Detection of Chicken Diseases from Fecal Images with the Pre-Trained Places365-GoogLeNet Model

Cinar, Ilkay

doi:10.1109/idaacs58523.2023.10348829

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…The conventional methods of disease identification are timeconsuming and prone to errors, leading to the development of innovative techniques based on deep learning. These techniques include the use of deep convolutional neural networks (CNNs) for disease identification and classification, which have achieved impressive accuracy rates [12][13][14]. Another study proposes an ensemble CNN method that utilizes multiple CNN models to enhance plant leaf recognition performance, achieving high accuracies when employing the Xception model along with data augmentation techniques [15,16].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Classification of Mulberry Leaf Diseases

YASIN,

KURSUN,

KOKLU

2024

icisna

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

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