Classification of Dried Clove Flower Quality using Convolutional Neural Network

Chalik, F. A.; Maki, Wikky Fawwaz Al

doi:10.1109/databia53375.2021.9650199

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…Chalik and Maki [3] classified the quality of clove flowers into four classes; this is certainly different from the ISO 2254-2004 standard, which only has three classes in the clove type: whole clove, headless clove, and mother clove. This study uses the CNN method for the classification process, but color features are used.…”

Section: Related Workmentioning

confidence: 90%

“…The accuracy obtained was 87.75% without any hyperparameter initialization process on CNN. Research by Chalik and Maki [3], the red, green, blue (RGB), hue, saturation, intensity (HSV), and luminance, chromaticity blue, chromaticity red (YCbCr) color feature extraction process was carried out. The system accuracy obtained reaches 96%.…”

Section: Comparison and Before Researchmentioning

confidence: 99%

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Classification of clove types using convolution neural network algorithm with optimizing hyperparamters

Tempola,

Wardoyo,

Musdholifah

et al. 2024

Bulletin EEI

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This study uses clove imagery by classifying it according to ISO 2254-2004 standards: whole, headless, and mother clove. This type of clove will affect the quality and economic value when it has been dried. For this reason, it is necessary to take a first step to control cloves' quality. One way is to classify it from the start. This research will utilize the convolution neural network algorithm and compare it with model transfer learning and modified VGG16 architecture on clove images. In addition, research is also looking for the most optimal hyperparameter. The results of this study indicate that the application of convolution neural network (CNN) to clove images obtains an accuracy value of 84% using a hyperparameter of 50 epochs, a learning rate of 0.001, and a batch size of 16. Meanwhile, for the application of transfer learning VGG16, Resnet50, MobileNetV2, InceptionV3, DensetNet151, and modified VGG16 have respectively each of the highest accuracy including 95.70%, 76.15%, 96.89%, 98.07%, 98.96%, and 99.11%.

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Section: Related Workmentioning

confidence: 90%

Section: Comparison and Before Researchmentioning

confidence: 99%

Classification of clove types using convolution neural network algorithm with optimizing hyperparamters

Tempola,

Wardoyo,

Musdholifah

et al. 2024

Bulletin EEI

View full text Add to dashboard Cite

show abstract

“…As carried out by Prayogi et al [4], classifying cloves using the CNN method, the research results show that accuracy is good but can still be improved. Then, Chalik and Maki [11] carried out clove classification with CNN, but the feature extraction process used clove color features. The resulting accuracy can also still be improved.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving System Accuracy by Modifying the Transfer Learning Architecture for Detecting Clove Maturity Levels

Rosihan,

Tempola,

Nurtanzis Sutoyo

et al. 2024

JAIT

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Detecting the maturity level of cloves is the initial stage in getting quality cloves. Early recognition of the maturity level of cloves is an essential stage in the clove industry. The maturity level of clove flowers can provide valuable information to clove farmers regarding clove harvest time. During the process of determining the level of maturity, it still relies on visual observation. This causes novice farmers and clove workers to still make mistakes in determining the start of the clove harvest. For this reason, in this research, initial detection of the maturity level of cloves was carried out based on images of clove flowers. There are four maturity levels: mature cloves, semimature cloves, overmature cloves, and dry cloves. The proposed research method is a modification of the transfer learning architecture. The research results show that modifying the Transfer learning architecture by adding three layers can increase system accuracy in the VGG16 and ResNet50 models by more than 5%, so that the highest accuracy obtained from modifying the VGG16 model is 95.5% and modifying the ResNet50 model is 87.75%. Meanwhile, for the VGG19 model, accuracy increased only when initializing the number of epochs to 10.

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“…1 is a sample of data from images of North American bird species that have been taken and will be processed as a dataset for use in this paper. The dataset will be divided and split into 80% for training data, 10% for validation data and 10% for testing data [10]. There are 11,788 total images data before pre-processing (training data = 9414, validation data = 1139 and testing data = 1235).…”

Section: Datasetmentioning

confidence: 99%

convolutional neural network bird’s classification using north American bird images

Sitepu¹,

Liu²,

Sigiro³

et al. 2022

ijhs

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In general, image classification is more like to classify objects with large categories, where these objects have a low level of similarity that is relatively rare. Birds image classification is a tough image dataset annotated with many bird species. It may be a challenging issue as numerous of the species of birds have degree of visual closeness. Bird species recognition can be challenging for people, let alone computer vision calculations. To analyze the image, this paper resizes the image into 224224 pixels. We use the Convolutional Neural network (CNN) approach and add the structure of MobileNetV2, EfficientNetB0, EfficientNetB3 and the weight of the network that has previously trained using ImageNet. This paper attempts to analyze the comparative results from using MobileNetV2, EfficientNetB0 and EfficientNetB3 architecture. The F1 weighted average score MobileNetV2 is 75%, EfficientNet B0 is 81% and EfficientNet B3 is 81%. This result shows that some models are completely unable to predict some classes of birds, but other models can recognize them. This can happen because of differences of the operation in each CNN architecture parameter. These models complement each other.

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Classification of Dried Clove Flower Quality using Convolutional Neural Network

Cited by 7 publications

References 16 publications

Classification of clove types using convolution neural network algorithm with optimizing hyperparamters

Classification of clove types using convolution neural network algorithm with optimizing hyperparamters

Improving System Accuracy by Modifying the Transfer Learning Architecture for Detecting Clove Maturity Levels

convolutional neural network bird’s classification using north American bird images

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