Image processing approach for grading tobacco leaf based on color and quality

Harjoko, Agus; Prahara, Adhi; Supardi, Tri Wahyu; Candradewi, Ika; Pulungan, Reza; Hartati, Sri

doi:10.21307/ijssis-2019-010

Cited by 9 publications

(6 citation statements)

References 8 publications

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“…He uses image processing techniques such as image thresholding, morphological operation, spot detection, and tobacco leaf color analysis to determine tobacco leaf grade. Although the method he proposed can detect blade defects, the accuracy of the detection is not clearly stated [1]. Camlica believes that tobacco is important to the agricultural sector in Turkey, and different regions of the country produce high-quality varieties.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Data Research on Tobacco Leaf Image Collection Based on Computer Vision Sensor

2021

Journal of Sensors

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In the process of tobacco silk making, how to better improve the quality of stem and leaf separation has become an issue of concern. His research mainly discusses the data collected from tobacco leaf images based on computer vision sensors. In LM (Levenberg-Marquarelt) as a training function, the algorithm uses threshing effect samples for training and learning. This paper is aimed at extracting the shape characteristic parameters of tobacco leaves and obtains the shape parameters of the length, width, area, circumference, and roundness of the tobacco leaves. In this paper, boundary tracking is used to obtain the coordinate and direction information of the tobacco leaf boundary pixels in the image, which provides a basis for obtaining the subsequent extraction of tobacco leaf characteristic parameters. In the tobacco leaf grading system, the tobacco leaf feature parameter extraction module displays the geometric characteristics of tobacco leaf, such as length, width, area, aspect ratio, rectangularity, and color characteristic, hue H , saturation S , A channel, and B channel in detail through the computer vision sensor. Finally, the subjective and objective combination weighting method is used to combine and weight the indicators of the threshing effect of the first-level threshing machine, which not only considers the quantity of information provided by the indicators but also takes into account the subjective view of the experts, which increases the weight of the indicators, accuracy, and scientificity. The approximation accuracy of the training samples of the threshing effect prediction model based on the BP neural network LM algorithm is 99.495%, the approximation accuracy of the validation set is 96.535%, and the approximation accuracy of the test set is 98.392%. This research will greatly improve production efficiency and meet the enterprise’s requirements for high efficiency and low cost.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Data Research on Tobacco Leaf Image Collection Based on Computer Vision Sensor

2021

Journal of Sensors

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“…The destructive method is tested in the laboratory but it is costly and time-consuming [27]. Then, a monotonous and long duration of work would raise a human error [28]. Therefore, non-destructive is needed.…”

Section: Related Workmentioning

confidence: 99%

“…The camera is useful for capturing plant images in a hydroponic environment [34]. Some of the plants that have been observed are tomato, chili, tobacco, spinach, okra, and others [28], [35], [36]. Digital image processing is used to analyze the captured images to obtain plant conditions.…”

Section: Related Workmentioning

confidence: 99%

Design of an Intelligent Hydroponics System to Identify Macronutrient Deficiencies in Chili

Rahadiyan¹,

Hartati²,

Wahyono³

et al. 2022

IJACSA

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Nutrient contents are important for plants. Lack of macronutrients causes plant damage. Several macronutrient deficiencies exhibit similar visual characteristics that are difficult for ordinary farmers to identify. Collaboration between Computer Vision technology and IoT has become a nondestructive method for nutrient monitoring and control, included in the hydroponic system. Computer vision plays a role in processing plant image data based on specific characteristics. However, the analysis of one characteristic cannot represent plant health. In addition, knowing the percentage of macronutrient deficiencies is also needed to support precision agriculture systems. Therefore, we propose a Multi Layer Perceptron architecture that can perform multi-tasks, namely, identification and estimation. In addition, the optimal architecture will also be sought based on the characteristics of the combination of three features in the form of texture, color, and leaf shape. Based on analysis and design, our proposed model has a high potential for identifying and estimating macronutrient deficiency at the same time as well and can be applied to support precision agriculture in Indonesia.

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“…Marzan et al [1] used a CNN classifier for grading tobacco leaves using segmented images. Harjoko et al [2] used image preprocessing and color extraction for grading tobacco leaves using a threshold for the local color category and majority voting for the global color category. Guru et al [3] detect the maturity level of tobacco leaves to be harvested using GLTP (Gray Level Local Texture Patterns).…”

Section: Introductionmentioning

confidence: 99%

Classification of Air-Cured Tobacco Leaf Pests Using Pruning Convolutional Neural Networks and Transfer Learning

Swasono¹,

Tjandrasa²,

Fatichah³

2022

International Journal on Advanced Science, Engineering and Information Technology

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Convolutional Neural Network (CNN) usually uses a large image dataset with many parameters. Small datasets require a small number of parameters. Existing standard (pre-trained) models such as Alexnet, VGG, Inception, and Resnet have been tested with high accuracy but have many parameters. For small datasets, too many parameters become less efficient and increase computation costs. The high computational costs make the model unsuitable for computers with limited resources such as embedded devices and mobile phones. This research proposes pruning on the depth of resnet50 architecture and adds a dimensionality reduction layer after the pruning point. This approach does not require a complex pruning criteria algorithm, so it is easy to implement. Resnet50 was chosen because it is a good performance with batch normalization and skip connections. We use transfer learning for Resnet50 weight. Pruning is carried out at a depth of the network by cutting at the layer of the activation function. Several pruning points were selected to produce several models with certain parameters. The more networks layer pruned, the smaller the number of parameters produced. We add a layer for channel reduction after pruned network to reduce the number of feature maps before entering the fully connected (FC) layer as a classifier. We retrained a new network using a 2000 tobacco leaf pest dataset split into 1600 training and 400 validation images with 4-classes. The result shows that the accuracy could be maintained equal to the unpruned network up to 100% accuracy and 74.38% reduction rate for the number of parameters. A higher reduction rate of the number of parameters up to 90.62% still provides high accuracy of validation data around 99.3%. These prove that our proposed method effectively maintained accuracy and reduced the number of parameters.

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Image processing approach for grading tobacco leaf based on color and quality

Cited by 9 publications

References 8 publications

[Retracted] Data Research on Tobacco Leaf Image Collection Based on Computer Vision Sensor

[Retracted] Data Research on Tobacco Leaf Image Collection Based on Computer Vision Sensor

Design of an Intelligent Hydroponics System to Identify Macronutrient Deficiencies in Chili

Classification of Air-Cured Tobacco Leaf Pests Using Pruning Convolutional Neural Networks and Transfer Learning

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