled by these technologies.…”

Section: Introductionmentioning

confidence: 99%

Determination of quality grade of tobacco leaf by image processing on correlated color temperature

Odabas

Kurt

2022

Concurrency and Computation

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The most important factor in tobacco is quality. It is the sum of physical, chemical, and smoking properties that characterize quality. Post-harvest curing of leaves plays an important role in quality. In this study, an approach based on correlated color temperature (CCT) is proposed to determine the ideal curing time of tobacco leaves. While photographing leaf samples from different days during the curing of tobacco leaves, color correction was made using X-rite color checker to eliminate the negative effect of ambient light. CCT values were calculated from the repaired photographs using the McCammy method. The inclusion rate of pixels during the calculation is 97%. The equation of CCT variation depending on the day is fitted as CCT = (−0.1889 × day 3 ) + (4.8347 × day 2 ) − (115.59 × day) + 4919.2. RMSE (26.77 K) and high R 2 value were calculated as 98.8% in the equation. CCT values have been converted to quality grade (QG) values to be more understandable. As a result of the study, CCT values ranged between 5000 and 2500 K and corresponding QG values ranged between 79% and 105%. The ideal curing time was calculated as the 17th day after harvesting, the QG value for that day was 100% and the average CCT value was 3000 K.

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“…Feature extraction was selected because feature extraction is an important process before pattern classification, both in machine learning and in data mining [1][2][3][4]. The purpose of feature extraction is to find a transformation from a smaller dimensional space, thereby reducing the complexity in iris recognition [5].…”

Section: Introductionmentioning

confidence: 99%

Iris recognition based on distance similarity and PCA

Sari

Alkaff

Pramunendar

2018

AIP Conference Proceedings

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Abstract.Iris is regarded as the most unique biometric identification. This paper proposes a new feature extraction based on iris texture patterns with Principal Component Analysis (PCA). PCA is used to store computing process in classification process. The focus of this paper was to compare the accuracy result of classification methods of distance measurement such as Euclidean Distance, City Block Distance, Chebyshev Distance, Canberra Distance and Bray-Curtis Distance. Accuracy test shows that PCA can be used in various classification methods that use distance measurement.

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Classification Quality of Tobacco Leaves as Cigarette Raw Material Based on Artificial Neural Networks

Cited by 7 publications

References 5 publications

Automated Tobacco Grading Using Image Processing Techniques and a Convolutional Neural Network

Automated Tobacco Grading Using Image Processing Techniques and a Convolutional Neural Network

Determination of quality grade of tobacco leaf by image processing on correlated color temperature

Iris recognition based on distance similarity and PCA

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