Classification of bread wheat varieties with a combination of deep learning approach

Yasar, Ali; Golcuk, Adem; Sari, Omer Faruk

doi:10.1007/s00217-023-04375-x

Cited by 8 publications

(1 citation statement)

References 51 publications

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“…Additionally, plant disease recognition systems optimized for smartphones have been developed, showcasing their effectiveness in identifying leaf diseases when incorporating offline training and data augmentation methods. Overall, these studies highlight the potential of deep learning techniques, such as CNNs [17][18][19], in transforming plant disease identification and classification and providing accurate and efficient solutions for mitigating the impact of plant diseases on agriculture and human livelihoods [20,21].…”

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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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Classification of Mulberry Leaf Diseases

YASIN,

KURSUN,

KOKLU

2024

icisna

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Convolutional neural network-support vector machine-based approach for identification of wheat hybrids

Sonmez,

Sabanci,

Aydin

2024

Eur Food Res Technol

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Selecting wheat hybrids is vital for enhancing crop yield, adapting to changing climates, and ensuring food security. These hybrids align with market demands and sustainable farming practices, contributing to efficient crop management. Traditional methods for wheat hybrid selection, such as molecular techniques, are costly and time-consuming, and are prone to human error. However, advancements in artificial intelligence and machine learning offer non-destructive, objective, and more efficient solutions. This study is explored the classification of wheat varieties and hybrids using two deep learning models, MobileNetv2 and GoogleNet. These models are achieved impressive classification accuracy, with MobileNetv2 reaching 99.26% and GoogleNet achieving 97.41%. In the second scenario, the deep features obtained from these models are classified with Support Vector Machine (SVM). In the classification made with the MobileNetv2-SVM hybrid model, an accuracy of 99.91% is achieved. This study is provided rapid and accurate wheat variety and hybrid identification method, as well as contributing to breeding programs and crop management.

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