Image Classification for Egg Incubator using Transfer Learning of VGG16 and VGG19

Junaidi, Apri; Lasama, Jerry; Adhinata, Faisal Dharma; Iskandar, Ade Rahmat

doi:10.1109/comnetsat53002.2021.9530826

Cited by 14 publications

(7 citation statements)

References 15 publications

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“…To simplify the procedure for identifying and evaluating image fragments, using the Threshold function, its threshold binarization is performed, i.e. splitting the image into two regions, one of which contains all image elements with a value below a certain threshold, and the other contains all image elements with a value above this threshold [17]. The Threshold function from an input image p generates a binary output image q, with the transition level given by the threshold value t, this function is determined by How:…”

Section: Resultsmentioning

confidence: 99%

“…The use of monitoring chick embryos in incubators has become an invaluable tool in modern poultry farming and research [16]. This state-of-the-art technique allows for precise control and observation throughout the incubation process, ensuring optimal conditions for embryo development [17], [18]. Advanced monitoring systems utilize high-resolution cameras to capture real-time images of the developing embryos, enabling accurate tracking of growth and behavior patterns.…”

Section: Introductionmentioning

confidence: 99%

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The Use of a Vision System for Monitoring Chick Embryos in Incubator

Al-Ansi,

Ryabtsev,

Utkina

2023

Spekta

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Background: Determine the quality of hatching eggs and control the process of their embryonic development, it is necessary to perform candling of eggs, which makes it possible to identify and eliminate the causes of low hatching of chicks. Contribution: The proposed vision system for processing images of chicken eggs makes it possible to distinguish fertilized eggs from unfertilized ones, which makes it possible to change the parameters of the incubator in the event of a large number of dead embryos. Method: With the help of the National Instruments Vision Assistant module, which simplifies the collection, reproduction and storage of images recorded using a video camera connected via a FireWire interface, an incubator vision system has been developed. Results: The yield of healthy chicks is increased due to the periodic automatic candling of eggs and the timely regulation of parameters during the incubation process. Conclusion: Being able to monitor embryo development more closely allows you to determine the optimal time to change incubation parameters, such as humidity, to create the best conditions for egg hatching, leading to more efficient chick production. Similarly, the ability to use different light and temperature conditions to influence chick hatch time can allow for a faster incubation process and hatch healthy chicks in a shorter period of time.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Use of a Vision System for Monitoring Chick Embryos in Incubator

Al-Ansi,

Ryabtsev,

Utkina

2023

Spekta

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“…In order to verify the superiority of the proposed aECA-ResNet34 model, this paper will compare the aECA-ResNet34 model with other neural networks under the same parameter settings using multiple evaluation indicators, including precision (Accuracy), average precision (AP), average recall (Rec), average F1 score (F1), and a confusion matrix [40]. The models involved in the evaluation include ResNet34, SE-ResNet34, ECA-ResNet34, VGG19, ShuffleNet V2, and DenseNet121 [41][42][43]. The formulas for Acc, AP, Rec, and F1 are shown in Equations ( 11)- (14).…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

An Effective Image Classification Method for Plant Diseases with Improved Channel Attention Mechanism aECAnet Based on Deep Learning

Yang,

Yuan,

Zhang

et al. 2024

Symmetry

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Since plant diseases occurring during the growth process are a significant factor leading to the decline in both yield and quality, the classification and detection of plant leaf diseases, followed by timely prevention and control measures, are crucial for safeguarding plant productivity and quality. As the traditional convolutional neural network structure cannot effectively recognize similar plant leaf diseases, in order to more accurately identify the diseases on plant leaves, this paper proposes an effective plant disease image recognition method aECA-ResNet34. This method is based on ResNet34, and in the first and the last layers of this network, respectively, we add this paper’s improved aECAnet with the symmetric structure. aECA-ResNet34 is compared with different plant disease classification models on the peanut dataset constructed in this paper and the open-source PlantVillage dataset. The experimental results show that the aECA-ResNet34 model proposed in this paper has higher accuracy, better performance, and better robustness. The results show that the aECA-ResNet34 model proposed in this paper is able to recognize diseases of multiple plant leaves very accurately.

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“…stacking technique have high ability in training. Different issue that relate medical fields solved by deep learning included many algorithms that have several structure and different properties (Saraiva et al, 2019;Bakht et al, 2021;Gupta et al, 2022;Hameed et al, 2020;Hamida et al, 2021;Junaidi et al, 2021;Kandel et al, 2021;Khan et al, 2020;Khan et al, 2021;Kundu et al, 2021;Mohan et al, 2022;Rahman et al, 2022;Vigier et al, 2021;Xiong et al, 2021;Yadav et al, 2021). Additionally, for effective surgical planning, identifying and forecasting brain disease, a precise categorization permitting a batter differentiation of sick tissues and normal tissues of the MRI image has become crucial (Gupta et al, 2022).…”

Section: Brain Tumor Classification Based On Improved Stacked Ensembl...mentioning

confidence: 99%

Brain Tumor Classification based on Improved Stacked Ensemble Deep Learning Methods

Al-Azzwi,

Nazarov

2023

Asian Pac J Cancer Prev

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Objective: Brain Tumor diagnostic prediction is essential for assisting radiologists and other healthcare professionals in identifying and classifying brain tumors. For the diagnosis and treatment of cancer diseases, prediction and classification accuracy are crucial. The aim of this study was to improve ensemble deep learning models for classifing brain tumor and increase the performance of structure models by combining different model of deep learning to develop a model with more accurate predictions than the individual models. Methods: Convolutional neural networks (CNNs), which are made up of a single algorithm called CNN model, are the foundation of most current methods for classifying cancer illness images. The model CNN is combined with other models to create other methods of classification called ensemble method. However, compared to a single machine learning algorithm, ensemble machine learning models are more accurate. This study used stacked ensemble deep learning technology. The data set used in this study was obtained from Kaggle and included two categories: abnormal & normal brains. The data set was trained with three models: VGG19, Inception v3, and Resnet 10. Result: The 96.6% accuracy for binary classification (0,1) have been achieved by stacked ensemble deep learning model with Loss binary cross entropy, and Adam optimizer take into consideration with stacking models. Conclusion: The stacked ensemble deep learning model can be improved over a single framework.

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Image Classification for Egg Incubator using Transfer Learning of VGG16 and VGG19

Cited by 14 publications

References 15 publications

The Use of a Vision System for Monitoring Chick Embryos in Incubator

The Use of a Vision System for Monitoring Chick Embryos in Incubator

An Effective Image Classification Method for Plant Diseases with Improved Channel Attention Mechanism aECAnet Based on Deep Learning

Brain Tumor Classification based on Improved Stacked Ensemble Deep Learning Methods

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