Machine Learning-Based Classification of Mulberry Leaf Diseases DOI Open Access
Elham Tahsin Yasin, Ramazan Kursun, Murat Köklü

и другие.

Proceedings of international conference on intelligent systems and new applications., Год журнала: 2024, Номер unknown

Опубликована: Апрель 28, 2024

This research examines the potential of machine learning methods in classification Mulberry leaf diseases. By applying SqueezeNet's deep feature extraction, study aimed to identify disease patterns efficiently. The dataset used consisted ten distinct classes diseases, which was divided into an 80% training set and a 20% testing set. Support Vector Machine (SVM) supervised algorithm classify model achieved accuracy 77.5%. results demonstrate effectiveness approaches aiding detection management can contribute advancements agricultural monitoring mitigation strategies.

Язык: Английский

Fisheye freshness detection using common deep learning algorithms and machine learning methods with a developed mobile application DOI Creative Commons
Müslüme Beyza Yıldız, Elham Tahsin Yasin, Murat Köklü

и другие.

European Food Research and Technology, Год журнала: 2024, Номер 250(7), С. 1919 - 1932

Опубликована: Апрель 18, 2024

Abstract Fish is commonly ingested as a source of protein and essential nutrients for humans. To fully benefit from the proteins substances in fish it crucial to ensure its freshness. If stored an extended period, freshness deteriorates. Determining can be done by examining eyes, smell, skin, gills. In this study, artificial intelligence techniques are employed assess The author’s objective evaluate analyzing eye characteristics. achieve this, we have developed combination deep machine learning models that accurately classify fish. Furthermore, application utilizes both learning, instantly detect any given sample was created. Two algorithms (SqueezeNet, VGG19) were implemented extract features image data. Additionally, five levels samples applied. Machine include (k-NN, RF, SVM, LR, ANN). Based on results, inferred employing VGG19 model feature selection conjunction with Artificial Neural Network (ANN) classification yields most favorable success rate 77.3% FFE dataset. Graphical

Язык: Английский

Процитировано

7
Classification of Sugarcane Leaf Disease with AlexNet Model DOI Open Access
Ramazan Kursun, Elham Tahsin Yasin, Murat Köklü

и другие.

Proceedings of international conference on intelligent systems and new applications., Год журнала: 2024, Номер unknown

Опубликована: Апрель 28, 2024

This study evaluates the influence of activation functions on performance AlexNet deep learning model in classifying sugarcane diseases. Two popular functions, ReLU and LeakyReLU, were compared terms classification accuracy computational efficiency. The function, known for its simplicity speed, achieved an 87.90% with a total training testing time 47 minutes. In contrast, which allows small gradient when input is negative hence provides continuity process, obtained higher 90.67%, albeit at cost, taking 54 minutes phase. These results highlight trade-off between deployment models agricultural applications. suggests that while LeakyReLU can lead to more accurate models, remains competitive choice efficiency paramount. Future research should focus optimizing balance potentially through tuning parameters or development hybrid models.

Язык: Английский

Процитировано

1
Hybridizing Long Short-Term Memory and Bi-Directional Long Short-Term Memory Models for Efficient Classification: A Study on Xanthomonas axonopodis pv. phaseoli (XaP) in Two Bean Varieties DOI Creative Commons
Ramazan Kursun, A. Gur, Kubilay Kurtuluş Bastas

и другие.

Agronomy, Год журнала: 2024, Номер 14(7), С. 1495 - 1495

Опубликована: Июль 10, 2024

This study was conducted on Xanthomonas axonopodis pv, which causes significant economic losses in the agricultural sector. Here, we a common bacterial blight disease caused by phaseoli (XaP) pathogen Üstün42 and Akbulut bean genera. In this study, total of 4000 images, healthy diseased, were used for both breeds. These images classified AlexNet, VGG16, VGG19 models. Later, reclassification performed applying pre-processing to raw images. According results obtained, accuracy rates pre-processed VGG19, VGG16 AlexNet models determined as 0.9213, 0.9125 0.8950, respectively. The then hybridized with LSTM BiLSTM new created. When performance these hybrid evaluated, it found that more successful than simple models, while gave better LSTM. particular, VGG19+BiLSTM model attracted attention achieving 94.25% classification emphasizes effectiveness image processing techniques agriculture field detection is important dataset literature evaluating

Язык: Английский

Процитировано

0
Machine Learning-Based Classification of Mulberry Leaf Diseases DOI Open Access
Elham Tahsin Yasin, Ramazan Kursun, Murat Köklü

и другие.

Proceedings of international conference on intelligent systems and new applications., Год журнала: 2024, Номер unknown

Опубликована: Апрель 28, 2024

This research examines the potential of machine learning methods in classification Mulberry leaf diseases. By applying SqueezeNet's deep feature extraction, study aimed to identify disease patterns efficiently. The dataset used consisted ten distinct classes diseases, which was divided into an 80% training set and a 20% testing set. Support Vector Machine (SVM) supervised algorithm classify model achieved accuracy 77.5%. results demonstrate effectiveness approaches aiding detection management can contribute advancements agricultural monitoring mitigation strategies.

Язык: Английский

Процитировано

0