Renk Momentleri ve Destek Vektör Makineleri Kullanarak Haploid Mısır Tohumlarının Tanımlanmasında Renk Uzaylarının Sınıflandırma Performansına Etkisinin Karşılaştırılması

Altuntaş, Yahya; Kocamaz, Adnan Fatih

doi:10.35234/fumbd.585312

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…In this study, SVM proposed by Vapnik [27] was used as a classifier. It has been reported that the SVM classifier shows superior performance in different agricultural image classification problems [28].…”

Section: Support Vector Machinesmentioning

confidence: 99%

Deep Feature Extraction for Detection of Tomato Plant Diseases and Pests based on Leaf Images

Altuntaş¹,

Kocamaz

2021

Celal Bayar Üniversitesi Fen Bilimleri Dergisi

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Plant diseases and pests cause yield and quality losses. It has great importance to detect plant diseases and pests quickly and with high accuracy in terms of preventing yield and quality losses. Plant disease and pest detection performed by plant protection experts through visual observation is a labor-intensive process with a high error rate. Developing effective, fast and highly successful computer-aided disease detection systems has become a necessity in terms of precision agriculture applications. In this study, well-known pre-trained convolutional neural network (CNN) models AlexNet, GoogLeNet and ResNet-50 are used as feature extractors. In addition, a deep learning model that concatenate deep features extracted from 3 CNN models has been proposed. The deep features were used to train the support vector machine classifier. The proposed model was used to classify leaf images of tomato plant diseases and pests, which is a subset of open-access PlantVillage dataset consisting of a total of 18835 images belonging to 10 classes including a healthy one. Accuracy, precision, sensitivity and f-score performance metrics were used with the hold-out validation method in determining model performances. Experimental results show that the detection of tomato plant diseases and pests is possible using concatenated deep features with an overall accuracy rate of 96.99%.

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Section: Support Vector Machinesmentioning

confidence: 99%

Deep Feature Extraction for Detection of Tomato Plant Diseases and Pests based on Leaf Images

Altuntaş¹,

Kocamaz

2021

Celal Bayar Üniversitesi Fen Bilimleri Dergisi

Self Cite

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“…To achieve this goal, CNN have been used to automatically recognize haploid and diploid maize seeds with a transfer learning approach. Altuntaş and Kocamaz [18] have proposed a computer-based method for the identification of haploid maize seeds. Maize seed embryos have been segmented by the k-means clustering method.…”

Section: Related Studiesmentioning

confidence: 99%

Classification of Haploid and Diploid Maize Seeds based on Pre-Trained Convolutional Neural Networks

Dönmez

2020

Celal Bayar Üniversitesi Fen Bilimleri Dergisi

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Analysis of agricultural products is an important area that is widely emphasized today. In this context, with the development of technology, computer-aided analysis systems are also being developed. In this study, a system has been proposed for classifying maize seeds as haploid and diploid using pre-trained convolutional neural networks. For this purpose, AlexNet, GoogLeNet, ResNet-18, ResNet-50, and VGG-16 pre-trained models have been used as feature extractors for the haploid and diploid seed classification process. In the first stage, the deep features of haploid and diploid maize seeds have been obtained in these models. The features have been taken from different layers of network architecture. Instead of softmax classifier in the last layer of the network, classifiers based on decision tree, k-nearest neighbor, and support vector machine have been used. According to the classification results with these features, the achievements in network architectures and classifier methods have been observed. The experiments have been carried out on a publicly available dataset consisting of 3000 haploid and diploid maize seed images. The experimental results revealed that the developed classification systems demonstrate a remarkable performance.

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Identification of haploid and diploid maize seeds using hybrid transformer model

Dönmez,

Kılıçarslan,

Közkurt

et al. 2023

Multimedia Systems

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Renk Momentleri ve Destek Vektör Makineleri Kullanarak Haploid Mısır Tohumlarının Tanımlanmasında Renk Uzaylarının Sınıflandırma Performansına Etkisinin Karşılaştırılması

Cited by 8 publications

References 21 publications

Deep Feature Extraction for Detection of Tomato Plant Diseases and Pests based on Leaf Images

Deep Feature Extraction for Detection of Tomato Plant Diseases and Pests based on Leaf Images

Classification of Haploid and Diploid Maize Seeds based on Pre-Trained Convolutional Neural Networks

Identification of haploid and diploid maize seeds using hybrid transformer model

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