Epoch Optimization on Rice Leaf Image Classification Using Convolutional Neural Network (CNN) MobileNet

Masykur, Fauzan; Setyawan, Mohammad Bhanu; Winangun, Kuntang

doi:10.24114/cess.v7i2.37336

Cited by 6 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Similarly, in a study of tomato seed variety classification, the MobileNet model achieved the highest classification accuracy [8]. In a study of rice plant leaf classification, the MobileNet model, with 150 epochs, resulted in the highest accuracy [9]. These findings highlight the effectiveness of the MobileNet model in various image classification tasks.…”

Section: Introductionmentioning

confidence: 75%

Deep Machine Learning of MobileNet, Efficient, and Inception Models

Rybczak,

Kozakiewicz

2024

Algorithms

View full text Add to dashboard Cite

Today, specific convolution neural network (CNN) models assigned to specific tasks are often used. In this article, the authors explored three models: MobileNet, EfficientNetB0, and InceptionV3 combined. The authors were interested in investigating how quickly an artificial intelligence model can be taught with limited computer resources. Three types of training bases were investigated, starting with a simple base verifying five colours, then recognizing two different orthogonal elements, followed by more complex images from different families. This research aimed to demonstrate the capabilities of the models based on training base parameters such as the number of images and epoch types. Architectures proposed by the authors in these cases were chosen based on simulation studies conducted on a virtual machine with limited hardware parameters. The proposals present the advantages and disadvantages of the different models based on the TensorFlow and Keras libraries in the Jupiter environment based on the Python programming language. An artificial intelligence model with a combination of MobileNet, proposed by Siemens, and Efficient and Inception, selected by the authors, allows for further work to be conducted on image classification, but with limited computer resources for industrial implementation on a programmable logical controller (PLC). The study showed a 90% success rate, with a learning time of 180 s.

show abstract

Section: Introductionmentioning

confidence: 75%

Deep Machine Learning of MobileNet, Efficient, and Inception Models

Rybczak,

Kozakiewicz

2024

Algorithms

View full text Add to dashboard Cite

show abstract

“…Similarly, in a study of tomato seed variety classification, the MobileNet model achieved the highest classification accuracy [9]. In a study of rice plant leaf classification, the Mobilenet model with 150 epochs resulted in the highest accuracy [10]. These findings highlight the effectiveness of the Mobilenet model in various image classification tasks.…”

Section: Introductionmentioning

confidence: 82%

“…The file is then saved to an SMC card on the computer where the model was taught. This card is transferred to the NPU module and, following the steps suggested by the manufacturer, the S7-1500 family PLC reads the parameters from the card [see Figures 9,10…”

Section: Verification Model Inception On Plcmentioning

confidence: 99%

Deep machine learning of the MobileNet, Efficient and Inception models

Rybczak,

Kozakiewicz

2023

Preprint

View full text Add to dashboard Cite

Today, specific CNN models assigned to specific tasks are often used. In this article, the authors have investigated three models MobileNet, EfficientNetB0 and InceptionV3. Artificial intelligence methods for deep networks will be presented but implemented in a limited computer resource. Three types of training bases were investigated, starting with a simple base verifying five colors, followed by recognition of two different orthogonal elements, and then more complex images discriminating between elements. The research aimed to demonstrate the models' capabilities based on training base parameters such as the number of images and epoch types. The architectures proposed by the authors in these cases were chosen based on simulation studies conducted on a virtual machine with limited hardware parameters. The proposals present the advantages and disadvantages of different models based on TensorFlow, Keras libraries in the Jupiter environment written based on the Python language. The authors' proposal of the selected AI model enables further work on e.g. image classification, but in a limited computer resource for industrial implementation e.g. on a PLC.

show abstract

“…An experiment on the best epoch was carried out by Fauzan in 2022 which stated that the higher the epoch value, the better the accuracy and the smaller the loss. Research on the epoch was carried out with variations of 50, 100 and 150 and the results showed that epoch 150 got the best score (Defriani & Jaelani, 2022;Masykur et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Bot Telegram as Broadcasting Media Classification Results of Convolutional Neural Network (CNN) Images of Rice Plant Leaves

Fajaryanto

Masykur

Rosyadi

2023

CNAHPC

View full text Add to dashboard Cite

Rice plants play an important role in the life of the Indonesian people because rice is the raw material for rice as a staple food. The rice production process does not rule out the possibility of interference by pests and diseases resulting in losses that cause crop failure. Meanwhile, pests on rice plants can be caused by various types, namely types of fungi (leafblast, hispa, brownspot) and types of nuisance animals. In this research, it will be carried out how to classify the image of rice plant leaves using the deep learning Convolutional Neural Network (CNN) algorithm, then the results of the classification are sent to users by utilizing the telegram chat application. The rice plant leaf image dataset is grouped into 4 groups (leafblast, brownspot, hispa and healthy). From several experiments it can be seen the results of system performance, namely the classification speed takes 30-60 seconds.

show abstract

Epoch Optimization on Rice Leaf Image Classification Using Convolutional Neural Network (CNN) MobileNet

Cited by 6 publications

References 0 publications

Deep Machine Learning of MobileNet, Efficient, and Inception Models

Deep Machine Learning of MobileNet, Efficient, and Inception Models

Deep machine learning of the MobileNet, Efficient and Inception models

Implementation of Bot Telegram as Broadcasting Media Classification Results of Convolutional Neural Network (CNN) Images of Rice Plant Leaves

Contact Info

Product

Resources

About