Feature fusion based artificial neural network model for disease detection of bean leaves
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Dosyalar
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Amer Inst Mathematical Sciences-Aims
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Plant diseases reduce yield and quality in agricultural production by 20-40%. Leaf diseases cause 42% of agricultural production losses. Image processing techniques based on artificial neural networks are used for the non-destructive detection of leaf diseases on the plant. Since leaf diseases have a complex structure, it is necessary to increase the accuracy and generalizability of the developed machine learning models. In this study, an artificial neural network model for bean leaf disease detection was developed by fusing descriptive vectors obtained from bean leaves with HOG (Histogram Oriented Gradient) feature extraction and transfer learning feature extraction methods. The model using feature fusion has higher accuracy than only HOG feature extraction and only transfer learning feature extraction models. Also, the feature fusion model converged to the solution faster. Feature fusion model had 98.33, 98.40 and 99.24% accuracy in training, validation, and test datasets, respectively. The study shows that the proposed method can effectively capture interclass distinguishing features faster and more accurately.
Açıklama
Anahtar Kelimeler
leaf disease, artificial neural network, feature fusion, deep neural networks, computer vision
Kaynak
Electronic Research Archive
WoS Q Değeri
Q1
Scopus Q Değeri
Q3
Cilt
31
Sayı
5
